CMPUT 663 Reading List

Winter 2026

This page mirrors the CMPUT 663 topic outline. This list is not exhaustive, and we will cover additional papers in class.

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Mining Software Repository and Software Analytics

  • Abram Hindle, Earl T. Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu. 2012. On the naturalness of software. In Proceedings of the 34th International Conference on Software Engineering (ICSE ’12). IEEE Press, Piscataway, NJ, USA, 837–847. https://doi.org/10.1109/ICSE.2012.6227135
  • Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M. German, and Daniela Damian. 2014. The promises and perils of mining GitHub. In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR ’14). Association for Computing Machinery, New York, NY, USA, 92–101. https://doi.org/10.1145/2597073.2597074
  • Miltiadis Allamanis and Charles Sutton. 2014. Mining idioms from source code. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE ’14). Association for Computing Machinery, New York, NY, USA, 472–483. https://doi.org/10.1145/2635868.2635901

Tasks and Benchmarking

  • Mark Chen, Jerry Tworek, Heewoo Jun, et al. 2021. Evaluating Large Language Models Trained on Code. arXiv:2107.03374. https://arxiv.org/abs/2107.03374
  • Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023. Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation. In Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS ’23). Curran Associates Inc., Red Hook, NY, USA, 21558-21572.
  • Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. 2021. Program Synthesis with Large Language Models. arXiv:2108.07732. https://arxiv.org/abs/2108.07732
  • Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 2021. CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. arXiv:2102.04664. https://arxiv.org/abs/2102.04664
  • Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2023. SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2310.06770. https://arxiv.org/abs/2310.06770
  • Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2024. Evaluating Large Language Models in Class-Level Code Generation. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (ICSE ’24). Association for Computing Machinery, New York, NY, USA, 1-13. https://doi.org/10.1145/3597503.3639219
  • Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. 2024. LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code. arXiv:2403.07974. https://arxiv.org/abs/2403.07974
  • Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, Thong Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, Binyuan Hui, Niklas Muennighoff, David Lo, Daniel Fried, Xiaoning Du, Harm de Vries, and Leandro Von Werra. 2024. BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions. arXiv:2406.15877. https://arxiv.org/abs/2406.15877
  • Yun Peng, Jun Wan, Yichen Li, and Xiaoxue Ren. 2025. COFFE: A Code Efficiency Benchmark for Code Generation. arXiv:2502.02827. https://arxiv.org/abs/2502.02827

Building CodeLLMs: Pre-training

  • Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, 1536-1547. https://doi.org/10.18653/v1/2020.findings-emnlp.139
  • Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2020. GraphCodeBERT: Pre-training Code Representations with Data Flow. arXiv:2009.08366. https://arxiv.org/abs/2009.08366
  • Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Unified Pre-training for Program Understanding and Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 2655-2668. https://doi.org/10.18653/v1/2021.naacl-main.211
  • Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022. InCoder: A Generative Model for Code Infilling and Synthesis. arXiv:2204.05999. https://arxiv.org/abs/2204.05999
  • Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, and Harm de Vries. 2022. The Stack: 3 TB of permissively licensed source code. arXiv:2211.15533. https://arxiv.org/abs/2211.15533
  • Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi. 2021. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8696-8708. https://doi.org/10.18653/v1/2021.emnlp-main.685
  • Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022. CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. arXiv:2203.13474. https://arxiv.org/abs/2203.13474
  • Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. 2023. StarCoder: may the source be with you!. arXiv:2305.06161. https://arxiv.org/abs/2305.06161
  • Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang. 2024. DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence. arXiv:2401.14196. https://arxiv.org/abs/2401.14196
  • Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2023. Code Llama: Open Foundation Models for Code. arXiv:2308.12950. https://arxiv.org/abs/2308.12950

Building CodeLLMs: Post-training

  • Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023. WizardCoder: Empowering Code Large Language Models with Evol-Instruct. arXiv:2306.08568. https://arxiv.org/abs/2306.08568
  • Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K. Reddy. 2023. Execution-based Code Generation using Deep Reinforcement Learning. arXiv:2301.13816. https://arxiv.org/abs/2301.13816
  • Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven C. H. Hoi. 2022. CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning. arXiv:2207.01780. https://arxiv.org/abs/2207.01780
  • Jiate Liu, Yiqin Zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Wei Yang, and Deheng Ye. 2023. RLTF: Reinforcement Learning from Unit Test Feedback. arXiv:2307.04349. https://arxiv.org/abs/2307.04349
  • Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023. Teaching Large Language Models to Self-Debug. arXiv:2304.05128. https://arxiv.org/abs/2304.05128
  • Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre. 2023. OctoPack: Instruction Tuning Code Large Language Models. arXiv:2308.07124. https://arxiv.org/abs/2308.07124
  • Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, and Xuanjing Huang. 2024. StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 4571-4585. https://doi.org/10.18653/v1/2024.acl-long.251
  • Kechi Zhang, Ge Li, Yihong Dong, Jingjing Xu, Jun Zhang, Jing Su, Yongfei Liu, and Zhi Jin. 2024. CodeDPO: Aligning Code Models with Self Generated and Verified Source Code. arXiv:2410.05605. https://arxiv.org/abs/2410.05605

