Awesome Code LLM
1.0.0
Это репо для нашего опроса TMLR, объединяющего перспективы НЛП и разработки программного обеспечения: опрос по языковым моделям для кода - всесторонний обзор исследований LLM для кода. Работы в каждой категории упорядочены хронологически. Если у вас есть базовое понимание машинного обучения, но новичок в NLP, мы также предоставляем список рекомендуемых показаний в разделе 9.
[2024/11/28] Показанные статьи:
Оптимизация предпочтений для рассуждений с псевдо обратной связью из Технологического университета Наняна.
ScribeAgent: к специализированным веб-агентам с использованием данных рабочего процесса в масштабе производства от Scribe.
Программирование, управляемое планированием: большой рабочий процесс программирования с большим языком из Мельбурнского университета.
Цель перевода кода на уровне репозитория нацелен на ржавчину из Университета Sun Yat-Sen.
Использование предыдущего опыта: расширяемая вспомогательная база знаний для текста до SQL от Университета науки и техники Китая.
Codexembed: универсальное семейство модели встраиваемого для многолигенового и многозадачного кода поиска из исследований Salesforce AI.
PROSEC: Укрепляющий код LLM с упреждающим выравниванием безопасности в Университете Пердью.
[2024/10/22] Мы собрали 70 документов с сентября и октября 2024 года в одной статье WeChat.
[2024/09/06] Наш опрос был принят для публикации с помощью транзакций по исследованиям машинного обучения (TMLR).
[2024/09/14] Мы собрали 57 документов с августа 2024 года (в том числе 48, представленных в ACL 2024) в одной статье WeChat.
Если вы обнаружите, что документ отсутствует в этом репозитории, неуместно в категории или не имея ссылки на информацию в журнале/конференции, пожалуйста, не стесняйтесь создавать проблему. Если вы обнаружите, что это репо является полезным, пожалуйста, процитируйте наш опрос:
@article{zhang2024unifying,
title={Unifying the Perspectives of {NLP} and Software Engineering: A Survey on Language Models for Code},
author={Ziyin Zhang and Chaoyu Chen and Bingchang Liu and Cong Liao and Zi Gong and Hang Yu and Jianguo Li and Rui Wang},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=hkNnGqZnpa},
note={}
}
Опросы
Модели
2.1 Base LLM и стратегии предварительной подготовки
2.2 Существующий LLM адаптирован к коду
2.3 Общая предварительная подготовка кода
2.4 (инструкция) тонкая настройка кода
2.5 Подкрепление обучения на коде
Когда кодирование встречается
3.1 Кодирование для рассуждения
3.2 Моделирование кода
3.3 Кодовые агенты
3.4 Интерактивное кодирование
3.5 Фронтальная навигация
Код LLM для языков с низким ресурсом, низким уровнем и доменом
Методы/модели для нижестоящих задач
Программирование
Тестирование и развертывание
DevOps
Требование
Анализ кода, сгенерированного AI,
Человеческое взаимодействие
Наборы данных
8.1 Предварительная подготовка
8.2 тесты
Рекомендуемые показания
Цитирование
Звездная история
Присоединяйтесь к нам
Мы перечисляем несколько недавних опросов по аналогичным темам. Хотя они все о языковых моделях для кода, 1-2 фокусируются на стороне NLP; 3-6 Фокус на стороне SE; 7-11 выпускаются после нашего.
«Модели крупных языков встречаются с NL2Code: опрос» [2022-12] [ACL 2023] [Paper]
«Опрос о предварительно проведенных языковых моделях для интеллекта нейронного кода» [2022-12] [Paper]
«Эмпирическое сравнение предварительно обученных моделей исходного кода» [2023-02] [ICSE 2023] [Paper]
«Большие языковые модели для разработки программного обеспечения: систематический обзор литературы» [2023-08] [Paper]
«На пути к пониманию больших языковых моделей в задачах разработки программного обеспечения» [2023-08] [Paper]
«Подводные камни в языковых моделях для интеллекта кода: таксономия и опрос» [2023-10] [Paper]
«Опрос о крупных языковых моделях для разработки программного обеспечения» [2023-12] [Paper]
«Глубокое обучение для интеллекта кода: опрос, эталонный и инструментарий» [2023-12] [Paper]
«Обследование интеллекта нейронного кодекса: парадигмы, достижения и за его пределами» [2024-03] [Paper]
«Задачи, которые люди приглашают: таксономия LLM-нижних задач в подходах к проверке и фальсификации программного обеспечения» [2024-04] [Paper]
«Автоматическое программирование: модели больших языков и за пределами» [2024-05] [Paper]
«Программное обеспечение и модели фундамента: понимание отраслевых блогов с использованием жюри моделей фундамента» [2024-10] [Paper]
«Глубокое обучение разработке программного обеспечения: прогресс, проблемы и возможности» [2024-10] [Paper]
Эти LLMS не специально обучены для кода, но продемонстрировали различную возможность кодирования.
Ламда : «Ламда: Языковые модели для диалоговых приложений» [2022-01] [Paper]
Palm : «Палм: моделирование языка масштабирования с помощью путей» [2022-04] [JMLR] [Paper]
GPT-neox : «GPT-NEOX-20B: модель авторегрессии с открытым исходным кодом» [2022-04]
Bloom : «Bloom: модель многоязычного языка с открытым доступом 176B-параметра» [2022-11] [Paper] [Модель]
Лама : «Лама: открытые и эффективные языковые модели фундамента» [2023-02] [Paper]
GPT-4 : «Технический отчет GPT-4» [2023-03] [Paper]
Llama 2 : «Llama 2: Open Foundation и тонкие модели чата» [2023-07] [Paper] [Repo]
PHI-1.5 : «Учебники-это все, что вам нужно II: PHI-1.5 Технический отчет» [2023-09] [Paper] [Модель]
Baichuan 2 : «Baichuan 2: открытые крупномасштабные языковые модели» [2023-09] [Paper] [Repo]
QWEN : «Технический отчет QWEN» [2023-09] [Paper] [Repo]
Мишстраль : «Мисстраль 7b» [2023-10] [Paper] [Repo]
Близнецы : «Близнецы: семейство высокоэффективных мультимодальных моделей» [2023-12] [Paper]
Phi-2 : «Phi-2: удивительная сила малых языковых моделей» [2023-12] [блог]
Yayi2 : «Yayi 2: многоязычные модели с открытым исходным кодом» [2023-12] [Paper] [Repo]
DeepSeek : «Deepseek LLM: масштабирование языковых моделей с открытым исходным кодом с долгомрождением» [2024-01] [Paper] [Repo]
Mixtral : «Миктрал экспертов» [2024-01] [Paper] [Блог]
DeepSeekmoe : «Deepseekmoe: к окончательной экспертной специализации в смеси языковых моделей» [2024-01] [Paper] [Repo]
Orion : «Orion-14b: многоязычные большие языковые модели с открытым исходным кодом» [2024-01] [Paper] [Repo]
OLMO : «Olmo: ускорение науки о языковых моделях» [2024-02] [Paper] [Repo]
Джемма : «Джемма: открытые модели на основе исследований и технологий Близнецов» [2024-02] [Paper] [Блог]
Claude 3 : «Модельная семейство Claude 3: Opus, Sonnet, Haiku» [2024-03] [Paper] [Блог]
YI : "Yi: Open Foundation Models By 01.ai" [2024-03] [Paper] [Repo]
Поро : «Поро 34b и благословение многоязычной» [2024-04] [Paper] [Модель]
Jetmoe : «Jetmoe: достижение производительности Llama2 с 0,1 млн долларов» [2024-04] [Paper] [Repo]
Llama 3 : «стадо ламы 3 моделей» [2024-04] [блог] [Repo] [Paper]
Reka Core : «Reka Core, Flash и Edge: серия мощных мультимодальных языковых моделей» [2024-04] [Paper]
PHI-3 : «Технический отчет PHI-3: высоко способная языковая модель локально на вашем телефоне» [2024-04] [Paper]
Openelm : «Openelm: эффективная семейство моделей языка с рамками обучения и вывода с открытым исходным кодом» [2024-04] [Paper] [Repo]
Tele-Flm : «Технический отчет Tele-FLM» [2024-04] [Paper] [Модель]
DeepSeek-V2 : «DeepSeek-V2: сильная, экономичная и эффективная модель языковой смеси экспертов» [2024-05] [Paper] [Repo]
Гекко : «Гекко: модель генеративного языка для английского, кода и корейского» [2024-05] [Paper] [Модель]
MAP-neo : «MAP-neo: очень способный и прозрачный двуязычный серия моделей большого языка» [2024-05] [Paper] [Repo]
Skywork-Moe : «Skywork-Moe: глубокое погружение в методы обучения для языковых моделей смеси» [2024-06] [Paper]
Xmodel-LM : «Технический отчет Xmodel-LM» [2024-06] [Paper]
GEB : «GEB-1.3B: открытая легкая модель большой языка» [2024-06] [Paper]
Заяц : «Заяц: Priors, ключ к эффективности модели малого языка» [2024-06] [Paper]
DCLM : «DataComp-LM: в поисках следующего поколения обучающих наборов для языковых моделей» [2024-06] [Paper]
Nemotron-4 : «Nemotron-4 340b технический отчет» [2024-06] [Paper]
Чатглм : «Чатглм: семейство больших языковых моделей от GLM-130B до GLM-4 Все инструменты» [2024-06] [Paper]
ЮЛАН : «Юлан: большая языковая модель с открытым исходным кодом» [2024-06] [Paper]
Gemma 2 : «Gemma 2: улучшение моделей открытого языка при практическом размере» [2024-06] [Paper]
H2O-Danube3 : «Технический отчет H2O-Danube3» [2024-07] [Paper]
QWEN2 : «Технический отчет QWEN2» [2024-07] [Paper]
Аллам : «Аллам: Большие языковые модели для арабского и английского языка» [2024-07] [Paper]
SEALLMS 3 : «SEALLMS 3: Open Foundation и чат многоязычных больших языковых моделей для языков Юго-Восточной Азии» [2024-07] [Paper]
AFM : «Языковые модели Apple Intelligence Foundation» [2024-07] [Paper]
«Кодировать или не кодировать? Изучение влияния кода на предварительном обучении» [2024-08] [Paper]
Olmoe : «Olmoe: Open Mix-Of Experts Language Models» [2024-09] [Paper]
«Как предварительная подготовка кода влияет на выполнение задачи языковой модели?» [2024-09] [бумага]
Eurollm : «Eurollm: многоязычные языковые модели для Европы» [2024-09] [Paper]
«Какой язык программирования и какие функции на стадии предварительного обучения влияют на производительность логического вывода вниз по течению?» [2024-10] [бумага]
GPT-4O : «Системная карта GPT-4O» [2024-10] [Paper]
Hunyuan-Large : «Hunyuan-Large: модель MOE с открытым исходным кодом с 52 миллиардами активированных параметров Tencent» [2024-11] [Paper]
Кристалл : «Кристалл: освещающие способности LLM на языке и коде» [2024-11] [Paper]
Xmodel-1.5 : «Xmodel-1.5: многоязычный LLM в масштабе 1B» [2024-11] [Paper]
Эти модели представляют собой LLMS общего назначения, которые далее предварительно предварительно предоставлены данными, связанными с кодом.
Кодекс (GPT-3): «Оценка моделей крупных языков, обученных коду» [2021-07] [Paper]
Palm Coder (Palm): «Палм: моделирование языка масштабирования с путями» [2022-04] [JMLR] [Paper]
Minerva (Palm): «Решение количественных проблем рассуждений с языковыми моделями» [2022-06] [Paper]
Пальма 2 * (пальма 2): «Палм 2 Технический отчет» [2023-05] [Paper]
Код Llama (Llama 2): «Code Llama: Open Foundation Models для кода» [2023-08] [Paper] [Repo]
Лемур (лама 2): «Лемур: гармонизирующий естественный язык и код для языковых агентов» [2023-10] [ICLR 2024 Spotlight] [Paper]
BTX (Llama 2): «Mranch-Train-Mix: Mixing Expert LLMS в смесь Experts LLM» [2024-03] [Paper]
Хироп : «Хироп: экстраполяция длины для моделей кода с использованием иерархической позиции» [2024-03] [ACL 2024] [Paper]
«Освоение текста, кода и математику одновременно путем слияния высокоспециализированных языковых моделей» [2024-03] [Paper]
Codegemma : «CodeGemma: Open Code Models на основе Gemma» [2024-04] [Paper] [Модель]
DeepSeek-Coder-V2 : «DeepSeek-Coder-V2: нарушение барьер моделей с закрытым исходным кодом в интеллекте кода» [2024-06] [Paper]
«Обещание и опасность моделей генерации совместного кода: баланс эффективности и запоминания» [2024-09] [Paper]
QWEN2.5-CODER : «Технический отчет QWEN2,5-CODER» [2024-09] [Paper]
Lingma swe-gpt : «Lingma swe-gpt: открытая ориентированная на языковая модель разработки для автоматизированного улучшения программного обеспечения» [2024-11] [Paper]
Эти модели представляют собой трансформаторные кодеры, декодеры и декодеры энкодеров, предоставленные с нуля с использованием существующих целей для общего языкового моделирования.
