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The ability to execute the test suite of a project is essential in many scenarios, e.g., to assess code quality and code coverage, to validate code changes made by developers or automated tools, and to ensure compatibility with dependencies.
Defects4J: a database of existing faults to enable controlled testing studies for Java programs. In ISSTA . 437–440
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Bugswarm: Mining and continuously growing a dataset of reproducible failures and fixes. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 339–349
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Unsupervised Translation of Programming Languages. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)
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Evaluating Large Language Models Trained on Code
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CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang. 2022 · 2022
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DynaPyt: A Dynamic Analysis Framework for Python. In ESEC/FSE ’22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . ACM
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PAL: Program-aided Language Models
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Chain-of-thought prompting elicits reasoning in large language models
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CodePlan: Repository-level Coding using LLMs and Planning
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FlaPy: Mining Flaky Python Tests at Scale. In 45th IEEE/ACM International Conference on Software Engineering: ICSE 2023 Companion Proceedings, Melbourne, Australia, May 14-20, 2023 . IEEE, 127–131
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Grace: Language Models Meet Code Edits. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2023, San Francisco, CA, USA, December 3-9, 2023 , Satish Chandra, Kelly Blincoe, and Paolo Tonella (Eds.). ACM, 1483–1495
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SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2023 · 2023
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Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction. In 45th IEEE/ACM International Conference on Software Engineering, ICSE . 2312–2323
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CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models. In 45th International Conference on Software Engineering, ser. ICSE
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Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (Eds.)
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Augmented Language Models: a Survey
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Gorilla: Large Language Model Connected with Massive APIs
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An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
Prompting Is All Your Need: Automated Android Bug Replay with Large Language Models. In ICSE
Sidong Feng and Chunyang Chen. 2024 · 2024
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From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, and Huaming Chen. 2024 · 2024
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MarsCode Agent: AI-native Automated Bug Fixing
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CoverUp: Coverage-Guided LLM-Based Test Generation
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Exploring LLM-Based Agents for Root Cause Analysis. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, FSE 2024, Porto de Galinhas, Brazil, July 15-19, 2024 , Marcelo d’Amorim (Ed.). ACM, 208–219
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Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2024 · 2023
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Toolformer: Language Models Can Teach Themselves to Use Tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
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Repository-level prompt generation for large language models of code. In International Conference on Machine Learning . PMLR, 31693–31715
Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow. 2023 · 2023
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A Survey on Large Language Model based Autonomous Agents
Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Ji-Rong Wen. 2023 · 2023
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Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing
Jiayi Wei, Greg Durrett, and Isil Dillig. 2023 · 2023
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Auto-gpt for online decision making: Benchmarks and additional opinions
Hui Yang, Sifu Yue, and Yunzhong He. 2023 · 2023
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ReAct: Synergizing Reasoning and Acting in Language Models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. 2023 · 2023
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RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023
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Automated Unit Test Improvement using Large Language Models at Meta. In FSE , Vol. abs/2402.09171
Nadia Alshahwan, Jubin Chheda, Anastasia Finegenova, Beliz Gokkaya, Mark Harman, Inna Harper, Alexandru Marginean, Shubho Sengupta, and Eddy Wang. 2024 · 2024
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Devjeet Roy, Xuchao Zhang, Rashi Bhave, Chetan Bansal, Pedro Henrique B. Las-Casas, Rodrigo Fonseca, and Saravan Rajmohan. 2024 · 2024
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Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM. In FSE
Gabriel Ryan, Siddhartha Jain, Mingyue Shang, Shiqi Wang, Xiaofei Ma, Murali Krishna Ramanathan, and Baishakhi Ray. 2024 · 2024
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GitBug-Java: A Reproducible Benchmark of Recent Java Bugs. In 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR) . IEEE, 118–122
André Silva, Nuno Saavedra, and Martin Monperrus. 2024 · 2024
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MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution
Wei Tao, Yucheng Zhou, Wenqiang Zhang, and Yu Cheng. 2024 · 2024
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An In-Context Learning Agent for Formal Theorem-Proving
Amitayush Thakur, George Tsoukalas, Yeming Wen, Jimmy Xin, and Swarat Chaudhuri. 2024 · 2024
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Fuzz4All: Universal Fuzzing with Large Language Models. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024 . ACM, 126:1–126:13
Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian, Michael Pradel, and Lingming Zhang. 2024 · 2024
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SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024 , Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, and Cheng Zhang (Eds.)
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AutoCodeRover: Autonomous Program Improvement. In ISSTA
Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. 2024 · 2024
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Agentic AI Software Engineer: Programming with Trust
Abhik Roychoudhury, Corina Pasareanu, Michael Pradel, and Baishakhi Ray. 2025 · 2025
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Calibration and Correctness of Language Models for Code. In International Conference on Software Engineering (ICSE)
Claudio Spiess, David Gros, Kunal Suresh Pai, Michael Pradel, Md Rafiqul Islam Rabin, Amin Alipour, Susmit Jha, Premkumar Devanbu, and Toufique Ahmed. 2025 · 2025
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