Building CodeLLMs: Test-time scaling

  • Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022. CodeT: Code Generation with Generated Tests. arXiv:2207.10397. https://arxiv.org/abs/2207.10397
  • Dacheng Li, Shiyi Cao, Chengkun Cao, Xiuyu Li, Shangyin Tan, Kurt Keutzer, Jiarong Xing, Joseph E. Gonzalez, and Ion Stoica. 2025. S*: Test Time Scaling for Code Generation. arXiv:2502.14382. https://arxiv.org/abs/2502.14382
  • DeepSeek-AI. 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv:2501.12948. https://arxiv.org/abs/2501.12948
  • Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022. Competition-level code generation with AlphaCode. Science 378, 6624 (1092-1097). https://doi.org/10.1126/science.abq1158
  • Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023. Reflexion: Language Agents with Verbal Reinforcement Learning. arXiv:2303.11366. https://arxiv.org/abs/2303.11366
  • Hung Le, Yingbo Zhou, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. 2024. INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness. arXiv:2407.02518. https://arxiv.org/abs/2407.02518
  • Boaz Lavon, Shahar Katz, and Lior Wolf. 2025. Execution Guided Line-by-Line Code Generation. arXiv:2506.10948. https://arxiv.org/abs/2506.10948
  • Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, and Sida I. Wang. 2022. Coder Reviewer Reranking for Code Generation. arXiv:2211.16490. https://arxiv.org/abs/2211.16490

Handling Complex Repositories using CodeLLMs

  • John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024. SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering. arXiv:2405.15793. https://arxiv.org/abs/2405.15793
  • Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. 2024. AutoCodeRover: Autonomous Program Improvement. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA ’24). Association for Computing Machinery, Vienna, Austria, 1592-1604. https://doi.org/10.1145/3650212.3680384
  • Islem Bouzenia, Premkumar Devanbu, and Michael Pradel. 2025. RepairAgent: An Autonomous, LLM-Based Agent for Program Repair. In Proceedings of the 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE ’25). IEEE, Ottawa, ON, Canada, 2188-2200. https://doi.org/10.1109/ICSE55347.2025.00157
  • Sirui Hong, Mingchen Zhuge, Jiaqi Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2023. MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. arXiv:2308.00352. https://arxiv.org/abs/2308.00352
  • Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2024. ChatDev: Communicative Agents for Software Development. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (ACL ’24). Association for Computational Linguistics, Bangkok, Thailand, 15174-15186. https://doi.org/10.18653/v1/2024.acl-long.810
  • Wei Cheng, Yuhan Wu, and Wei Hu. 2024. Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion. arXiv:2405.19782. https://arxiv.org/abs/2405.19782
  • Di Wu, Wasi Uddin Ahmad, Dejiao Zhang, Murali Krishna Ramanathan, and Xiaofei Ma. 2024. Repoformer: Selective Retrieval for Repository-Level Code Completion. arXiv:2403.10059. https://arxiv.org/abs/2403.10059
  • Wei Liu, Ailun Yu, Daoguang Zan, Bo Shen, Wei Zhang, Haiyan Zhao, Zhi Jin, and Qianxiang Wang. 2024. GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model. arXiv:2406.07003. https://arxiv.org/abs/2406.07003
  • Yanlin Wang, Yanli Wang, Daya Guo, Jiachi Chen, Ruikai Zhang, Yuchi Ma, and Zibin Zheng. 2024. RLCoder: Reinforcement Learning for Repository-Level Code Completion. arXiv:2407.19487. https://arxiv.org/abs/2407.19487
  • Jinyao Guo, Chengpeng Wang, Xiangzhe Xu, Zian Su, and Xiangyu Zhang. 2025. RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing. arXiv:2501.18160. https://arxiv.org/abs/2501.18160