Cubert (MLM + NSP): «Изучение и оценка контекстуального встраивания исходного кода» [2019-12] [ICML 2020] [Paper] [Repo]
Codebert (MLM + RTD): «Codebert: предварительно обученная модель для программирования и естественных языков» [2020-02] [EMNLP 2020 выводы] [Paper] [Repo]
GraphCodebert (MLM + DFG Edge Production + выравнивание узлов DFG): «GraphCodebert: представления кода до обучения с помощью потока данных» [2020-09] [ICLR 2021] [Paper] [Repo]
Syncobert (MLM + идентификатор прогнозирование + AST Edge прогнозирование + контрастное обучение): «Syncobert: Многомодальное контрастное предварительное обучение, управляемое синтаксисом, для представления кода» [2021-08] [Paper]
Disco (MLM + тип узла MLM + контрастное обучение): «На пути к обучению (DIS) -SImility исходного кода из программных контрастов» [2021-10] [ACL 2022] [Paper]
Code-MVP (MLM + тип вывод + контрастное обучение): «Code-MVP: обучение для представления исходного кода из нескольких представлений с контрастным предварительным тренировком» [2022-05] [NAACL 2022 Технический трек] [Paper]
Codesage (MLM + Deobfuscation + Contrastive Learning): «Обучение представления кода в масштабе» [2024-02] [ICLR 2024] [Paper]
Колсберт (MLM): «Законы масштабирования модели понимания кода» [2024-02] [Paper]
GPT-C (CLM): «Компостит Intellicode: генерация кода с использованием трансформатора» [2020-05] [ESEC/FSE 2020] [Paper]
CodeGpt (CLM): «CodexGlue: набор данных контрольного управления для понимания и генерации кода» [2021-02] [Наборы и контрольные показатели Neurips 2021] [Paper] [Repo]
CodeParrot (CLM) [2021-12] [блог]
Polycoder (CLM): «Систематическая оценка крупных языковых моделей кода» [2022-02] [DL4C@ICLR 2022] [Paper] [Repo]
CodeGen (CLM): «CodeGen: открытая большая языковая модель для кода с синтезом программы с несколькими разворотами» [2022-03] [ICLR 2023] [Paper] [Repo]
Incoder (причинно-следственная маскировка): «Инкодер: генеративная модель для кода заполнения и синтеза» [2022-04] [ICLR 2023] [Paper] [Repo]
Pycodegpt (CLM): «Cert: постоянное предварительное обучение по эскизам для генерации кода, ориентированной на библиотеку» [2022-06] [IJCAI-Ecai 2022] [Paper] [Repo]
Pangu-Coder (CLM): «Pangu-Coder: Синтез программы с языком функционального уровня» [2022-07] [Paper]
Santacoder (FIM): "Santacoder: не дотягитесь за звездами!" [2023-01] [Paper] [Модель]
Codegeex (CLM): «CodeGeex: предварительно обученная модель для генерации кода с многоязычными оценками на HumaneVal-X» [2023-03] [Paper] [Repo]
StarCoder (FIM): «StarCoder: Да пребудет источник!» [2023-05] [Paper] [Модель]
PHI-1 (CLM): «Учебники-это все, что вам нужно» [2023-06] [Paper] [Модель]
CodeFuse (CLM): «CodeFuse-13B: предварительно предварительно проведенный многоязычный код большой язык» [2023-10] [Paper] [Модель]
DeepSeek Coder (CLM+FIM): «DeepSeek-Coder: Когда модель большого языка соответствует программированию-рост интеллекта кода» [2024-01] [Paper] [Repo]
StarCoder2 (CLM+FIM): «StarCoder 2 и стек V2: следующее поколение» [2024-02] [Paper] [Repo]
CodeShell (CLM+FIM): «Технический отчет CodeShell» [2024-03] [Paper] [Repo]
CodeQwen1.5 [2024-04] [блог]
Гранит : «Модели гранитного кода: семейство моделей открытых фундаментов для интеллекта кода» [2024-05] [Paper] «Масштабирование моделей гранита кода до 128K контекст» [2024-07] [Paper]
NT-Java : «Узкий трансформатор: Java-LM на основе StarCoder для настольного компьютера» [2024-07] [Paper]
Arctic-Snowcoder : «Arctic-Snowcoder: демистификация высококачественных данных в предварительной подготовке кода» [2024-09] [Paper]
AIXCODER : «AIXCODER-7B: легкая и эффективная большая языковая модель для завершения кода» [2024-10] [Paper]
Opencoder : «Opencoder: открытая кулинарная книга для кода высшего уровня на большие языковые модели» [2024-11] [Paper]
PYMT5 (CRORUPTION): «PYMT5: Многомодовая трансляция естественного языка и кода Python с трансформаторами» [2020-10] [EMNLP 2020] [Paper]
Mastropaolo et al. (MLM + Deobfuscation): «DOBF: A Deobfuscation предварительно тренировочный объектив для языков программирования» [2021-02] [ICSE 2021] [Paper] [Repo]
DOBF (SPAN CORRUPTION): «Изучение использования трансформатора передачи текста в текст для поддержки задач, связанных с кодом» [2021-02] [Neurips 2021] [Paper] [Repo]
Plbart (DAE): «Объединенное предварительное обучение для понимания программы и поколения» [2021-03] [NAACL 2021] [Paper] [Repo]
CODET5 (SPAN CORRUPTION + TAGING IDENIFIER + Прогнозирование идентификатора маскированного идентификатора + TEXT2CODE + CODE2TEXT): «CODET5: Унифицированный предварительно обученный предварительно обученные модели энкодера для понимания и генерации кода» [2021-09] [EMNLP 2021] [Paper] [Repo]
SPT-Code (Span Corruption + NSP + Прогнозирование имени метода): «SPT-Code: предварительное обучение последовательности в последовательности для представлений исходного кода обучения» [2022-01] [ICSE 2022 Технический трек] [Paper]
Альфакод (MLM + CLM): «Генерация кода на уровне конкуренции с альфакодом» [2022-02] [Science] [Paper] [Блог]
Natgen (Code Naturalization): «Natgen: генеративная предварительная тренировка путем« натурализации »исходного кода» [2022-06] [ESEC/FSE 2022] [Paper] [Repo]
Ernie-Code (Span Corruption + Pivot Translation LM): «Эрни-код: за пределами английского ориентированного межсового предварительного подготовки для языков программирования» [2022-12] [ACL23 (выводы)] [Paper] [Repo]
CODET5 + (CROMRUPTION + CLM + Текстовый код контрастирующий обучение + трансляция текстового кода): «Codet5 +: Open Code Большой языковые модели для понимания кода и генерации» [2023-05] [EMNLP 2023] [Paper] [Repo]
AST-T5 (SPAN CORRUPTION): «AST-T5: Предварительная подготовка кода и понимание» [2024-01] [ICML 2024] [Paper] [Paper]
CUGLM (MLM + NSP + CLM): «Многозадачная модель обучения на основе обучения для завершения кода» [2020-12] [ASE 2020] [Paper]
UnixCoder (MLM + NSP + CLM + SPAN CORRUPTION + Contrastive Learning + Code2text): «UnixCoder: унифицированное межмодальное предварительное обучение для представления кода» [2022-03] [ACL 2022] [Paper] [Repo]
Эти модели применяют методы тонкой настройки инструкции для повышения возможностей кода LLMS.
WizardCoder (StarCoder + Evol-Instruct): «WizardCoder: расширение возможностей кода большие языковые модели с помощью evol-instruct» [2023-06] [ICLR 2024] [Paper] [Repo]
Pangu-Coder 2 (StarCoder + Evol-Instruct + RRTF): «Pangu-Coder2: Увеличение больших языковых моделей для кода с ранжированным отзывом» [2023-07] [Paper]
Octocoder (StarCoder) / OctoGeex (CodeGeex2): «Octopack: код настройки инструкций большие языковые модели» [2023-08] [ICLR 2024 Spotlight] [Paper] [Repo]
«На этом этап обучения справляется с кодом данных, которые помогают LLMS рассуждать» [2023-09] [ICLR 2024 Spotlight] [Paper]
Инструктатор : «Инструктор: настройка инструкции большие языковые модели для редактирования кода» [Paper] [Repo]
MFTCoder : «MFTCoder: повышение кода LLM с многозадажной тонкой настройкой» [2023-11] [KDD 2024] [Paper] [Repo]
«Очистка кода с помощью LLM для точных генераторов кода обучения» [2023-11] [ICLR 2024] [Paper]
Magicoder : «Magicoder: расширение возможностей кода с помощью OSS-Instruct» [2023-12] [ICML 2024] [Paper]
WaveCoder : «WaveCoder: широко распространенное и универсальное улучшение для кода большие языковые модели путем настройки инструкций» [2023-12] [ACL 2024] [Paper]
Astraios : «Astraios: Параметр-экономичный код настройки инструкций большие языковые модели» [2024-01] [Paper]
Dolphcoder : «Dolphcoder: эхополирующий код большие языковые модели с разнообразными и многообъясняющими настройками инструкций» [2024-02] [ACL 2024] [Paper]
SafeCoder : «Настройка инструкции для получения защищенного кода» [2024-02] [ICML 2024] [Paper]
«Код нуждается в комментариях: улучшение кода LLM с увеличением комментариев» [ACL 2024 выводы] [Paper]
CCT : «Настройка сравнения кода для моделей Code на больших языках» [2024-03] [Paper]
SAT : «Структурно-аналитическая настройка для моделей, предварительно обученных кодом» [2024-04] [Paper]
Codefort : «Codefort: надежное обучение для моделей генерации кодов» [2024-04] [Paper]
Xft : «XFT: разблокировка мощности настройки инструкций кода, просто слияя Upcycled Mix-Of-Experts» [2024-04] [ACL 2024] [Paper] [Repo]
Анайв-инстакция : «Автокондер: улучшение кода с большой языковой моделью с помощью aiev-instruct» [2024-05] [Paper]
Alchemistcoder : «Alchemistcoder: гармонизирующая и выявляя возможность кода путем настройки задним числом на данные с несколькими источниками» [2024-05] [Paper]
«От символических задач до генерации кода: диверсификация дает лучшие исполнители задач» [2024-05] [Paper]
«Раскрытие влияния инструкции по кодированию данных настройки на большие языковые модели» [2024-05] [Paper]
Слива : «Слива: предпочтение обучению плюс тестовые примеры дают лучшие модели языка кода» [2024-06] [Paper]
MCODER : «MCEVAL: массовая многоязычная оценка кода» [2024-06] [Paper]
«Разблокируйте корреляцию между контролируемой точной настройкой и обучением подкрепления в коде обучения на больших языковых моделях» [2024-06] [Paper]
Код-оптимиза : «Кода-оптимизация: данные самогенерированных предпочтений для правильности и эффективности» [2024-06] [Paper]
Unicoder : «Unicoder: Code Code большой языковой модель через универсальный код» [2024-06] [ACL 2024] [Paper]
«Благодарность-это душа остроумия: обрезка длинных файлов для генерации кода» [2024-06] [Paper]
«Код меньше, выровняйте больше: эффективная тонкая настройка LLM для генерации кода с обрезкой данных» [2024-07] [Paper]
Inversecoder : «Inversecoder: выпущение мощности LLMS, настроенных на инструкции, с обратной установкой» [2024-07] [Paper]
«Обучение учебным программам для малых моделей языка кода» [2024-07] [Paper]
Генетический инстактр : «Генетический инструкт: масштабирование синтетической генерации инструкций кодирования для моделей крупных языков» [2024-07] [Paper]
DataScope : «Синтез набора данных API-управляемых с большими моделями Conetune крупных кодов» [2024-08] [Paper]
