• Jiang S and Coblenz M. (2024). An Analysis of the Costs and Benefits of Autocomplete in IDEs. Proceedings of the ACM on Software Engineering. 1:FSE. (1284-1306). Online publication date: 12-Jul-2024.

    https://doi.org/10.1145/3660765

  • Murali V, Maddila C, Ahmad I, Bolin M, Cheng D, Ghorbani N, Fernandez R, Nagappan N and Rigby P. (2024). AI-Assisted Code Authoring at Scale: Fine-Tuning, Deploying, and Mixed Methods Evaluation. Proceedings of the ACM on Software Engineering. 1:FSE. (1066-1085). Online publication date: 12-Jul-2024.

    https://doi.org/10.1145/3643774

  • Wang W, Ning H, Zhang G, Liu L and Wang Y. (2024). Rocks Coding, Not Development: A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks. Proceedings of the ACM on Software Engineering. 1:FSE. (699-721). Online publication date: 12-Jul-2024.

    https://doi.org/10.1145/3643758

  • Cook M. The Art of Programming: Challenges in Generating Code for Creative Applications. Proceedings of the 1st ACM International Conference on AI-Powered Software. (139-143).

    https://doi.org/10.1145/3664646.3664774

  • de Moor A, van Deursen A and Izadi M. (2024). A Transformer-Based Approach for Smart Invocation of Automatic Code Completion AIware '24: 1st ACM International Conference on AI-Powered Software. 10.1145/3664646.3664760. 9798400706851. (28-37). Online publication date: 10-Jul-2024.

    https://dl.acm.org/doi/10.1145/3664646.3664760

  • Brown A, D'Angelo S, Murillo A, Jaspan C and Green C. Identifying the Factors That Influence Trust in AI Code Completion. Proceedings of the 1st ACM International Conference on AI-Powered Software. (1-9).

    https://doi.org/10.1145/3664646.3664757

  • Khemka M and Houck B. (2024). Toward Effective AI Support for Developers. Queue. 22:3. (53-78). Online publication date: 30-Jun-2024.

    https://doi.org/10.1145/3675416

  • Cheng R, Wang R, Zimmermann T and Ford D. (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. (1-39). Online publication date: 30-Jun-2024.

    https://doi.org/10.1145/3651990

  • Weber T, Brandmaier M, Schmidt A and Mayer S. (2024). Significant Productivity Gains through Programming with Large Language Models. Proceedings of the ACM on Human-Computer Interaction. 8:EICS. (1-29). Online publication date: 17-Jun-2024.

    https://doi.org/10.1145/3661145

  • Choksi M, Mandel I, Widder D and Shvartzshnaider Y. The Emerging Artifacts of Centralized Open-Code. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. (1971-1983).

    https://doi.org/10.1145/3630106.3659019

  • Wang R, Cheng R, Ford D and Zimmermann T. Investigating and Designing for Trust in AI-powered Code Generation Tools. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. (1475-1493).

    https://doi.org/10.1145/3630106.3658984

  • Tankelevitch L, Kewenig V, Simkute A, Scott A, Sarkar A, Sellen A and Rintel S. The Metacognitive Demands and Opportunities of Generative AI. Proceedings of the CHI Conference on Human Factors in Computing Systems. (1-24).

    https://doi.org/10.1145/3613904.3642902

  • Nguyen S, Babe H, Zi Y, Guha A, Anderson C and Feldman M. How Beginning Programmers and Code LLMs (Mis)read Each Other. Proceedings of the CHI Conference on Human Factors in Computing Systems. (1-26).

    https://doi.org/10.1145/3613904.3642706

  • M. Bran A, Cox S, Schilter O, Baldassari C, White A and Schwaller P. (2024). Augmenting large language models with chemistry tools. Nature Machine Intelligence. 10.1038/s42256-024-00832-8. 6:5. (525-535).

    https://www.nature.com/articles/s42256-024-00832-8

  • Mendes W, Souza S and de Souza C. (2024). Colaboração com Assistente de Codificação Baseado em IA: Benefícios e Desafios Simpósio Brasileiro de Sistemas Colaborativos. 10.5753/sbsc.2024.237964. . (228-236).

    https://sol.sbc.org.br/index.php/sbsc/article/view/28120

  • Sikand S, Phokela K, Sharma V, Singi K, Kaulgud V, Tung T, Sharma P and Burden A. How much SPACE do metrics have in GenAI assisted software development?. Proceedings of the 17th Innovations in Software Engineering Conference. (1-5).

    https://doi.org/10.1145/3641399.3641419

  • Ani Z, Hamid Z and Zhamri N. (2024). The Recent Trends of Research on GitHub Copilot: A Systematic Review. Computing and Informatics. 10.1007/978-981-99-9589-9_27. (355-366).

    https://link.springer.com/10.1007/978-981-99-9589-9_27

  • Dakhel A, Nikanjam A, Khomh F, Desmarais M and Washizaki H. (2024). Generative AI for Software Development: A Family of Studies on Code Generation. Generative AI for Effective Software Development. 10.1007/978-3-031-55642-5_7. (151-172).

