GitHub Copilot AI pair programmer: Asset or Liability?
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Metrics and datasets for code generation models test how well the model can complete code in an offline setting without developer input rather than evaluating how well such recommendations assist programmers in situ.
“However, in each case, these metrics test how well the model can complete code in an ofine setting without developer input rather than evaluating how well such recommendations assist programmers in situ.”
From Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming (2024) · cites this paper as [9]
GenAI assistants may not always generate suggestions that align with the coding patterns and practices in an existing code base.
“while GenAI assistants can provide immediate coding assistance, they may not always generate suggestions that align with the coding patterns and practices in an existing code base [9].”
From The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Coding Tasks (2025) · cites this paper as [9]
AI-assisted pair programming offers flexibility in scheduling because students can engage with an AI partner at any time without coordinating with a human partner.
“The flexibility in scheduling is a significant advantage of AI-assisted pair programming. Students can engage with their AI partner at any time, eliminating the need to coordinate with a human partner (Dakhel et al., 2023).”
Concerns regarding academic honesty and student learning have dominated discourse on GenAI in higher education.
“Given the ability of GenAI tools to generate human-like content, such as language and code, concerns regarding academic honesty and student learning have dominated discourse [16, 29, 33, 36, 55, 115, 117].”
This aligns with Orenstrakh et al., with eight tools providing very different levels of accuracy and false positives.
“This aligns to the findings of Orenstrakh et al., with eight tools providing very different levels of accuracy and false positives [36].”
From Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment (2024) · cites this paper as [36]
Cites
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works
Experimentation in Software Engineering
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. (2012) · doi:10.1007/978-3-642-29044-2 · Cited by 4,460 · see citing works
Evaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436 · see citing works
Competition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043 · see citing works
Vaithilingam, P., Zhang, T., & Glassman, E. L. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519665 · Cited by 660 · see citing works
Alur, R., Bodik, R., Juniwal, G., Martin, M. M. K., Raghothaman, M., Seshia, S. A., Singh, R., Solar-Lezama, A., Torlak, E., & Udupa, A. (2013) · 2013 Formal Methods in Computer-Aided Design · doi:10.1109/fmcad.2013.6679385 · Cited by 656 · see citing works
The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming
Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022) · Proceedings of the 24th Australasian Computing Education Conference · doi:10.1145/3511861.3511863 · Cited by 463 · see citing works
An empirical evaluation of GitHub copilot's code suggestions
Nguyen, N., & Nadi, S. (2022) · Proceedings of the 19th International Conference on Mining Software Repositories · doi:10.1145/3524842.3528470 · Cited by 313 · see citing works
Quantifying Memorization Across Neural Language Models
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C. (2022) · arXiv · doi:10.48550/arxiv.2202.07646 · Cited by 204 · see citing works
Productivity Assessment of Neural Code Completion
Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin, D., Simister, S., Sittampalam, G., & Aftandilian, E. (2022) · Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming · doi:10.1145/3520312.3534864 · Cited by 200 · see citing works
Productivity Assessment of Neural Code Completion
Ziegler, A., Kalliamvakou, E., Simister, S., Sittampalam, G., Li, A., Rice, A., Rifkin, D., & Aftandilian, E. (2022) · arXiv · doi:10.48550/arxiv.2205.06537 · Cited by 200 · see citing works
Using GitHub Copilot to Solve Simple Programming Problems
Wermelinger, M. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569830 · Cited by 191 · see citing works
Is GitHub copilot a substitute for human pair-programming?: an empirical study
Imai, S. (2022) · Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings · doi:10.1145/3510454.3522684 · Cited by 116 · see citing works
Does BLEU Score Work for Code Migration?