** xcoder **: «Как выполняет ваш код LLM?
Галла : «Галла: график выравнивал большие языковые модели для улучшения понимания исходного кода» [2024-09] [Paper]
Hexacoder : «Hexacoder: Gesect Code Generation с помощью синтетического обучения Oracle с помощью Oracle» [2024-09] [Paper]
AMR-EVOL : «AMR-EVOL: Эволюция адаптивного модульного ответа вызывает лучшую дистилляцию знаний для крупных языковых моделей в генерации кода» [2024-10] [Paper]
Lintseq : «Модели языка обучения на последовательностях синтетических редактирования улучшают синтез кода» [2024-10] [Paper]
COBA : «COBA: Balancer с конвергенцией для многозадачного создания больших языковых моделей» [2024-10] [EMNLP 2024] [Paper]
Coursorcore : «Cursorcore: Помощь в программировании через что-нибудь по сравнению с чем-либо» [2024-10] [Paper]
SelfCodeAlign : «SelfCodeAlign: самоопределение для генерации кода» [2024-10] [Paper]
«Освоение ремесла синтеза данных для коделлм» [2024-10] [Paper]
Коделутра : «Коделутра: повышение генерации кода LLM посредством уточнения под предпочтениями» [2024-11] [Paper]
DSTC : «DSTC: Прямое обучение предпочтениям с самостоятельными тестами и кодом для улучшения кода LMS» [2024-11] [Paper]
Compcoder : «Генерация с компилятором с обратной связью компилятора» [2022-03] [ACL 2022] [Paper]
Coderl : «Coderl: Mastering Generation с помощью предварительно подготовленных моделей и обучения глубоким подкреплением» [2022-07] [Neurips 2022] [Paper] [Repo]
PPOCODER : «Генерация кода на основе выполнения с использованием глубокого обучения подкреплению» [2023-01] [TMLR 2023] [Paper] [Repo]
RLTF : «RLTF: Увеличение подкрепления от обратной связи единичных испытаний» [2023-07] [Paper] [Repo]
B-Coder : «B-Coder: Обучение на основе глубокого армирования на основе ценностей для синтеза программы» [2023-10] [ICLR 2024] [Paper]
Ircoco : «Ircoco: немедленное обучение глубоким подкреплению, управляемое вознаграждениями для завершения кода» [2024-01] [FSE 2024] [Paper]
Отчетный кодер : «Отчетник: улучшить генерацию кода с помощью обучения подкреплению от обратной связи компилятора» [2024-02] [ACL 2024] [Paper]
RLPF & DPA : «LLMS, выдвинутые на производительность для генерации быстрого кода» [2024-04] [Paper]
«Измерение запоминания в RLHF для завершения кода» [2024-06] [Paper]
«Применение RLAIF для генерации кода с API-USAGE в легких LLMS» [2024-06] [Paper]
RLCODER : «RLCODER: обучение подкрепления для завершения кода на уровне хранилища» [2024-07] [Paper]
PF-PPO : «Фильтрация политики в RLHF для Fine-Tune LLM для генерации кода» [2024-09] [Paper]
Кофе-гим : «Кофейный гим: среда для оценки и улучшения обратной связи естественного языка по ошибочному коду» [2024-09] [Paper]
RLEF : «RLEF: код заземления LLMS в обратной связи с подкреплением» [2024-10] [Paper]
Codepmp : «Codepmp: масштабируемая модель предпочтения предварительно подготовка для рассуждения о большой языке» [2024-10] [Paper]
CodedPo : «CodedPo: выравнивающие кодовые модели с самого сгенерированным и проверенным исходным кодом» [2024-10] [Paper]
«Оптимизация политики под контролем процесса для генерации кода» [2024-10] [Paper]
«Выравнивание коделлм с прямой оптимизацией предпочтений» [2024-10] [Paper]
Сокол : «Сокол: адаптивная адаптивная/кратковременная память, управляемая обратной связью» [2024-10] [Paper]
PFPO : «Оптимизация предпочтений для рассуждения с псевдо обратной связью» [2024-11] [Paper]
PAL : «PAL: модели языка с программой» [2022-11] [ICML 2023] [Paper] [Repo]
Горник : «Программа мыслей, подсказывающая: раскрытие вычислений от рассуждений по численным задачам» [2022-11] [TMLR 2023] [Paper] [Repo]
PAD : «PAD: Программная дистилляция может научить мелких моделей, рассуждать лучше, чем цепь той настройки» [2023-05] [NAACL 2024] [Paper]
CSV : «Решение сложных задач по математическим словам с использованием интерпретатора кода GPT-4 с самоверовым на основе кода» [2023-08] [ICLR 2024] [Paper]
MathCoder : «MathCoder: бесшовная интеграция кода в LLMS для улучшенных математических рассуждений» [2023-10] [ICLR 2024] [Paper]
COC : «Цепочка кода: рассуждения с эмулятором кода с моделью языка» [2023-12] [ICML 2024] [Paper]
Марио : «Марио: математическая рассуждения с выводом интерпретатора кода-воспроизводимый трубопровод» [2024-01] [ACL 2024 выводы] [Paper]
Regal : «Regal: программы рефакторинга для обнаружения обобщаемых абстракций» [2024-01] [ICML 2024] [Paper]
«Действия исполняемого кода вызывают лучшие агенты LLM» [2024-02] [ICML 2024] [Paper]
Hpropro : «Изучение гибридного ответа на вопрос с помощью программного подсказки» [2024-02] [ACL 2024] [Paper]
Xstreet : «Выявление лучших многоязычных структурированных рассуждений из LLMS до кода» [2024-03] [ACL 2024] [Paper]
Flowmind : «Flowmind: автоматическое генерация рабочих процессов с LLMS» [2024-03] [Paper]
Think и Execute : «Языковые модели как компиляторы: моделирование выполнения псевдокода улучшает алгоритмические рассуждения в языковых моделях» [2024-04] [Paper]
Ядро : «Ядро: LLM как интерпретатор для программирования естественного языка, программирования псевдокода и программирования потока агентов ИИ» [2024-05] [Paper]
Mumath-код : «Mumath-код: комбинирование больших языковых моделей с использованием инструментов с многоперспективным увеличением данных для математических рассуждений» [2024-05] [Paper]
Cogex : «Learning to рассуждать с помощью генерации, эмуляции и поиска программ» [2024-05] [Paper]
«Арифметические рассуждения с LLM: генерация и перестановка пролог» [2024-05] [Paper]
"Могут ли LLMS разум в дикой природе с программами?" [2024-06] [бумага]
Дотамат : «Дотамат: разложение мышления с помощью кодовой помощи и самокоррекции для математических рассуждений» [2024-07] [Paper]
Cibench : «Cibench: оценка ваших LLM с помощью плагина интерпретатора кода» [2024-07] [Paper]
Pybench : «Pybench: оценка агента LLM на различных реальных задачах кодирования» [2024-07] [Paper]
Adacoder : «Adacoder: адаптивное приглашение сжатие для программного визуального ответа» [2024-07] [Paper]
Pyramidcoder : «Pyramid Coder: иерархический код генератор для композиционного визуального ответа» [2024-07] [Paper]
CodeGraph : «CodeGraph: улучшение рассуждений с графом LLM с кодом» [2024-08] [Paper]
SIAM : «Сиам: самосовершенствование кода математические рассуждения о крупных языковых моделях» [2024-08] [Paper]
Codeplan : «Codeplan: разблокировка потенциала рассуждения в крупных моделях Langauge путем масштабирования планирования формы кода» [2024-09] [Paper]
Горник : «Доказательство мышления: синтез нейросимболической программы допускает надежные и интерпретируемые рассуждения» [2024-09] [Paper]
Метамат : «Метамат: интеграция естественного языка и кода для улучшенных математических рассуждений в моделях крупных языков» [2024-09] [Paper]
«Babelbench: Omni Clandmark для кодового анализа мультимодальных и мультиструктурированных данных» [2024-10] [Paper]
Codesteer : «Управление большими языковыми моделями между выполнением кода и текстовыми рассуждениями» [2024-10] [Paper]
MathCoder2 : «MathCoder2: лучшие математические рассуждения от продолжающейся предварительной подготовки по математическому коду, трансляционному модели» [2024-10] [Paper]
LLMFP : «Планирование чего-либо с строгостью: планирование общего назначения с нулевым выстрелом с формализованным программированием на основе LLM» [2024-10] [Paper]
Докажите : «Не все голоса считаются программами как проверки улучшают самосогласованность языковых моделей для математических рассуждений» [2024-10] [Paper]
Докажите : «Доверьтесь, но проверьте: программная оценка VLM в дикой природе» [2024-10] [Paper]
Геокодер : «Геокодер: решение задач геометрии путем генерации модульного кода через модели на языке зрения» [2024-10] [Paper]
DeaseAgain : «Разрешение: Использование извлекаемых символических программ для оценки математических рассуждений» [2024-10] [Paper]
GFP : «Подсказка заполнения пробелов усиливает математические рассуждения с помощью кода» [2024-11] [Paper]
Utmath : «Utmath: оценка математики с помощью единичного теста посредством мысли о рассуждениях о кодировании» [2024-11] [Paper]
Кокоп : «Кокоп: Улучшение текстовой классификации с помощью LLM с помощью подсказки завершения кода» [2024-11] [Paper]
Планирование Replan : «Интерактивное и выразительное планирование кода с большими языковыми моделями» [2024-11] [Paper]
«Проблемы моделирования кода для больших языковых моделей» [2024-01] [Paper]
«Codemind: основа для бросания в борьбу с большими языковыми моделями для рассуждения кода» [2024-02] [Paper]
«Выполнение алгоритмов, описываемых естественным языком с большими языковыми моделями: исследование» [2024-02] [Paper]
«Могут ли языковые модели притворяться решателями? Моделирование логического кода с LLMS» [2024-03] [Paper]
«Оценка крупных языковых моделей с поведением времени выполнения программы» [2024-03] [Paper]
«Далее: обучение крупным языковым моделям для разума об выполнении кода» [2024-04] [ICML 2024] [Paper]
«SelfPico: самостоятельное выполнение частичного кода с LLMS» [2024-07] [Paper]
«Большие языковые модели как исполнители кода: исследовательское исследование» [2024-10] [Paper]
«VisualCoder: направление больших языковых моделей в выполнении кода с помощью мелкозернистой мультимодальной цепочки мыслей» [2024-10] [Paper]
Самооборота : «Генерация кода самостоятельной работы через CHATGPT» [2023-04] [Paper]
Чатдев : «Коммуникативные агенты для разработки программного обеспечения» [2023-07] [Paper] [Repo]
METAGPT : «Метагпт: метапрограммирование для многоагентной совместной системы» [2023-08] [Paper] [Repo]
CODECAUN : «Кодахня: к генерации модульного кода через цепочку самостоятельных разложений с репрезентативными подмодулами» [2023-10] [ICLR 2024] [Paper]
Codeagent : «Кодекс: улучшение генерации кода с помощью интегрированных инструментов систем агентов для реальных задач кодирования в реальном мире» [2024-01] [ACL 2024] [Paper]
Conline : «Conline: Комплексное генерация и утонченность кода с помощью онлайн-поиска и испытания правильности» [2024-03] [Paper]
LCG : «Когда генерация кодов на основе LLM соответствует процессу разработки программного обеспечения» [2024-03] [Paper]
RepairAgent : «RepairAgent: автономный агент на основе LLM для ремонта программы» [2024-03] [Paper]
MAGIS :: «Magis: LLM на основе многоагентной структуры для решения выпуска GitHub» [2024-03] [Paper]
SOA : «Самоорганизованные агенты: многоагентная структура LLM в направлении Ultra крупномасштабной генерации и оптимизации кода» [2024-04] [Paper]
Autocoderover : «Autocoderover: Автономное улучшение программы» [2024-04] [Paper]
Swe-Agent : «Swe-Agent: Agent-Computer Interfaces включает автоматизированную разработку программного обеспечения» [2024-05] [Paper]
MapCoder : «MapCoder: генерация многоагентных кодов для решения конкурентных проблем» [2024-05] [ACL 2024] [Paper]
«Борьба с огнем с огнем: сколько мы можем доверять CHATGPT по задачам, связанным с исходным кодом?» [2024-05] [бумага]
FunCoder : "Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation" [2024-05] [paper]
CTC : "Multi-Agent Software Development through Cross-Team Collaboration" [2024-06] [paper]
MASAI : "MASAI: Modular Architecture for Software-engineering AI Agents" [2024-06] [paper]
AgileCoder : "AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology" [2024-06] [paper]
CodeNav : "CodeNav: Beyond tool-use to using real-world codebases with LLM agents" [2024-06] [paper]
INDICT : "INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness" [2024-06] [paper]
AppWorld : "AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents" [2024-07] [paper]
CortexCompile : "CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis" [2024-08] [paper]
Survey : "Large Language Model-Based Agents for Software Engineering: A Survey" [2024-09] [paper]
AutoSafeCoder : "AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing" [2024-09] [paper]
SuperCoder2.0 : "SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer" [2024-09] [paper]
Survey : "Agents in Software Engineering: Survey, Landscape, and Vision" [2024-09] [paper]
MOSS : "MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents" [2024-09] [paper]
HyperAgent : "HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale" [2024-09] [paper]
"Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective" [2024-09] [paper]
RGD : "RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance" [2024-10] [paper]
AutoML-Agent : "AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML" [2024-10] [paper]
Seeker : "Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent Approach" [2024-10] [paper]
REDO : "REDO: Execution-Free Runtime Error Detection for COding Agents" [2024-10] [paper]
"Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios" [2024-10] [paper]
EvoMAC : "Self-Evolving Multi-Agent Collaboration Networks for Software Development" [2024-10] [paper]
VisionCoder : "VisionCoder: Empowering Multi-Agent Auto-Programming for Image Processing with Hybrid LLMs" [2024-10] [paper]
AutoKaggle : "AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions" [2024-10] [paper]
Watson : "Watson: A Cognitive Observability Framework for the Reasoning of Foundation Model-Powered Agents" [2024-11] [paper]
CodeTree : "CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models" [2024-11] [paper]
EvoCoder : "LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues" [2024-11] [paper]
"Interactive Program Synthesis" [2017-03] [paper]
"Question selection for interactive program synthesis" [2020-06] [PLDI 2020] [paper]
"Interactive Code Generation via Test-Driven User-Intent Formalization" [2022-08] [paper]
"Improving Code Generation by Training with Natural Language Feedback" [2023-03] [TMLR] [paper]
"Self-Refine: Iterative Refinement with Self-Feedback" [2023-03] [NeurIPS 2023] [paper]
"Teaching Large Language Models to Self-Debug" [2023-04] [paper]
"Self-Edit: Fault-Aware Code Editor for Code Generation" [2023-05] [ACL 2023] [paper]
"LeTI: Learning to Generate from Textual Interactions" [2023-05] [paper]
"Is Self-Repair a Silver Bullet for Code Generation?" [2023-06] [ICLR 2024] [paper]
"InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback" [2023-06] [NeurIPS 2023] [paper]
"INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair" [2023-11] [ACL 2024 Findings] [paper]
"OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement" [2024-02] [ACL 2024 Findings] [paper]
"Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback" [2024-03] [ACL 2024 Findings] [paper]
"CYCLE: Learning to Self-Refine the Code Generation" [2024-03] [paper]