    https://link.springer.com/10.1007/978-3-031-55642-5_7

  • Venkatesh V, Venkatesh V and Kumar V. Evaluating Copilot on CS1 Code Writing Problems with Suppressed Specifications. Proceedings of the 16th Annual ACM India Compute Conference. (104-107).

    https://doi.org/10.1145/3627217.3627235

  • Hliš T, Četina L, Beranič T and Pavlič L. (2023). Evaluating the Usability and Functionality of Intelligent Source Code Completion Assistants: A Comprehensive Review. Applied Sciences. 10.3390/app132413061. 13:24. (13061).

    https://www.mdpi.com/2076-3417/13/24/13061

  • Wang J and Chen Y. (2023). A Review on Code Generation with LLMs: Application and Evaluation 2023 IEEE International Conference on Medical Artificial Intelligence (MedAI). 10.1109/MedAI59581.2023.00044. 979-8-3503-5878-0. (284-289).

    https://ieeexplore.ieee.org/document/10403378/

  • Asare O, Nagappan M and Asokan N. (2023). Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?. Empirical Software Engineering. 28:6. Online publication date: 1-Nov-2023.

    https://doi.org/10.1007/s10664-023-10380-1

  • Aveni T, Fox A and Hartmann B. Bringing Context-Aware Completion Suggestions to Arbitrary Text Entry Interfaces. Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. (1-3).

    https://doi.org/10.1145/3586182.3615825

  • Liu J, Tang X, Li L, Chen P and Liu Y. (2023). ChatGPT vs. Stack Overflow: An Exploratory Comparison of Programming Assistance Tools 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C). 10.1109/QRS-C60940.2023.00105. 979-8-3503-5939-8. (364-373).

    https://ieeexplore.ieee.org/document/10430054/

  • Ferdowsi K, Williams J, Drosos I, Gordon A, Negreanu C, Polikarpova N, Sarkar A and Zorn B. (2023). COLDECO: An End User Spreadsheet Inspection Tool for AI-Generated Code 2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 10.1109/VL-HCC57772.2023.00017. 979-8-3503-2946-9. (82-91).

    https://ieeexplore.ieee.org/document/10305647/

  • Hearst M. (2023). Show It or Tell It? Text, Visualization, and Their Combination. Communications of the ACM. 66:10. (68-75). Online publication date: 1-Oct-2023.

    https://doi.org/10.1145/3593580

  • Sandoval G, Pearce H, Nys T, Karri R, Garg S and Dolan-Gavitt B. Lost at C. Proceedings of the 32nd USENIX Conference on Security Symposium. (2205-2222).

    /doi/10.5555/3620237.3620361

  • Cassano F, Gouwar J, Nguyen D, Nguyen S, Phipps-Costin L, Pinckney D, Yee M, Zi Y, Anderson C, Feldman M, Guha A, Greenberg M and Jangda A. (2023). MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation. IEEE Transactions on Software Engineering. 49:7. (3675-3691). Online publication date: 1-Jul-2023.

    https://doi.org/10.1109/TSE.2023.3267446

  • Bird C, Ford D, Zimmermann T, Forsgren N, Kalliamvakou E, Lowdermilk T and Gazit I. (2023). Taking Flight with Copilot. Communications of the ACM. 66:6. (56-62). Online publication date: 1-Jun-2023.

    https://doi.org/10.1145/3589996

  • Nashid N, Sintaha M and Mesbah A. Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning. Proceedings of the 45th International Conference on Software Engineering. (2450-2462).

    https://doi.org/10.1109/ICSE48619.2023.00205

  • Mastropaolo A, Pascarella L, Guglielmi E, Ciniselli M, Scalabrino S, Oliveto R and Bavota G. On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot. Proceedings of the 45th International Conference on Software Engineering. (2149-2160).

    https://doi.org/10.1109/ICSE48619.2023.00181

  • Sun Z, Du X, Song F, Wang S, Ni M and Li L. (2023). Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems 2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 10.1109/ICSE-Companion58688.2023.00089. 979-8-3503-2263-7. (324-325).

    https://ieeexplore.ieee.org/document/10172653/

  • Arakawa R, Yakura H and Goto M. CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (1-19).

    https://doi.org/10.1145/3544548.3581133

  • Mcnutt A, Wang C, Deline R and Drucker S. On the Design of AI-powered Code Assistants for Notebooks. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (1-16).

    https://doi.org/10.1145/3544548.3580940

  • Ross S, Martinez F, Houde S, Muller M and Weisz J. The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development. Proceedings of the 28th International Conference on Intelligent User Interfaces. (491-514).

    https://doi.org/10.1145/3581641.3584037

  • Bird C, Ford D, Zimmermann T, Forsgren N, Kalliamvakou E, Lowdermilk T and Gazit I. (2023). Taking Flight with Copilot. Queue. 20:6. (35-57). Online publication date: 31-Dec-2022.

    https://doi.org/10.1145/3582083

  • Al Madi N. How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilot. Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. (1-5).

    https://doi.org/10.1145/3551349.3560438