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Recent Large Language Models (LLMs) have demonstrated significant capabilities in generating code snippets directly from problem statements.
Does automated unit test generation really help software testers? a controlled empirical study
Gordon Fraser, Matt Staats, Phil McMinn, Andrea Arcuri, and Frank Padberg. 2015 · 2015
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Measuring Coding Challenge Competence With APPS
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Test driven development: By example
Kent Beck. 2022 · 2022
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Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
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Interactive code generation via test-driven user-intent formalization
Shuvendu K Lahiri, Aaditya Naik, Georgios Sakkas, Piali Choudhury, Curtis von Veh, Madanlal Musuvathi, Jeevana Priya Inala, Chenglong Wang, and Jianfeng Gao. 2022 · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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Compilable neural code generation with compiler feedback
Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, and Qun Liu. 2022b · 2022
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Large language models are reasoners with self-verification
Yixuan Weng, Minjun Zhu, Shizhu He, Kang Liu, and Jun Zhao. 2022 · 2022
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Decentralised autonomous society through large language models’ based agents: a pathway to empower small communities
Vasili Braga. 2023 · 2023
Cited alongside, same era.
MultiPL-E: a scalable and polyglot approach to benchmarking neural code generation
Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al · 2023
Cited alongside, same era.
CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, et al · 2023
Cited alongside, same era.
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
Cited alongside, same era.
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
Later among the works it cites.
Chatbot arena: An open platform for evaluating llms by human preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E Gonzalez, et al · 2024
Closest in time.
Evaluating large language models in class-level code generation. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering . 1–13
Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2024 · 2024
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LLM-based Test-driven Interactive Code Generation: User Study and Empirical Evaluation
Sarah Fakhoury, Aaditya Naik, Georgios Sakkas, Saikat Chakraborty, and Shuvendu K Lahiri. 2024 · 2024
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Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 919–931
Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen. 2023 · 2023
Cited alongside, same era.
Making language models better reasoners with step-aware verifier. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 5315–5333
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023
Cited alongside, same era.
Lever: Learning to verify language-to-code generation with execution. In International Conference on Machine Learning . PMLR, 26106–26128
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin. 2023 · 2023
Cited alongside, same era.
Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
Cited alongside, same era.
An empirical evaluation of using large language models for automated unit test generation
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2023 · 2023
Cited alongside, same era.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023 · 2023
Cited alongside, same era.
Test-Driven Multi-Task Learning with Functionally Equivalent Code Transformation for Neural Code Generation. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–6
Xin Wang, Xiao Liu, Pingyi Zhou, Qixia Liu, Jin Liu, Hao Wu, and Xiaohui Cui. 2022a
Cited in the paper.
Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2024 · 2024
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Automatic Programming: Large Language Models and Beyond
Michael R Lyu, Baishakhi Ray, Abhik Roychoudhury, Shin Hwei Tan, and Patanamon Thongtanunam. 2024 · 2024
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Quality and Trust in LLM-generated Code
Claudio Spiess, David Gros, Kunal Suresh Pai, Michael Pradel, Md Rafiqul Islam Rabin, Susmit Jha, Prem Devanbu, and Toufique Ahmed. 2024 · 2024
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Enhancing Trust in LLM-Generated Code Summaries with Calibrated Confidence Scores
Yuvraj Virk, Premkumar Devanbu, and Toufique Ahmed. 2024 · 2024
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AutoCodeRover: Autonomous Program Improvement
Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. 2024 · 2024
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