"LLM-based Test-driven Interactive Code Generation: User Study and Empirical Evaluation" [2024-04] [paper]
"SOAP: Enhancing Efficiency of Generated Code via Self-Optimization" [2024-05] [paper]
"Code Repair with LLMs gives an Exploration-Exploitation Tradeoff" [2024-05] [paper]
"ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation" [2024-05] [paper]
"Training LLMs to Better Self-Debug and Explain Code" [2024-05] [paper]
"Requirements are All You Need: From Requirements to Code with LLMs" [2024-06] [paper]
"I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation" [2024-07] [paper]
"An Empirical Study on Self-correcting Large Language Models for Data Science Code Generation" [2024-08] [paper]
"RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation" [2024-09] [paper]
"From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging" [2024-10] [paper] [repo]
"What Makes Large Language Models Reason in (Multi-Turn) Code Generation?" [2024-10] [paper]
"The First Prompt Counts the Most! An Evaluation of Large Language Models on Iterative Example-based Code Generation" [2024-11] [paper]
"Planning-Driven Programming: A Large Language Model Programming Workflow" [2024-11] [paper]
"ConAIR:Consistency-Augmented Iterative Interaction Framework to Enhance the Reliability of Code Generation" [2024-11] [paper]
"MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding" [2021-10] [ACL 2022] [paper]
"WebKE: Knowledge Extraction from Semi-structured Web with Pre-trained Markup Language Model" [2021-10] [CIKM 2021] [paper]
"WebGPT: Browser-assisted question-answering with human feedback" [2021-12] [paper]
"CM3: A Causal Masked Multimodal Model of the Internet" [2022-01] [paper]
"DOM-LM: Learning Generalizable Representations for HTML Documents" [2022-01] [paper]
"WebFormer: The Web-page Transformer for Structure Information Extraction" [2022-02] [WWW 2022] [paper]
"A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility" [2022-02] [ECCV 2022] [paper]
"WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents" [2022-07] [NeurIPS 2022] [paper]
"Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding" [2022-10] [ICML 2023] [paper]
"Understanding HTML with Large Language Models" [2022-10] [EMNLP 2023 findings] [paper]
"WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics" [2023-01] [CHI 2023] [paper]
"Mind2Web: Towards a Generalist Agent for the Web" [2023-06] [NeurIPS 2023] [paper]
"A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis", [2023-07] [ICLR 2024] [paper]
"WebArena: A Realistic Web Environment for Building Autonomous Agents" [2023-07] [paper]
"CogAgent: A Visual Language Model for GUI Agents" [2023-12] [paper]
"GPT-4V(ision) is a Generalist Web Agent, if Grounded" [2024-01] [paper]
"WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models" [2024-01] [paper]
"WebLINX: Real-World Website Navigation with Multi-Turn Dialogue" [2024-02] [paper]
"OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web" [2024-02] [paper]
"AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent" [2024-04] [paper]
"WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents" [2024-04] [paper]
"AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation" [2024-04] [paper]
"GUICourse: From General Vision Language Models to Versatile GUI Agents" [2024-06] [paper]
"NaviQAte: Functionality-Guided Web Application Navigation" [2024-09] [paper]
"MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding" [2024-09] [paper]
"Multimodal Auto Validation For Self-Refinement in Web Agents" [2024-10] [paper]
"Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents" [2024-10] [paper]
"Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation" [2024-10] [paper]
"Harnessing Webpage UIs for Text-Rich Visual Understanding" [2024-10] [paper]
"AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents" [2024-10] [paper]
"Beyond Browsing: API-Based Web Agents" [2024-10] [paper]
"Large Language Models Empowered Personalized Web Agents" [2024-10] [paper]
"AdvWeb: Controllable Black-box Attacks on VLM-powered Web Agents" [2024-10] [paper]
"Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents" [2024-10] [paper]
"OS-ATLAS: A Foundation Action Model for Generalist GUI Agents" [2024-10] [paper]
"From Context to Action: Analysis of the Impact of State Representation and Context on the Generalization of Multi-Turn Web Navigation Agents" [2024-10] [paper]
"AutoGLM: Autonomous Foundation Agents for GUIs" [2024-10] [paper]
"WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning" [2024-11] [paper]
"The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use" [2024-11] [paper]
"ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data" [2024-11] [paper]
"ShowUI: One Vision-Language-Action Model for GUI Visual Agent" [2024-11] [paper]
[ Ruby ] "On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages" [2022-04] [ICPC 2022] [paper]
[ Verilog ] "Benchmarking Large Language Models for Automated Verilog RTL Code Generation" [2022-12] [DATE 2023] [paper]
[ OCL ] "On Codex Prompt Engineering for OCL Generation: An Empirical Study" [2023-03] [MSR 2023] [paper]
[ Ansible-YAML ] "Automated Code generation for Information Technology Tasks in YAML through Large Language Models" [2023-05] [DAC 2023] [paper]
[ Hansl ] "The potential of LLMs for coding with low-resource and domain-specific programming languages" [2023-07] [paper]
[ Verilog ] "VeriGen: A Large Language Model for Verilog Code Generation" [2023-07] [paper]
[ Verilog ] "RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model" [2023-08] [paper]
[ Racket, OCaml, Lua, R, Julia ] "Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs" [2023-08] [paper]
[ Verilog ] "VerilogEval: Evaluating Large Language Models for Verilog Code Generation" [2023-09] [ICCAD 2023] [paper]
[ Verilog ] "RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models" [2023-11] [paper]
[ Verilog ] "Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis" [2023-12] [paper]
[ Verilog ] "RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution" [2023-12] [paper]
[ Verilog ] "BetterV: Controlled Verilog Generation with Discriminative Guidance" [2024-02] [ICML 2024] [paper]
[ R ] "Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R" [2024-03] [paper]
[ Haskell ] "Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study" [2024-03] [paper]
[ Verilog ] "A Multi-Expert Large Language Model Architecture for Verilog Code Generation" [2024-04] [paper]
[ Verilog ] "CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation" [2024-04] [paper]
[ Alloy ] "An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications" [2024-04] [paper]
[ Verilog ] "Evaluating LLMs for Hardware Design and Test" [2024-04] [paper]
[ Kotlin, Swift, and Rust ] "Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT" [2024-04] [paper]
[ Verilog ] "MEIC: Re-thinking RTL Debug Automation using LLMs" [2024-05] [paper]
[ Bash ] "Tackling Execution-Based Evaluation for NL2Bash" [2024-05] [paper]
[ Fortran, Julia, Matlab, R, Rust ] "Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust" [2024-05] [paper]
[ OpenAPI ] "Optimizing Large Language Models for OpenAPI Code Completion" [2024-05] [paper]
[ Kotlin ] "Kotlin ML Pack: Technical Report" [2024-05] [paper]
[ Verilog ] "VerilogReader: LLM-Aided Hardware Test Generation" [2024-06] [paper]
"Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming" [2024-06] [paper]
[ Logo ] "Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment" [2024-06] [paper]
[ Ansible YAML, Bash ] "DocCGen: Document-based Controlled Code Generation" [2024-06] [paper]
[ Qiskit ] "Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models" [2024-06] [paper]
[ Perl, Golang, Swift ] "DistiLRR: Transferring Code Repair for Low-Resource Programming Languages" [2024-06] [paper]
[ Verilog ] "AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation" [2024-06] [paper]
"A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation" [2024-07] [paper]
[ Json, XLM, YAML ] "ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages" [2024-07] [paper]
[ Verilog ] "AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design" [2024-07] [paper]
[ Verilog ] "CodeV: Empowering LLMs for Verilog Generation through Multi-Level Summarization" [2024-07] [paper]
[ Verilog ] "ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation" [2024-07] [paper]
[ Verilog ] "OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection" [2024-07] [paper]
[ Verilog ] "Large Language Model for Verilog Generation with Golden Code Feedback" [2024-07] [paper]
[ Verilog ] "AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs" [2024-07] [paper]
[ RPA ] "Plan with Code: Comparing approaches for robust NL to DSL generation" [2024-08] [paper]
[ Verilog ] "VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool" [2024-08] [paper]
[ Verilog ] "Revisiting VerilogEval: Newer LLMs, In-Context Learning, and Specification-to-RTL Tasks" [2024-08] [paper]
[ MaxMSP, Web Audio ] "Benchmarking LLM Code Generation for Audio Programming with Visual Dataflow Languages" [2024-09] [paper]
[ Verilog ] "RTLRewriter: Methodologies for Large Models aided RTL Code Optimization" [2024-09] [paper]
[ Verilog ] "CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair" [2024-09] [paper]
[ Bash ] "ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement" [2024-09] [paper]
[ Survey ] "Survey on Code Generation for Low resource and Domain Specific Programming Languages" [2024-10] [paper]
[ R ] "Do Current Language Models Support Code Intelligence for R Programming Language?" [2024-10] [paper]
"Can Large Language Models Generate Geospatial Code?" [2024-10] [paper]
[ PLC ] "Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents" [2024-10] [paper]
[ Lua ] "Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks" [2024-10] [paper]
"Improving Parallel Program Performance Through DSL-Driven Code Generation with LLM Optimizers" [2024-10] [paper]
"GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks" [2024-10] [paper]
[ R, D, Racket, Bash ]: "Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Code" [2024-10] [paper]
[ SPICE ]: "SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance" [2024-10] [paper]
[ IEC 61131-3 ST ]: "Training LLMs for Generating IEC 61131-3 Structured Text with Online Feedback" [2024-10] [paper]
[ Verilog ] "MetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMs" [2024-11] [paper]
[ Verilog ] "CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design" [2024-11] [paper]
[ MUMPS, ALC ] "Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation" [2024-11] [paper]
For each task, the first column contains non-neural methods (eg n-gram, TF-IDF, and (occasionally) static program analysis); the second column contains non-Transformer neural methods (eg LSTM, CNN, GNN); the third column contains Transformer based methods (eg BERT, GPT, T5).
"Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency" [2023-09] [ACL 2024] [paper]
"Self-Infilling Code Generation" [2023-11] [ICML 2024] [paper]
"JumpCoder: Go Beyond Autoregressive Coder via Online Modification" [2024-01] [ACL 2024] [paper]
"Unsupervised Evaluation of Code LLMs with Round-Trip Correctness" [2024-02] [ICML 2024] [paper]
"The Larger the Better? Improved LLM Code-Generation via Budget Reallocation" [2024-03] [paper]
"Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models" [2024-03] [ACL 2024] [paper]
"Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective" [2024-04] [ACL 2024 Findings] [paper]
"Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs" [2024-04] [paper]
"Quality Assessment of Prompts Used in Code Generation" [2024-04] [paper]
"Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation" [2024-04] [paper]
"Large Language Models Synergize with Automated Machine Learning" [2024-05] [paper]
"Model Cascading for Code: Reducing Inference Costs with Model Cascading for LLM Based Code Generation" [2024-05] [paper]
"A Survey on Large Language Models for Code Generation" [2024-06] [paper]
"Is Programming by Example solved by LLMs?" [2024-06] [paper]
"Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" [2024-06] [paper]
"MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning" [2024-06] [ACL 2024] [paper]
"Revisiting the Impact of Pursuing Modularity for Code Generation" [2024-07] [paper]
"Evaluating Long Range Dependency Handling in Code Generation Models using Multi-Step Key Retrieval" [2024-07] [paper]
"When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention" [2024-07] [paper]
"Assessing Programming Task Difficulty for Efficient Evaluation of Large Language Models" [2024-07] [paper]
"ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models" [2024-08] [ACL 2024] [paper]
"Fine-tuning Language Models for Joint Rewriting and Completion of Code with Potential Bugs" [2024-08] [ACL 2024 Findings] [paper]
"Selective Prompt Anchoring for Code Generation" [2024-08] [paper]
"Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer" [2024-08] [paper]
"Optimizing Large Language Model Hyperparameters for Code Generation" [2024-08] [paper]
"EPiC: Cost-effective Search-based Prompt Engineering of LLMs for Code Generation" [2024-08] [paper]
"CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers" [2024-08] [paper]
"No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair" [2024-09] [paper]
"Planning In Natural Language Improves LLM Search For Code Generation" [2024-09] [paper]
"Multi-Programming Language Ensemble for Code Generation in Large Language Model" [2024-09] [paper]
"A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement" [2024-09] [paper]
"USCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding" [2024-09] [paper]
"Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation" [2024-09] [paper]
"Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity" [2024-09] [paper]
"Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning" [2024-10] [paper]
"Showing LLM-Generated Code Selectively Based on Confidence of LLMs" [2024-10] [paper]
"AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generation" [2024-10] [paper]
"Enhancing LLM Agents for Code Generation with Possibility and Pass-rate Prioritized Experience Replay" [2024-10] [paper]
"From Solitary Directives to Interactive Encouragement! LLM Secure Code Generation by Natural Language Prompting" [2024-10] [paper]
"Self-Explained Keywords Empower Large Language Models for Code Generation" [2024-10] [paper]
"Context-Augmented Code Generation Using Programming Knowledge Graphs" [2024-10] [paper]