Trustworthy CodeLLMs

  • Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2025. Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions. Communications of the ACM 68, 2 (2025), 96-105. https://doi.org/10.1145/3610721
  • Neil Perry, Megha Srivastava, Deepak Kumar, and Dan Boneh. 2023. Do Users Write More Insecure Code with AI Assistants? In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23). Association for Computing Machinery, Copenhagen, Denmark, 2785-2799. https://doi.org/10.1145/3576915.3623157
  • Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, and David Lo. 2024. Stealthy Backdoor Attack for Code Models. IEEE Transactions on Software Engineering 50, 4 (2024), 721-741. https://doi.org/10.1109/TSE.2024.3361661
  • Jingxuan He and Martin Vechev. 2023. Large Language Models for Code: Security Hardening and Adversarial Testing. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23). Association for Computing Machinery, Copenhagen, Denmark, 1865-1879. https://doi.org/10.1145/3576915.3623175
  • Shenao Yan, Shen Wang, Yue Duan, Hanbin Hong, Kiho Lee, Doowon Kim, and Yuan Hong. 2024. An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection. In Proceedings of the USENIX Security Symposium (USENIX Security ’24). USENIX Association.
  • Liang Niu, Muhammad Shujaat Mirza, Zayd Maradni, and Christina Pöpper. 2023. CodexLeaks: Privacy Leaks from Code Generation Language Models in GitHub Copilot. In Proceedings of the USENIX Security Symposium (USENIX Security ’23). USENIX Association, 2133-2150.
  • Joseph Spracklen, Raveen Wijewickrama, A. H. M. Nazmus Sakib, Anindya Maiti, Bimal Viswanath, and Murtuza Jadliwala. 2025. We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs. In Proceedings of the USENIX Security Symposium (USENIX Security ’25). USENIX Association, 3687-3706.
  • Weiwei Xu, Kai Gao, Hao He, and Minghui Zhou. 2024. LiCoEval: Evaluating LLMs on License Compliance in Code Generation. arXiv:2408.02487. https://arxiv.org/abs/2408.02487
  • Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro, Laura A. Heymann, Massimiliano Di Penta, Daniel M German, and Denys Poshyvanyk. 2024. Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development. arXiv:2411.10877. https://arxiv.org/abs/2411.10877
  • Arina Kudriavtseva, Nisar Ahmad Hotak, and Olga Gadyatskaya. 2025. My Code Is Less Secure with Gen AI: Surveying Developers’ Perceptions of the Impact of Code Generation Tools on Security. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC ’25). Association for Computing Machinery, Catania International Airport Catania Italy, 1637-1646. https://doi.org/10.1145/3672608.3707778

Improving AI4SE (generate secure code, efficiency code, reasoning ability)

  • Tony, Catherine et al. (2024) – Prompting Techniques for Secure Code Generation: A Systematic Investigation.

Human-factors in Intelligent Coding Tools

  • Shraddha Barke, Michael B. James, and Nadia Polikarpova. 2023. Grounded Copilot: How Programmers Interact with Code-Generating Models. Proceedings of the ACM on Programming Languages 7, OOPSLA1 (2023), 85-111. https://doi.org/10.1145/3586030
  • Jiachen Li, Elizabeth D. Mynatt, Varun Mishra, and Jonathan Bell. 2025. ‘Always Nice and Confident, Sometimes Wrong’: Developer’s Experiences Engaging Generative AI Chatbots Versus Human-Powered Q&A Platforms. Proceedings of the ACM on Human-Computer Interaction 9, 2 (2025), 1-22. https://doi.org/10.1145/3710927
  • Ranim Khojah, Mazen Mohamad, Philipp Leitner, and Francisco Gomes de Oliveira Neto. 2024. Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice. Proceedings of the ACM on Software Engineering 1, FSE (2024), 1819-1840. https://doi.org/10.1145/3660788
  • Jenny T. Liang, Chenyang Yang, and Brad A. Myers. 2024. A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (ICSE ’24). Association for Computing Machinery, Lisbon, Portugal, 1-13. https://doi.org/10.1145/3597503.3608128
  • Ruijia Cheng, Ruotong Wang, Thomas Zimmermann, and Denae Ford. 2024. “It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation Tools. ACM Transactions on Interactive Intelligent Systems 14, 2 (2024), 1-39. https://doi.org/10.1145/3651990
  • Yunbo Lyu, Zhou Yang, Jieke Shi, Jianming Chang, Yue Liu, and David Lo. 2025. “My productivity is boosted, but …” Demystifying Users’ Perception on AI Coding Assistants. arXiv:2508.12285. https://arxiv.org/abs/2508.12285

Industrial adoption and opinions of AI4SE

  • Nicole Davila, Igor Wiese, Igor Steinmacher, Lucas Lucio da Silva, Andre Kawamoto, Gilson Jose Peres Favaro, and Ingrid Nunes. 2024. An Industry Case Study on Adoption of AI-based Programming Assistants. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP ’24). Association for Computing Machinery, Lisbon, Portugal, 92-102. https://doi.org/10.1145/3639477.3643648
  • Albert Ziegler, Eirini Kalliamvakou, X. Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, and Edward Aftandilian. 2024. Measuring GitHub Copilot’s Impact on Productivity. Communications of the ACM 67, 3 (2024), 54-63. https://doi.org/10.1145/3633453
  • Justin D. Weisz, Shraddha Vijay Kumar, Michael Muller, Karen-Ellen Browne, Arielle Goldberg, Katrin Ellice Heintze, and Shagun Bajpai. 2025. Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25). Association for Computing Machinery, Yokohama, Japan, 1-13. https://doi.org/10.1145/3706599.3706670