"In-Context Code-Text Learning for Bimodal Software Engineering" [2024-10] [paper]
"Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis" [2024-10] [paper]
"Less is More: DocString Compression in Code Generation" [2024-10] [paper]
"Multi-Programming Language Sandbox for LLMs" [2024-10] [paper]
"Personality-Guided Code Generation Using Large Language Models" [2024-10] [paper]
"Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?" [2024-11] [paper]
"Scattered Forest Search: Smarter Code Space Exploration with LLMs" [2024-11] [paper]
"Anchor Attention, Small Cache: Code Generation with Large Language Models" [2024-11] [paper]
"ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation" [2024-11] [paper]
"SRA-MCTS: Self-driven Reasoning Aurmentation with Monte Carlo Tree Search for Enhanced Code Generation" [2024-11] [paper]
"CodeGRAG: Extracting Composed Syntax Graphs for Retrieval Augmented Cross-Lingual Code Generation" [2024-05] [paper]
"Prompt-based Code Completion via Multi-Retrieval Augmented Generation" [2024-05] [paper]
"A Lightweight Framework for Adaptive Retrieval In Code Completion With Critique Model" [2024-06] [papaer]
"Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation" [2024-09] [paper]
"Building A Coding Assistant via the Retrieval-Augmented Language Model" [2024-10] [paper]
"DroidCoder: Enhanced Android Code Completion with Context-Enriched Retrieval-Augmented Generation" [2024-10] [ASE 2024] [paper]
"Assessing the Answerability of Queries in Retrieval-Augmented Code Generation" [2024-11] [paper]
"Fault-Aware Neural Code Rankers" [2022-06] [NeurIPS 2022] [paper]
"Functional Overlap Reranking for Neural Code Generation" [2023-10] [ACL 2024 Findings] [paper]
"Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking" [2024-08] [paper]
"DOCE: Finding the Sweet Spot for Execution-Based Code Generation" [2024-08] [paper]
"Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates" [2024-08] [paper]
"B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests" [2024-09] [paper]
"Learning Code Preference via Synthetic Evolution" [2024-10] [paper]
"Tree-to-tree Neural Networks for Program Translation" [2018-02] [NeurIPS 2018] [paper]
"Program Language Translation Using a Grammar-Driven Tree-to-Tree Model" [2018-07] [paper]
"Unsupervised Translation of Programming Languages" [2020-06] [NeurIPS 2020] [paper]
"Leveraging Automated Unit Tests for Unsupervised Code Translation" [2021-10] [ICLR 2022] paper]
"Code Translation with Compiler Representations" [2022-06] [ICLR 2023] [paper]
"Multilingual Code Snippets Training for Program Translation" [2022-06] [AAAI 2022] [paper]
"BabelTower: Learning to Auto-parallelized Program Translation" [2022-07] [ICML 2022] [paper]
"Syntax and Domain Aware Model for Unsupervised Program Translation" [2023-02] [ICSE 2023] [paper]
"CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution" [2023-06] [paper]
"Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code" [2023-08] [ICSE 2024] [paper]
"On the Evaluation of Neural Code Translation: Taxonomy and Benchmark", 2023-08, ASE 2023, [paper]
"Program Translation via Code Distillation" [2023-10] [EMNLP 2023] [paper]
"Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations" [2023-11] [EMNLP 2023 Findings] [paper]
"Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation" [2024-03] [paper]
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"LogStamp: Automatic Online Log Parsing Based on Sequence Labelling" [2022-08] [paper]
"Log Parsing with Prompt-based Few-shot Learning" [2023-02] [ICSE 2023] [paper]
"Log Parsing: How Far Can ChatGPT Go?" [2023-06] [ASE 2023] [paper]
"LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis" [2023-08] [paper]
"LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection" [2023-09] [paper]
"An Assessment of ChatGPT on Log Data" [2023-09] [paper]
"LILAC: Log Parsing using LLMs with Adaptive Parsing Cache" [2023-10] [paper]
"LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing" [2024-04] [paper]
"On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations" [2024-05] [paper]
"Log Parsing with Self-Generated In-Context Learning and Self-Correction" [2024-06] [paper]
"Stronger, Faster, and Cheaper Log Parsing with LLMs" [2024-06] [paper]
"ULog: Unsupervised Log Parsing with Large Language Models through Log Contrastive Units" [2024-06] [paper]
"Anomaly Detection on Unstable Logs with GPT Models" [2024-06] [paper]
"LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models" [2024-08] [KDD 2024] [paper]
"LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models" [2024-09] [paper]
"A Comparative Study on Large Language Models for Log Parsing" [2024-09] [paper]
"What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach" [2024-10] [paper]
"LogLM: From Task-based to Instruction-based Automated Log Analysis" [2024-10] [paper]
"Configuration Validation with Large Language Models" [2023-10] [paper]
"CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation" [2023-11] [paper]
"Can LLMs Configure Software Tools" [2023-12] [paper]
"LuaTaint: A Static Analysis System for Web Configuration Interface Vulnerability of Internet of Things Devices" [2024-02] [IOT] [paper]
"LLM-Based Misconfiguration Detection for AWS Serverless Computing" [2024-11] [paper]
"LogLLM: Log-based Anomaly Detection Using Large Language Models" [2024-11] [paper]
"Towards using Few-Shot Prompt Learning for Automating Model Completion" [2022-12] [paper]
"Model Generation from Requirements with LLMs: an Exploratory Study" [2024-04] [paper]
"How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts" [2024-04] [paper]
"Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" [2024-06] [paper]
"Studying and Benchmarking Large Language Models For Log Level Suggestion" [2024-10] [paper]
"A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decomposition" [2024-10] [paper]
"On the Utility of Domain Modeling Assistance with Large Language Models" [2024-10] [paper]
"On the use of Large Language Models in Model-Driven Engineering" [2024-10] [paper]
"LLM as a code generator in Agile Model Driven Development" [2024-10] [paper]
"A Transformer-based Approach for Abstractive Summarization of Requirements from Obligations in Software Engineering Contracts" [2023-09] [RE 2023] [paper]
"Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs" [2023-10] [paper]
"Requirements Engineering using Generative AI: Prompts and Prompting Patterns" [2023-11] [paper]
"Prioritizing Software Requirements Using Large Language Models" [2024-04] [paper]
"Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks" [2024-04] [paper]
"Enhancing Legal Compliance and Regulation Analysis with Large Language Models" [2024-04] [paper]
"MARE: Multi-Agents Collaboration Framework for Requirements Engineering" [2024-05] [paper]
"Natural Language Processing for Requirements Traceability" [2024-05] [paper]
"Multilingual Crowd-Based Requirements Engineering Using Large Language Models" [2024-08] [paper]
"From Specifications to Prompts: On the Future of Generative LLMs in Requirements Engineering" [2024-08] [paper]
"Leveraging LLMs for the Quality Assurance of Software Requirements" [2024-08] [paper]
"Generative AI for Requirements Engineering: A Systematic Literature Review" [2024-09] [paper]
"A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study" [2024-09] [paper]
"Leveraging Large Language Models for Predicting Cost and Duration in Software Engineering Projects" [2024-09] [paper]
"Privacy Policy Analysis through Prompt Engineering for LLMs" [2024-09] [paper]
"Exploring Requirements Elicitation from App Store User Reviews Using Large Language Models" [2024-09] [paper]
"LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement" [2024-09] [paper]
"Automatic Instantiation of Assurance Cases from Patterns Using Large Language Models" [2024-10] [paper]
"Whose fault is it anyway? SILC: Safe Integration of LLM-Generated Code" [2024-10] [paper]
"Assured Automatic Programming via Large Language Models" [2024-10] [paper]
"Does GenAI Make Usability Testing Obsolete?" [2024-11] [paper]
"Exploring LLMs for Verifying Technical System Specifications Against Requirements" [2024-11] [paper]
"Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo" [2024-11] [paper]
"You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion" [2021-08] [USENIX Security Symposium 2021] [paper]
"Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?" [2022-04] [Empir. Softw. Eng.] [paper]
"Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants" [2022-08] [USENIX Security Symposium 2023] [paper]
"Do Users Write More Insecure Code with AI Assistants?" [2022-1] [CCS 2023] [paper]
"Large Language Models for Code: Security Hardening and Adversarial Testing" [2023-02] [CCS 2023] [paper]
"Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models" [2023-12] [paper]
"CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion" [2024-03] [ACL 2024 Findings] [paper]
"Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers" [2024-03] [paper]
"DeVAIC: A Tool for Security Assessment of AI-generated Code" [2024-04] [paper]
"CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models" [2024-04] [paper]
"LLMs in Web-Development: Evaluating LLM-Generated PHP code unveiling vulnerabilities and limitations" [2024-04] [paper]
"Do Neutral Prompts Produce Insecure Code? FormAI-v2 Dataset: Labelling Vulnerabilities in Code Generated by Large Language Models" [2024-04] [paper]
"Codexity: Secure AI-assisted Code Generation" [2024-05] [paper]
"Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code" [2024-05] [paper]
"An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection" [2024-06] [paper]
"Is Your AI-Generated Code Really Secure? Evaluating Large Language Models on Secure Code Generation with CodeSecEval" [2024-07] [paper]
"Prompting Techniques for Secure Code Generation: A Systematic Investigation" [2024-07] [paper]
"TAPI: Towards Target-Specific and Adversarial Prompt Injection against Code LLMs" [2024-07] [paper]
"MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants" [2024-07] [paper]
"Eliminating Backdoors in Neural Code Models via Trigger Inversion" [2024-08] [paper]
""You still have to study" -- On the Security of LLM generated code" [2024-08] [paper]
"How Well Do Large Language Models Serve as End-to-End Secure Code Producers?" [2024-08] [paper]
"While GitHub Copilot Excels at Coding, Does It Ensure Responsible Output?" [2024-08] [paper]
"PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)" [2024-09] [paper]
"RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code" [2024-09] [paper]
"Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation" [2024-09] [paper]
"Demonstration Attack against In-Context Learning for Code Intelligence" [2024-10] [paper]
"Hallucinating AI Hijacking Attack: Large Language Models and Malicious Code Recommenders" [2024-10] [paper]
"SecCodePLT: A Unified Platform for Evaluating the Security of Code GenAI" [2024-10] [paper]
"Security of Language Models for Code: A Systematic Literature Review" [2024-10] [paper]
"RedCode: Risky Code Execution and Generation Benchmark for Code Agents" [2024-11] [paper]
"ProSec: Fortifying Code LLMs with Proactive Security Alignment" [2024-11] [paper]
"An Empirical Evaluation of GitHub Copilot's Code Suggestions" [2022-05] [MSR 2022] [paper]
"Large Language Models and Simple, Stupid Bugs" [2023-03] [MSR 2023] [paper]
"Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT" [2023-04] [paper]
"No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT" [2023-08] [paper]
"The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?" [2024-02] [ACL 2024 Findings] [paper]
"Bugs in Large Language Models Generated Code: An Empirical Study" [2024-03] [paper]
"ChatGPT Incorrectness Detection in Software Reviews" [2024-03] [paper]
"Validating LLM-Generated Programs with Metamorphic Prompt Testing" [2024-06] [paper]
"Where Do Large Language Models Fail When Generating Code?" [2024-06] [paper]
"GitHub Copilot: the perfect Code compLeeter?" [2024-06] [paper]
"What's Wrong with Your Code Generated by Large Language Models? An Extensive Study" [2024-07] [paper]
"Uncovering Weaknesses in Neural Code Generation" [2024-07] [paper]
"Understanding Defects in Generated Codes by Language Models" [2024-08] [paper]
"CodeSift: An LLM-Based Reference-Less Framework for Automatic Code Validation" [2024-08] [paper]
"Examination of Code generated by Large Language Models" [2024-08] [paper]
"Fixing Code Generation Errors for Large Language Models" [2024-09] [paper]
"Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation" [2024-09] [paper]
"Insights from Benchmarking Frontier Language Models on Web App Code Generation" [2024-09] [paper]
"Evaluating the Performance of Large Language Models in Competitive Programming: A Multi-Year, Multi-Grade Analysis" [2024-09] [paper]
"A Case Study of Web App Coding with OpenAI Reasoning Models" [2024-09] [paper]
"CodeJudge: Evaluating Code Generation with Large Language Models" [2024-10] [paper]
"An evaluation of LLM code generation capabilities through graded exercises" [2024-10] [paper]
"A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?" [2024-11] [paper]
"Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of Different Complexity Levels: An Empirical Analysis" [2024-11] [paper]
"LLM4DS: Evaluating Large Language Models for Data Science Code Generation" [2024-11] [paper]
"A Preliminary Study of Multilingual Code Language Models for Code Generation Task Using Translated Benchmarks" [2024-11] [paper]
"Exploring and Evaluating Hallucinations in LLM-Powered Code Generation" [2024-04] [paper]
"CodeHalu: Code Hallucinations in LLMs Driven by Execution-based Verification" [2024-04] [paper]
"We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs" [2024-06] [paper]
"Code Hallucination" [2024-07] [paper]
"On Mitigating Code LLM Hallucinations with API Documentation" [2024-07] [paper]
"CodeMirage: Hallucinations in Code Generated by Large Language Models" [2024-08] [paper]
"LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation" [2024-09] [paper]
"Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code" [2024-10] [paper]
"ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries" [2024-10] [paper]
"On Evaluating the Efficiency of Source Code Generated by LLMs" [2024-04] [paper]
"A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama" [2024-05] [paper]
"From Effectiveness to Efficiency: Comparative Evaluation of Code Generated by LCGMs for Bilingual Programming Questions" [2024-06] [paper]
"How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark" [2024-06] [paper]
"ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?" [2024-07] [paper]
"A Performance Study of LLM-Generated Code on Leetcode" [2024-07] [paper]
"Evaluating Language Models for Efficient Code Generation" [2024-08] [paper]
"Effi-Code: Unleashing Code Efficiency in Language Models" [2024-10] [paper]
"Rethinking Code Refinement: Learning to Judge Code Efficiency" [2024-10] [paper]
"Generating Energy-efficient code with LLMs" [2024-11] [paper]
"An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2" [2024-11] [paper]
"Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain" [2023-10] [paper]
"Do Large Code Models Understand Programming Concepts? A Black-box Approach" [2024-02] [ICML 2024] [paper]
"Syntactic Robustness for LLM-based Code Generation" [2024-04] [paper]
"NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations" [2024-06] [paper]
"An Empirical Study on Capability of Large Language Models in Understanding Code Semantics" [2024-07] [paper]
"Comparing Robustness Against Adversarial Attacks in Code Generation: LLM-Generated vs. Human-Written" [2024-11] [paper]
"A Critical Study of What Code-LLMs (Do Not) Learn" [2024-06] [ACL 2024 Findings] [paper]
"Looking into Black Box Code Language Models" [2024-07] [paper]
"DeepCodeProbe: Towards Understanding What Models Trained on Code Learn" [2024-07] [paper]
"Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations" [2024-07] [paper]
"How and Why LLMs Use Deprecated APIs in Code Completion? An Empirical Study" [2024-06] [paper]
"Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations" [2024-08] [paper]
"A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How" [2024-09] [paper]
"AutoAPIEval: A Framework for Automated Evaluation of LLMs in API-Oriented Code Generation" [2024-09] [paper]
"Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach" [2024-04] [paper]
"CodeCipher: Learning to Obfuscate Source Code Against LLMs" [2024-10] [paper]
"Decoding Secret Memorization in Code LLMs Through Token-Level Characterization" [2024-10] [paper]
"Exploring Multi-Lingual Bias of Large Code Models in Code Generation" [2024-04] [paper]
"Mitigating Gender Bias in Code Large Language Models via Model Editing" [2024-10] [paper]
"Bias Unveiled: Investigating Social Bias in LLM-Generated Code" [2024-11] [paper]
"Zero-Shot Detection of Machine-Generated Codes" [2023-10] [paper]
"CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code" [2024-04] [paper]
"ChatGPT Code Detection: Techniques for Uncovering the Source of Code" [2024-05] [paper]
"Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" [2024-05] [paper]
"Automatic Detection of LLM-generated Code: A Case Study of Claude 3 Haiku" [2024-09] [paper]
"An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?" [2024-11] [paper]
"Distinguishing LLM-generated from Human-written Code by Contrastive Learning" [2024-11] [paper]
"Who Wrote this Code? Watermarking for Code Generation" [2023-05] [ACL 2024] [paper]
"Testing the Effect of Code Documentation on Large Language Model Code Understanding" [2024-04] [paper]
"Automated Creation of Source Code Variants of a Cryptographic Hash Function Implementation Using Generative Pre-Trained Transformer Models" [2024-04] [paper]
"Evaluation of the Programming Skills of Large Language Models" [2024-05] [paper]
"Where Are Large Language Models for Code Generation on GitHub?" [2024-06] [paper]
"Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models" [2024-06] [paper]
"Benchmarking Language Model Creativity: A Case Study on Code Generation" [2024-07] [paper]
"Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models" [2024-07] [paper]
"Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes" [2024-08] [paper]
"Strategic Optimization and Challenges of Large Language Models in Object-Oriented Programming" [2024-08] [paper]
"A Survey on Evaluating Large Language Models in Code Generation Tasks" [2024-08] [paper]
"An exploratory analysis of Community-based Question-Answering Platforms and GPT-3-driven Generative AI: Is it the end of online community-based learning?" [2024-09] [paper]
"Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B" [2024-09] [paper]
"Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants" [2024-09] [paper]
"Model Editing for LLMs4Code: How Far are We?" [2024-11] [paper]
"An Empirical Study on LLM-based Agents for Automated Bug Fixing" [2024-11] [paper]
"Precision or Peril: Evaluating Code Quality from Quantized Large Language Models" [2024-11] [paper]
"Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models" [2022-04] [CHI EA 2022] [paper]
"Grounded Copilot: How Programmers Interact with Code-Generating Models" [2022-06] [OOPSLA 2023] [paper]
"Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming" [2022-10] [paper]
"The Impact of AI on Developer Productivity: Evidence from GitHub Copilot" [2023-02] [paper]
"The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development" [2023-02] [IUI 2023] [paper]
""It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers" [2023-04] [ACM TCHI] [paper]
"DevGPT: Studying Developer-ChatGPT Conversations" [2023-08] [paper]
"How Do Analysts Understand and Verify AI-Assisted Data Analyses?" [2023-09] [paper]
"How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment" [2023-09] [Koli Calling 2023] [paper]
"Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities" [2023-10] [paper]
"The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers" [2024-04] [paper]
"Unlocking Adaptive User Experience with Generative AI" [2024-04] [paper]
"BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks" [2024-04] [paper]
"How far are AI-powered programming assistants from meeting developers' needs?" [2024-04] [paper]
"Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice" [2024-04] [paper]
"The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances" [2024-04] [paper]
"amplified.dev: a living document that begins to sketch a vision for a future where developers are amplified, not automated" [2024-05] [paper]
"Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches" [2024-05] [paper]
"Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns" [2024-05] [paper]
"Full Line Code Completion: Bringing AI to Desktop" [2024-05] [paper]
"Developers' Perceptions on the Impact of ChatGPT in Software Development: A Survey" [2024-05] [paper]
"A Transformer-Based Approach for Smart Invocation of Automatic Code Completion" [2024-05] [paper]
"A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions" [2024-05] [paper]
"Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT" [2024-05] [paper]
"Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent" [2024-05] [paper]
"Learning Task Decomposition to Assist Humans in Competitive Programming" [2024-06] [ACL 2024] [paper]
"Impact of AI-tooling on the Engineering Workspace" [2024-06] [paper]
"Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward" [2024-06] [paper]
"Instruct, Not Assist: LLM-based Multi-Turn Planning and Hierarchical Questioning for Socratic Code Debugging" [2024-06] [paper]
"Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects" [2024-06] [paper]
"Let the Code LLM Edit Itself When You Edit the Code" [2024-07] [paper]
"Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation" [2024-07] [paper]
"How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course" [2024-07] [paper]
"Can Developers Prompt? A Controlled Experiment for Code Documentation Generation" [2024-08] [paper]
"The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies" [2024-09] [paper]
"Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering" [2024-09] [paper]
"The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot" [2024-09] [paper]
""I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development" [2024-09] [paper]
"Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development" [2024-09] [paper]
"Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks" [2024-10] [paper]
"Code-Survey: An LLM-Driven Methodology for Analyzing Large-Scale Codebases" [2024-10] [paper]
"The potential of LLM-generated reports in DevSecOps" [2024-10] [paper]
"The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot" [2024-10] [paper]
"Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning" [2024-10] [paper]
"One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks" [2024-10] [paper]
"UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models" [2024-10] [paper]
"How much does AI impact development speed? An enterprise-based randomized controlled trial" [2024-10] [paper]
"Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing" [2024-10] [paper]
"Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace" [2024-10] [paper]
"LLMs are Imperfect, Then What? An Empirical Study on LLM Failures in Software Engineering" [2024-11] [paper]
"Human-In-the-Loop Software Development Agents" [2024-11] [paper]
CodeSearchNet : "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [2019-09] [paper] [repo] [data]
The Pile : "The Pile: An 800GB Dataset of Diverse Text for Language Modeling" [2020-12], [paper] [data]
CodeParrot , 2022-02, [data]
The Stack : "The Stack: 3 TB of permissively licensed source code" [2022-11] [paper] [data]
ROOTS : "The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset" [2023-03] [NeurIPS 2022 Datasets and Benchmarks Track] [paper] [data]
The Stack v2 : "StarCoder 2 and The Stack v2: The Next Generation" [2024-02] [paper] [data]
CodeXGLUE : "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [2021-02] [NeurIPS Datasets and Benchmarks 2021] [paper] [repo] [data]
CodefuseEval : "CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model" [2023-10] [paper] [repo]
CodeScope : "CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation" [2023-11] [ACL 2024] [paper] [repo]
CodeEditorBench : "CodeEditorBench: Evaluating Code Editing Capability of Large Language Models" [2024-04] [paper] [repo]
Long Code Arena : "Long Code Arena: a Set of Benchmarks for Long-Context Code Models" [2024-06] [paper] [repo]
CodeRAG-Bench : "CodeRAG-Bench: Can Retrieval Augment Code Generation?" [2024-06] [paper] [repo]
LiveBench : "LiveBench: A Challenging, Contamination-Free LLM Benchmark" [2024-06] [paper] [repo]
DebugEval : "Enhancing the Code Debugging Ability of LLMs via Communicative Agent Based Data Refinement" [2024-08] [paper] [repo]
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2018-02 | LREC 2018 | NL2Bash | 9305 | Bash | "NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System" [paper] [data] |
| 2018-08 | EMNLP 2018 | CONCODE | 104K | Ява | "Mapping Language to Code in Programmatic Context" [paper] [data] |
| 2019-10 | EMNLP-IJCNLP 2019 | Сок | 1.5M/3725 * | Питон | "JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation" [paper] [data] |
| 2021-05 | NeurIPS 2021 | ПРИЛОЖЕНИЯ | 10000 | Питон | "Measuring Coding Challenge Competence With APPS" [paper] [data] |
| 2021-07 | arXiv | HumanEval | 164 | Питон | "Evaluating Large Language Models Trained on Code" [paper] [data] |
| 2021-08 | arXiv | MBPP/MathQA-Python | 974/23914 | Питон | "Program Synthesis with Large Language Models" [paper] [MBPP] [MathQA-Python] |
| 2021-08 | ACL/IJCNLP 2021 | PlotCoder | 40797 | Питон | "PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context" [paper] [data] |
| 2022-01 | arXiv | DSP | 1119 | Питон | "Training and Evaluating a Jupyter Notebook Data Science Assistant" [paper] [data] |
| 2022-02 | Наука | CodeContests | 13610 | C++, Python, Java | "Competition-Level Code Generation with AlphaCode" [paper] [data] |
| 2022-03 | EACL 2023 Findings | MCoNaLa | 896 | Питон | "MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages" [paper] [data] |
| 2022-06 | arXiv | AixBench | 336 | Ява | "AixBench: A Code Generation Benchmark Dataset" [paper] [data] |
| 2022-08 | IEEE Trans. Программное обеспечение | Несколько | "MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation", [paper] [data] | ||
| 2022-10 | ICLR 2023 | MBXP | 12.4K | Python, Java, JS, TypeScript, Go, C#, PHP, Ruby, Kotlin, C++, Perl, Scala, Swift | "Multi-lingual Evaluation of Code Generation Models" [paper] [data] |
| 2022-10 | ICLR 2023 | Multilingual HumanEval | 1.9K | Python, Java, JS, TypeScript, Go, C#, PHP, Ruby, Kotlin, Perl, Scala, Swift | "Multi-lingual Evaluation of Code Generation Models" [paper] [data] |
| 2022-10 | ICLR 2023 | MathQA-X | 5.6K | Python, Java, JS | "Multi-lingual Evaluation of Code Generation Models" [paper] [data] |
| 2022-11 | arXiv | ExeDS | 534 | Питон | "Execution-based Evaluation for Data Science Code Generation Models" [paper] [data] |
| 2022-11 | arXiv | DS-1000 | 1000 | Питон | "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation" [paper] [data] |
| 2022-12 | arXiv | ODEX | 945 | Питон | "Execution-Based Evaluation for Open-Domain Code Generation" [paper] [data] |
| 2023-02 | arXiv | CoderEval | 460 | Python, Java | "CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models" [paper] [data] |
| 2023-03 | ACL 2024 | xCodeEval | 5.5M | C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust | "XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [paper] [data] |
| 2023-03 | arXiv | HumanEval-X | 820 | Python, C++, Java, JS, Go | "CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [paper] [data] |
| 2023-05 | arXiv | HumanEval+ | 164 | Питон | "Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation" [paper] [data] |
| 2023-06 | ACL 2024 Findings | StudentEval | 1749 | Питон | "StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code" [paper] [data] |
| 2023-08 | ICLR 2024 Spotlight | HumanEvalPack | 984 | Python, JS, Go, Java, C++, Rust | "OctoPack: Instruction Tuning Code Large Language Models" [paper] [data] |
| 2023-06 | NeurIPS 2023 | DotPrompts | 10538 | Ява | "Guiding Language Models of Code with Global Context using Monitors" [paper] [data] |
| 2023-09 | arXiv | CodeApex | 476 | C ++ | "CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models" [paper] [data] |
| 2023-09 | arXiv | VerilogEval | 8645/156 | Verilog | "VerilogEval: Evaluating Large Language Models for Verilog Code Generation" [paper] [data] |
| 2023-11 | arXiv | ML-Bench | 10040 | Bash | "ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks" [paper] [data] |
| 2023-12 | arXiv | TACO | 26,433 | Питон | "TACO: Topics in Algorithmic COde generation dataset" [paper] [data] |
| 2024-01 | HPDC | ParEval | 420 | C++, CUDA, HIP | "Can Large Language Models Write Parallel Code?" [paper] [data] |
| 2024-02 | ACL 2024 Findings | OOP | 431 | Питон | "OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models" [paper] [data] |
| 2024-02 | LREC-COLING 2024 | HumanEval-XL | 22080 | 23NL, 12PL | "HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization" [paper] [data] |
| 2024-04 | arXiv | USACO | 307 | Питон | "Can Language Models Solve Olympiad Programming?" [paper] [data] |
| 2024-04 | LREC-COLING 2024 | PECC | 2396 | Питон | "PECC: Problem Extraction and Coding Challenges" [paper] [data] |
| 2024-04 | arXiv | CodeGuard+ | 23 | Python, C | "Constrained Decoding for Secure Code Generation" [paper] [data] |
| 2024-05 | ACL 2024 Findings | NaturalCodeBench | 402 | Python, Java | "NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts" [paper] [data] |
| 2024-05 | arXiv | MHPP | 140 | Питон | "MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation" [paper] [repo] |
| 2024-06 | arXiv | VHDL-Eval | 202 | VHDL | "VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation" [paper] |
| 2024-06 | arXiv | AICoderEval | 492 | Питон | "AICoderEval: Improving AI Domain Code Generation of Large Language Models" [paper] [data] |
| 2024-06 | arXiv | VersiCode | 98,692 | Питон | "VersiCode: Towards Version-controllable Code Generation" [paper] [data] |
| 2024-06 | IEEE AITest 2024 | ScenEval | 12,864 | Ява | "ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation" [paper] |
| 2024-06 | arXiv | BigCodeBench | 1,140 | Питон | "BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions" [paper] [data] |
| 2024-07 | arXiv | CodeUpdateArena | 670 | Питон | "CodeUpdateArena: Benchmarking Knowledge Editing on API Updates" [paper] [data] |
| 2024-07 | arXiv | LBPP | 161 | Питон | "On Leakage of Code Generation Evaluation Datasets" [paper] [data] |
| 2024-07 | arXiv | NoviCode | 150 | Питон | "NoviCode: Generating Programs from Natural Language Utterances by Novices" [paper] [data] |
| 2024-07 | arXiv | Case2Code | 1.3M | Питон | "Case2Code: Learning Inductive Reasoning with Synthetic Data" [paper] [data] |
| 2024-07 | arXiv | SciCode | 338 | Питон | "SciCode: A Research Coding Benchmark Curated by Scientists" [paper] [data] |
| 2024-07 | arXiv | auto-regression | 460 | Питон | "Generating Unseen Code Tests In Infinitum" [paper] |
| 2024-07 | arXiv | WebApp1K | 1000 | JavaScript | "WebApp1K: A Practical Code-Generation Benchmark for Web App Development" [paper] [data] |
| 2024-08 | ACL 2024 Findings | CodeInsight | 3409 | Питон | "CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow" [paper] [data] |
| 2024-08 | arXiv | DomainEval | 2454 | Питон | "DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation" [paper] [data] |
| 2024-09 | arXiv | ComplexCodeEval | 7184/3897 | Python/Java | "ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code" [paper] [data] |
| 2024-09 | ASE 2024 | CoCoNote | 58221 | Python Notebook | "Contextualized Data-Wrangling Code Generation in Computational Notebooks" [paper] [data] |
| 2024-10 | arXiv | unnamed | 77 | Питон | "Evaluation of Code LLMs on Geospatial Code Generation" [paper] [data] |
| 2024-10 | arXiv | mHumanEval | 836,400 | 25PL, 204NL | "mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation" [paper] [data] |
| 2024-10 | arXiv | FeatEng | 103 | Питон | "Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists" [paper] [data] |
| 2024-11 | arXiv | GitChameleon | 116 | Питон | "GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models" [paper] [data] |
* Automatically mined/human-annotated
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2024-04 | arXiv | MMCode | 3548 | Питон | "MMCode: Evaluating Multi-Modal Code Large Language Models with Visually Rich Programming Problems" [paper] [data] |
| 2024-05 | arXiv | Plot2Code | 132 | Питон | "Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots" [paper] [data] |
| 2024-06 | arXiv | ChartMimic | 1000 | Питон | "ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation" [paper] [data] |
| 2024-10 | arXiv | HumanEval-V | 108 | Питон | "HumanEval-V: Evaluating Visual Understanding and Reasoning Abilities of Large Multimodal Models Through Coding Tasks" [paper] [data] |
| 2024-10 | arXiv | TurtleBench | 260 | Питон | "TurtleBench: A Visual Programming Benchmark in Turtle Geometry" [paper] [data] |
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2021-09 | EMNLP 2021 Findings | CodeQA | 120K/70K | Java/Python | "CodeQA: A Question Answering Dataset for Source Code Comprehension" [paper] [data] |
| 2022-10 | NAACL 2022 | CS1QA | 9237 | Питон | "CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course" [paper] [data] |
| 2023-09 | arXiv | CodeApex | 250 | C ++ | "CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models" [paper] [data] |
| 2024-01 | ICML 2024 | CRUXEval | 800 | Питон | "CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution" [paper] [data] |
| 2024-05 | arXiv | PythonIO | 2650 | Питон | "Multiple-Choice Questions are Efficient and Robust LLM Evaluators" [paper] [data] |
| 2024-05 | arXiv | StaCCQA | 270K | Питон | "Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering" [paper] [data] |
| 2024-06 | arXiv | RepoQA | 500 | Python, C++, Java, Rust, TypeScript | "RepoQA: Evaluating Long Context Code Understanding" [paper] [data] |
| 2024-08 | arXiv | CruxEval-X | 12.6K | 19 | "CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution" [paper] [data] |
| 2024-09 | arXiv | SpecEval | 204 | Ява | "SpecEval: Evaluating Code Comprehension in Large Language Models via Program Specifications" [paper] [data] |
| 2024-10 | arXiv | CodeMMLU | 19912 | 13 | "CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities of CodeLLMs" [paper] [data] |
| 2024-11 | arXiv | unnamed | 80232 | Питон | "Leveraging Large Language Models in Code Question Answering: Baselines and Issues" [paper] [data] |
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2017-08 | arXiv | WikiSQL | 80654 | "Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning" [paper] [data] | |
| 2018-06 | CL 2018 | Advising | 4570 | "Improving Text-to-SQL Evaluation Methodology" [paper] [data] | |
| 2018-09 | EMNLP 2018 | Паук | 10181 | "Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task" [paper] [data] | |
| 2019-06 | ACL 2019 | SParC | 12726 | "SParC: Cross-Domain Semantic Parsing in Context" [paper] [data] | |
| 2019-07 | WWW 2020 | MIMICSQL | 10000 | "Text-to-SQL Generation for Question Answering on Electronic Medical Records" [paper] [data] | |
| 2019-09 | EMNLP-IJCNLP 2019 | CoSQL | 15598 | "CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases" [paper] [data] | |
| 2020-05 | LREC 2020 | Criteria-to-SQL | 2003 | "Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing" [paper] [data] | |
| 2020-10 | EMNLP 2020 Findings | Шквал | 11276 | "On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries" [paper] [data] | |
| 2020-10 | NAACL-HLT 2021 | Spider-Realistic | 508 | "Structure-Grounded Pretraining for Text-to-SQL" [paper] [data] | |
| 2021-06 | ACL/IJCNLP 2021 | Spider-Syn | 8034 | "Towards Robustness of Text-to-SQL Models against Synonym Substitution" [paper] [data] | |
| 2021-06 | NLP4Prog 2021 | SEDE | 12023 | "Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data" [paper] [data] | |
| 2021-06 | ACL/IJCNLP 2021 | KaggleDBQA | 400 | "KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers" [paper] [data] | |
| 2021-09 | EMNLP | Spider-DK | 535 | "Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization" [paper] [data] | |
| 2022-05 | NAACL 2022 Findings | Spider-SS/CG | 8034/45599 | "Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment" [paper] [data] | |
| 2023-05 | arXiv | ПТИЦА | 12751 | "Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs" [paper] [data] | |
| 2023-06 | ACL 2023 | XSemPLR | 24.4K | "XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations" [paper] [data] | |
| 2024-05 | ACL 2024 Findings | EHR-SeqSQL | 31669 | "EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records" [paper] | |
| 2024-06 | NAACL 2024 | BookSQL | 100K | "BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain" [paper] [data] | |
| 2024-08 | ACL 2024 Findings | MultiSQL | 9257 | "MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations" [paper] [data] | |
| 2024-09 | arXiv | BEAVER | 93 | "BEAVER: An Enterprise Benchmark for Text-to-SQL" [paper] | |
| 2024-10 | arXiv | PRACTIQ | 2812 | "PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries" [paper] | |
| 2024-10 | arXiv | БИС | 239 | "BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios" [paper] [data] | |
| 2024-11 | arXiv | Spider 2.0 | 632 | "Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows" [paper] [data] |
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2020-06 | NeurIPS 2020 | Transcoder GeeksforGeeks | 1,4k | C++, Java, Python | "Unsupervised Translation of Programming Languages" [paper] [data] |
| 2021-02 | NeurIPS Datasets and Benchmarks 2021 | CodeTrans | 11,8K | Java, C# | "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [paper] [data] |
| 2021-08 | ACL 2023 Findings | Аватар | 9515 | Java, Python | "AVATAR: A Parallel Corpus for Java-Python Program Translation" [paper] [data] |
| 2022-06 | AAAI 2022 | Расходы | 132K | C++, Java, Python, C#, JS, PHP, C | "Multilingual Code Snippets Training for Program Translation" [paper] [data] |
| 2022-06 | arXiv | XLCoST | 567K | C++, Java, Python, C#, JS, PHP, C | "XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence" [paper] [data] |
| 2023-03 | arXiv | xCodeEval | 5.6M | C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust | "xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [paper] [data] |
| 2023-03 | arXiv | HumanEval-X | 1640 | Python, C++, Java, JS, Go | "CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [paper] [data] |
| 2023-08 | arXiv | G-TransEval | 4000 | C++, Java, C#, JS, Python | "On the Evaluation of Neural Code Translation: Taxonomy and Benchmark" [paper] [data] |
| 2023-10 | arXiv | CodeTransOcean | 270.5K | 45 | "CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation" [paper] [data] |
| 2024-11 | arXiv | Classeval-T | 94 | Python, Java, C++ | "Escalating LLM-based Code Translation Benchmarking into the Class-level Era" [paper] |
| 2024-11 | arXiv | RustRepoTrans | 375 | C++, Java, Python, Rust | "Repository-level Code Translation Benchmark Targeting Rust" [paper] [data] |
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2014-07 | ISSTA 2014 | Defects4J | 357 | Ява | "Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs" [paper] [data] |
| 2015-12 | IEEE Trans. Программное обеспечение | ManyBugs/IntroClass | 185/998 | В | "The ManyBugs and IntroClass Benchmarks for Automated Repair of C Programs" [paper] [data] |
| 2016-11 | FSE 2016 | BugAID | 105K | Младший | "Discovering Bug Patterns in JavaScript" [paper] [data] |
| 2017-02 | AAAI 2017 | DeepFix | 6971 | В | "DeepFix: Fixing Common C Language Errors by Deep Learning" [paper] [data] |
| 2017-05 | ICSE-C 2017 | Codeflaws | 3902 | В | "DeepFix: Fixing Common C Language Errors by Deep Learning" [paper] [data] |
| 2017-10 | SPLASH 2017 | QuixBugs | 80 | Java, Python | "QuixBugs: a multi-lingual program repair benchmark set based on the quixey challenge" [paper] [data] |
| 2018-05 | MSR 2018 | Bugs.jar | 1158 | Ява | "Bugs.jar: a large-scale, diverse dataset of real-world Java bugs" [paper] [data] |
| 2018-12 | ACM Trans. Softw. Eng. Methodol. | BFP | 124K | Ява | "An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation" [paper] [data] |
| 2019-01 | SANER 2019 | Bears | 251 | Ява | "Bears: An Extensible Java Bug Benchmark for Automatic Program Repair Studies" [paper] [data] |
| 2019-01 | ICSE 2019 | unnamed | 21.8K * | Ява | "On Learning Meaningful Code Changes via Neural Machine Translation" [paper] [data] |
| 2019-04 | ICST 2019 | BugsJS | 453 | Младший | "BugsJS: a Benchmark of JavaScript Bugs" [paper] [data] |
| 2019-05 | ICSE 2019 | BugSwarm | 1827/1264 | Java/Python | "BugSwarm: mining and continuously growing a dataset of reproducible failures and fixes" [paper] [data] |
| 2019-05 | ICSE 2019 | CPatMiner | 17K * | Ява | "Graph-based mining of in-the-wild, fine-grained, semantic code change patterns" [paper] [data] |
| 2019-05 | MSR 2020 | ManySStuBs4J | 154K | Ява | "How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset" [paper] [data] |
| 2019-11 | ASE 2019 | Refactory | 1783 | Питон | "Re-factoring based program repair applied to programming assignments" [paper] [data] |
| 2020-07 | ISSTA 2020 | Кокос | 24M | Java, Python, C, JS | "CoCoNuT: combining context-aware neural translation models using ensemble for program repair" [paper] [data] |
| 2020-10 | Inf. Softw. Technol. | Review4Repair | 58021 | Ява | "Review4Repair: Code Review Aided Automatic Program Repairing" [paper] [data] |
| 2020-11 | ESEC/FSE 2020 | BugsInPy | 493 | Питон | "BugsInPy: A Database of Existing Bugs in Python Programs to Enable Controlled Testing and Debugging Studies" [paper] [data] |
| 2021-07 | ICML 2021 | TFix | 105K | Младший | "TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer" [paper] [data] |
| 2021-08 | arXiv | Megadiff | 663K * | Ява | "Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size" [paper] [data] |
| 2022-01 | SSB/TSSB | MSR 2022 | 9M/3M | Питон | "TSSB-3M: Mining single statement bugs at massive scale" [paper] [data] |
| 2022-10 | MSR 2022 | FixJS | 324K | Младший | "FixJS: a dataset of bug-fixing JavaScript commits" [paper] [data] |
| 2022-11 | ESEC/FSE 2022 | TypeBugs | 93 | Питон | "PyTER: Effective Program Repair for Python Type Errors" [paper] [data] |
| 2023-03 | arXiv | xCodeEval | 4.7M | C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust | "xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [paper] [data] |
| 2023-04 | arXiv | RunBugRun | 450K | C, C++, Java, Python, JS, Ruby, Go, PHP | "RunBugRun -- An Executable Dataset for Automated Program Repair" [paper] [data] |
| 2023-08 | arXiv | HumanEvalPack | 984 | Python, JS, Go, Java, C++, Rust | "OctoPack: Instruction Tuning Code Large Language Models" [paper] [data] |
| 2024-01 | arXiv | DebugBench | 4253 | C++, Java, Python | "DebugBench: Evaluating Debugging Capability of Large Language Models" [paper] [data] |
| 2024-11 | arXiv | MdEval | 3513 | 18 | "MdEval: Massively Multilingual Code Debugging" [paper] |
* These are code-change datasest, and only a subset therein concerns bug fixing.
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2016-08 | ACL 2016 | CODE-NN | 66K/32K | C#/SQL | "Summarizing Source Code using a Neural Attention Model" [paper] [data] |
| 2017-07 | IJCNLP 2017 | unnamed | 150K | Питон | "A parallel corpus of Python functions and documentation strings for automated code documentation and code generation" [paper] [data] |
| 2018-05 | ICPC 2018 | DeepCom | 588K | Ява | "Deep code comment generation" [paper] [data] |
| 2018-07 | IJCAI 2018 | TL-CodeSum | 411K | Ява | "Summarizing Source Code with Transferred API Knowledge" [paper] [data] |
| 2018-11 | ASE 2018 | unnamed | 109K | Питон | "Improving Automatic Source Code Summarization via Deep Reinforcement Learning" [paper] [data] |
| 2019-09 | arxiv | CodeSearchNet | 2.3M | Go, JS, Python, PHP, Java, Ruby | "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [paper] [data] |
| 2023-08 | arXiv | HumanEvalPack | 984 | Python, JS, Go, Java, C++, Rust | "OctoPack: Instruction Tuning Code Large Language Models" [paper] [data] |
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2018-01 | NDSS 2018 | CGD | 62к | C, C++ | "VulDeePecker: A Deep Learning-Based System for Vulnerability Detection" [paper] [data] |
| 2018-04 | IEEE Trans. Ind. Informatics | unnamed | 32988 | C, C++ | "Cross-Project Transfer Representation Learning for Vulnerable Function Discovery" [paper] [data] |
| 2018-07 | ICMLA 2018 | Draper VDISC | 12.8M | C, C++ | "Automated Vulnerability Detection in Source Code Using Deep Representation Learning" [paper] [data] |
| 2018-07 | IEEE TDSC | SySeVR | 15591 | C, C++ | "SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities" [paper] [data] |
| 2019-02 | MSR 2019 | unnamed | 624 | Ява | "A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software" [paper] [data] |
| 2019-09 | NeurIPS 2019 | Devign | 49K | В | "Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks" [paper] [data] |
| 2019-11 | IEEE TDSC | unnamed | 170K | C, C++ | "Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases" [paper] [data] |
| 2019-12 | ICLR 2020 | БОЛЬШОЙ | 2.8M | Питон | "Global Relational Models of Source Code" [paper] [data] |
| 2020-01 | IEEE TDSC | MVD | 182K | C, C++ | "μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection" [paper] [data] |
| 2020-02 | ICICS 2019 | unnamed | 1471 | В | "Deep Learning-Based Vulnerable Function Detection: A Benchmark" [paper] [data] |
| 2020-09 | IEEE Trans. Software Eng. | Раскрывать | 18K | В | "Deep Learning based Vulnerability Detection: Are We There Yet?" [paper] [data] |
| 2020-09 | MSR 2020 | Big-Vul | 265K | C, C++ | "AC/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries" [paper] [data] |
| 2021-02 | ICSE (SEIP) 2021 | D2A | 1.3M | C, C++ | "D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis" [paper] [data] |
| 2021-05 | NeurIPS 2021 | PyPIBugs | 2374 | Питон | "Self-Supervised Bug Detection and Repair" [paper] [data] |
| 2021-07 | In PROMISE 2021 | CVEfixes | 5495 | 27 | "CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software" [paper] [data] |
| 2021-08 | ESEC/FSE 2021 | CrossVul | 27476 | 40+ | "CrossVul: a cross-language vulnerability dataset with commit data" [paper] [data] |
| 2023-04 | RAID 2023 | DiverseVul | 349K | C, C++ | "DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection" [paper] [data] |
| 2023-06 | arXiv | VulnPatchPairs | 26K | В | "Limits of Machine Learning for Automatic Vulnerability Detection" [paper] [data] |
| 2023-11 | arXiv | VulBench | 455 | В | "How Far Have We Gone in Vulnerability Detection Using Large Language Models" [paper] [data] |
| 2024-03 | arXiv | PrimeVul | 236K | C/C++ | "Vulnerability Detection with Code Language Models: How Far Are We?" [бумага] |
| 2024-06 | arXiv | VulDetectBench | 1000 | C/C++ | "VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models" [paper] [data] |
| 2024-08 | arXiv | CodeJudge-Eval | 1860 | Питон | "CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?" [paper] [data] |
| 2024-11 | arXiv | CleanVul | 11632 | Java, Python, JS, C#, C/C++ | "CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics" [paper] [data] |
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2018-03 | WWW 2018 | StaQC | 148K/120K | Python/SQL | "StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow" [paper] [data] |
| 2018-05 | ICSE 2018 | DeepCS | 16.2M | Ява | "Deep Code Search" [paper] [data] |
| 2018-05 | MSR 2018 | CoNaLa | 600K/2.9K | Питон | "Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow" [paper] [data] |
| 2019-08 | arXiv | unnamed | 287 | Ява | "Neural Code Search Evaluation Dataset" [paper] [data] |
| 2019-09 | arXiv | CodeSearchNet | 2.3M/99 | Go, PHP, JS, Python, Java, Ruby | "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [paper] [data] |
| 2020-02 | SANER 2020 | CosBench | 52 | Ява | "Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries" [paper] [data] |
| 2020-08 | arXiv | SO-DS | 2.2K | Питон | "Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent" [paper] [data] |
| 2020-10 | ACM Trans. Knowl. Discov. Данные | FB-Java | 249K | Ява | "Deep Graph Matching and Searching for Semantic Code Retrieval" [paper] [data] |
| 2021-02 | NeurIPS Datasets and Benchmarks 2021 | AdvTest/WebQueryTest | 280K/1K | Питон | "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [paper] [[data]] |
| 2021-05 | ACL/IJCNLP 2021 | CoSQA | 21K | Питон | "CoSQA: 20,000+ Web Queries for Code Search and Question Answering" [paper] [data] |
| 2024-03 | arXiv | ProCQA | 5.2M | C, C++, Java, Python, Ruby, Lisp, JS, C#, Go, Rust, PHP | "ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search" [paper] [data] |
| 2024-06 | arXiv | CoSQA+ | 109K | Питон | "CoSQA+: Enhancing Code Search Dataset with Matching Code" [paper] [data] |
| 2024-07 | arXiv | CoIR | ~2M | 14 | "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models" [paper] [data] |
| 2024-08 | arXiv | SeqCoBench | 14.5K | Питон | "What can Large Language Models Capture about Code Functional Equivalence?" [бумага] |
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2019-12 | ESEC/FSE 2020 | TypeWriter OSS | 208K | Питон | "TypeWriter: Neural Type Prediction with Search-based Validation" [paper] [data] |
| 2020-04 | PLDI 2020 | Typilus | 252K | Питон | "Typilus: Neural Type Hints" [paper] [data] |
| 2020-04 | ICLR 2020 | LambdaNet | 300 * | Машинопись | "LambdaNet: Probabilistic Type Inference using Graph Neural Networks" [paper] [data] |
| 2021-04 | MSR 2021 | ManyTypes4Py | 869K | Питон | "ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference" [paper] [data] |
| 2022-10 | MSR 2022 | ManyTypes4TypeScript | 9.1M | Машинопись | "ManyTypes4TypeScript: a comprehensive TypeScript dataset for sequence-based type inference" [paper] [data] |
| 2023-02 | ECOOP 2023 | TypeWeaver | 513 * | Машинопись | "Do Machine Learning Models Produce TypeScript Types That Type Check?" [paper] [data] |
| 2023-03 | ICLR 2023 | BetterTypes4Py/InferTypes4Py | 608K/4.6K | Питон | "TypeT5: Seq2seq Type Inference using Static Analysis" [paper] [data] |
| 2023-05 | arXiv | OpenTau | 744 * | Машинопись | "Type Prediction With Program Decomposition and Fill-in-the-Type Training" [paper] [data] |
* These are project counts.
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2017-03 | ICPC 2017 | unnamed | 509K | Ява | "Towards Automatic Generation of Short Summaries of Commits" [paper] [data] |
| 2017-04 | ACL 2017 | CommitGen | 153K | Python, JS, C++, Java | "A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes" [paper] [data] |
| 2017-08 | ASE 2017 | CommitGen | 32K/75K * | Ява | "Automatically Generating Commit Messages from Diffs using Neural Machine Translation" [paper] [data] |
| 2018-09 | ASE 2018 | NNGen | 27K | Ява | "Neural-machine-translation-based commit message generation: how far are we?" [paper] [data] |
| 2019-05 | MSR 2019 | PtrGNCMsg | 64.9K | Ява | "Generating commit messages from diffs using pointer-generator network" [paper] [[data(https://zenodo.org/records/2593787)]] |
| 2019-08 | IJCAI 2019 | CoDiSum | 90.7K | Ява | "Commit message generation for source code changes" [paper] [data] |
| 2019-12 | IEEE Trans. Software Eng. | АТОМ | 160K | Ява | "ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking" [paper] [data] |
| 2021-05 | arXiv | CommitBERT | 346K | Python, PHP, Go, Java, JS, Ruby | "CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model" [paper] [data] |
| 2021-07 | ICSME 2021 | MCMD | 2,25 м | Java, C#, C++, Python, JS | "On the Evaluation of Commit Message Generation Models: An Experimental Study" [paper] [data] |
| 2021-07 | ACM Trans. Softw. Eng. Methodol. | CoRec | 107K | Ява | "Context-aware Retrieval-based Deep Commit Message Generation" [paper] [data] |
| 2023-07 | ASE 2023 | ExGroFi | 19263 | Ява | "Delving into Commit-Issue Correlation to Enhance Commit Message Generation Models" [paper] [data] |
| 2023-08 | ASE 2023 | CommitChronicle | 10.7M | 20 | "From Commit Message Generation to History-Aware Commit Message Completion" [paper] [data] |
* with/without verb-direct object filter
| Дата | Место проведения | Эталон | Размер | Язык | Источник |
|---|---|---|---|---|---|
| 2023-03 | arXiv | RepoEval | 1600/1600/373 * | Питон | "RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation" [paper] [data] |
| 2023-06 | ICLR 2024 | RepoBench | 890K/9M/43K | Python, Java | "RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems" [paper] [data] |
| 2023-06 | NeurIPS 2023 | PragmaticCode | 880 ** | Ява | "Guiding Language Models of Code with Global Context using Monitors" [paper] [data] |
| 2023-06 | arXiv | Stack-Repo | 816K | Ява | "RepoFusion: Training Code Models to Understand Your Repository" [paper] [data] |
| 2023-09 | ISMB 2024 | BioCoder | 2269/460/460 | Python, Java | "BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models" [paper] [data] |
| 2023-09 | arXiv | CodePlan | 645/21 | C#/Python | "CodePlan: Repository-level Coding using LLMs and Planning" [paper] [data] |
| 2023-10 | arXiv | SWE-Bench | 2294 | Питон | "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" [paper] [data] |
| 2023-10 | arXiv | CrossCodeEval | 9928 | Python, Java, TypeScript, C# | "CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion" [paper] [data] |
| 2024-03 | arXiv | EvoCodeBench | 275 | Питон | "EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories" [paper] [data] |
| 2024-05 | ACL 2024 Findings | DevEval | 1874 | Питон | "DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories" [paper] [data] |
| 2024-06 | arXiv | JavaBench | 389 | Ява | "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" [paper] [data] |
| 2024-06 | arXiv | HumanEvo | 200/200 | Python/Java | "Towards more realistic evaluation of LLM-based code generation: an experimental study and beyond" [paper] [data] |
| 2024-06 | arXiv | RepoExec | 355 | Питон | "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" [paper] |
| 2024-06 | arXiv | RES-Q | 100 | Python, JavaScript | "RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale" [paper] [data] |
| 2024-08 | arXiv | SWE-bench-java | 91 | Ява | "SWE-bench-java: A GitHub Issue Resolving Benchmark for Java" [paper] [data] |
| 2024-10 | arXiv | Codev-Bench | 296 | Питон | "Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?" [paper] [data] |
| 2024-10 | arXiv | SWE-bench M | 617 | JavaScript | "SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?" [paper] [data] |
| 2024-10 | arXiv | SWE-Bench+ | 548 | Питон | "SWE-Bench+: Enhanced Coding Benchmark for LLMs" [paper] [data] |
| 2024-10 | arXiv | DA-Code | 500 | Python, Bash, SQL | "DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models" [paper] [data] |
| 2024-10 | arXiv | RepoCod | 980 | Питон | "Can Language Models Replace Programmers? REPOCOD Says 'Not Yet'" [paper] |
| 2024-10 | arXiv | M2rc-Eval | 5993 repos | 18 | "M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation" [paper] [data] |
*Line Completion/API Invocation Completion/Function Completion
** File count
30 papers as a primer on LLM.
| Дата | Ключевое слово | Бумага | TL;DR |
|---|---|---|---|
| 2014-09 | Внимание | Neural Machine Translation by Jointly Learning to Align and Translate | The original attention, proposed for encoder-decoder RNN |
| 2015-08 | BPE | Neural Machine Translation of Rare Words with Subword Units | Byte-pair encoding: split rare words into subword units |
| 2017-06 | Transformer | Attention Is All You Need | Replace LSTM with self-attention for long-range dependency and parallel training |
| 2017-10 | Mixed Precision Training | Mixed Precision Training | Store model weights in fp16 to save memory |
| 2018-04 | КЛЕЙ | GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding | A language understanding benchmark |
| 2018-06 | Гф | Improving Language Understanding by Generative Pre-Training | Pretraining-finetuning paradigm applied to Transformer decoder |
| 2018-10 | БЕРТ | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | Masked Language Modeling (MLM) applied to Transformer encoder for pretraining |
| 2019-02 | GPT-2 | Language Models are Unsupervised Multitask Learners | GPT made larger (1.5B). They found language models implicitly learn about downstream tasks (such as translation) during pretraining. |
| 2019-05 | SuperGLUE | SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems | Another language understanding benchmark |
| 2019-07 | RoBERTa | RoBERTa: A Robustly Optimized BERT Pretraining Approach | An optimized BERT |
| 2019-09 | Megatron-LM | Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism | Model parallelism |
| 2019-10 | ZeRO | ZeRO: Memory Optimizations Toward Training Trillion Parameter Models | Memory-efficient distributed optimization |
| 2019-10 | T5 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | Transformer encoder-decoder pretrained with an MLM-like denoising objective |
| 2020-05 | GPT-3 | Language Models are Few-Shot Learners | By training an even larger version of GPT-2 (175B), they discovered a new learning paradigm: In-Context Learning (ICL) |
| 2020-09 | MMLU | Measuring Massive Multitask Language Understanding | A world-knowledge and complex reasoning benchmark |
| 2020-12 | Куча | The Pile: An 800GB Dataset of Diverse Text for Language Modeling | A diverse pretraining dataset |
| 2021-06 | Лора | Лора: адаптация с низким уровнем моделей крупных языков | Memory-efficient finetuning |
| 2021-09 | FLAN | Finetuned Language Models Are Zero-Shot Learners | Instruction-finetuning |
| 2021-10 | T0 | Multitask Prompted Training Enables Zero-Shot Task Generalization | Also instruction finetuning, but applied to the much smaller T5 |
| 2021-12 | Gopher | Scaling Language Models: Methods, Analysis & Insights from Training Gopher | A 280B LLM with comprehensive experiments |
| 2022-01 | CoT | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models | Chain-of-Though reasoning |
| 2022-03 | InstructGPT | Training language models to follow instructions with human feedback | GPT-3 instruction finetuned with RLHF (reinforcement learning from human feedback) |
| 2022-03 | Chinchilla | Training Compute-Optimal Large Language Models | A smaller (70B) version of Gopher that's pretrained on more data |
| 2022-04 | Ладонь | PaLM: Scaling Language Modeling with Pathways | The largest dense model ever (540B) |
| 2022-05 | 0-shot CoT | Large Language Models are Zero-Shot Reasoners | Tell LLMs to think step by step, and they can actually do it |
| 2022-06 | BIG Bench | Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models | Another world-knowledge and complex reasoning benchmark |
| 2022-06 | Emergent Ability | Emergent Abilities of Large Language Models | A review on emergent abilities |
| 2022-10 | Flan | Scaling Instruction-Finetuned Language Models | Consolidate all the existing instruction tuning datasets, and you get SOTA |
| 2022-11 | ЦВЕСТИ | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model | The largest open-source LLM, trained on 46 languages, with detailed discussion about training and evaluation |
| 2022-12 | Self-Instruct | Self-Instruct: Aligning Language Models with Self-Generated Instructions | Instruction tuning using LLM-generated data |
This list aims to provide the essential background for understanding current LLM technologies, and thus excludes more recent models such as LLaMA, GPT-4 or PaLM 2. For comprehensive reviews on these more general topics, we refer to other sources such as this paper or these repositories: Awesome-LLM, Awesome AIGC Tutorials. And for LLM applications in other specific domains: Awesome Domain LLM, Awesome Tool Learning, Awesome-LLM-MT, Awesome Education LLM.
If you find this repo or our survey helpful, please consider citing us:
@article{zhang2024unifying,
title={Unifying the Perspectives of {NLP} and Software Engineering: A Survey on Language Models for Code},
author={Ziyin Zhang and Chaoyu Chen and Bingchang Liu and Cong Liao and Zi Gong and Hang Yu and Jianguo Li and Rui Wang},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=hkNnGqZnpa},
note={}
}
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