Tran, N., Tran, H., Nguyen, S., Nguyen, H., & Nguyen, T. (2019) · 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2019.00034 · Cited by 49 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Robots Are Here: Navigating the Generative AI Revolution in Computing Education
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., et al. (2023) · Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3623762.3633499 · Cited by 396 · see citing works
The Metacognitive Demands and Opportunities of Generative AI
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642902 · Cited by 280 · see citing works
Navigating the Complexity of Generative AI Adoption in Software Engineering
Russo, D. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3652154 · Cited by 217 · see citing works
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges
Liang, J. T., Yang, C., & Myers, B. A. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3608128 · Cited by 203 · see citing works
Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning
Nashid, N., Sintaha, M., & Mesbah, A. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00205 · Cited by 178 · see citing works
Kabir, S., Udo-Imeh, D. N., Kou, B., & Zhang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642596 · Cited by 160 · see citing works
The Impact of Large Language Models on Programming Education and Student Learning Outcomes
Jošt, G., Taneski, V., & Karakatič, S. (2024) · Applied Sciences · doi:10.3390/app14104115 · Cited by 143 · see citing works
Effective test generation using pre-trained Large Language Models and mutation testing
Dakhel, A. M., Nikanjam, A., Majdinasab, V., Khomh, F., & Desmarais, M. C. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107468 · Cited by 157 · see citing works
Self-citationFan, G., Liu, D., Zhang, R., & Pan, L. (2025) · International Journal of STEM Education · doi:10.1186/s40594-025-00537-3 · Cited by 109 · see citing works
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641936 · Cited by 114 · see citing works
Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102135 · Cited by 90 · see citing works
Investigating and Designing for Trust in AI-powered Code Generation Tools
Wang, R., Cheng, R., Ford, D., & Zimmermann, T. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658984 · Cited by 87 · see citing works
Significant Productivity Gains through Programming with Large Language Models
Weber, T., Brandmaier, M., Schmidt, A., & Mayer, S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3661145 · Cited by 64 · see citing works
Generative Artificial Intelligence for Software Engineering—A Research Agenda
Nguyen‐Duc, A., Cabrero‐Daniel, B., Przybylek, A., Arora, C., Khanna, D., Herda, T., Rafiq, U., Melegati, J., Guerra, E., Kemell, K., et al. (2025) · Software: Practice and Experience · doi:10.1002/spe.70005 · Cited by 60 · see citing works
Reeves, B., Sarsa, S., Prather, J., Denny, P., Becker, B. A., Hellas, A., Kimmel, B., Powell, G., & Leinonen, J. (2023) · Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3587102.3588805 · Cited by 56 · see citing works
Accelerating Software Development Using Generative AI: ChatGPT Case Study
Rajbhoj, A., Somase, A., Kulkarni, P., & Kulkarni, V. (2024) · Proceedings of the 17th Innovations in Software Engineering Conference · doi:10.1145/3641399.3641403 · Cited by 50 · see citing works
Jordan, M., Ly, K., & Soosai Raj, A. G. (2024) · Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3626252.3630897 · Cited by 39 · see citing works
Weisz, J. D., Kumar, S. V., Muller, M., Browne, K.-E., Goldberg, A., Heintze, K. E., & Bajpai, S. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3706670 · Cited by 37 · see citing works
How Do Programming Students Use Generative AI?
Rahe, C., & Maalej, W. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3715762 · Cited by 45 · see citing works
Large Language Model-Aware In-Context Learning for Code Generation
Li, J., Li, G., Tao, C., Li, J., Zhang, H., Liu, F., & Jin, Z. (2023) · arXiv · doi:10.48550/arxiv.2310.09748 · Cited by 32 · see citing works
Song, F., Agarwal, A., & Wen, W. (2024) · SSRN Electronic Journal (preprint) · doi:10.2139/ssrn.4856935 · Cited by 30 · see citing works
Shihab, M. I. H., Hundhausen, C., Tariq, A., Haque, S., Qiao, Y., & Mulanda, B. W. (2025) · Proceedings of the 2025 ACM Conference on International Computing Education Research V.1 · doi:10.1145/3702652.3744219 · Cited by 28 · see citing works
Zhou, X., Liang, P., Zhang, B., Li, Z., Ahmad, A., Shahin, M., & Waseem, M. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112204 · Cited by 30 · see citing works
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2023) · arXiv · doi:10.48550/arxiv.2306.04930 · Cited by 22 · see citing works
Atcheson, A., Khan, O., Siemann, B., Jain, A., & Karahalios, K. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714121 · Cited by 22 · see citing works
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28878 · Cited by 23 · see citing works
Developer Behaviors in Validating and Repairing LLM-Generated Code Using IDE and Eye Tracking
Tang, N., Chen, M., Ning, Z., Bansal, A., Huang, Y., McMillan, C., & Li, T. J.-J. (2024) · 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00015 · Cited by 18 · see citing works
Analysis of Generative AI Policies in Computing Course Syllabi
Ali, A., Collier, A. H., Dewan, U., McDonald, N., & Johri, A. (2025) · Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3641554.3701823 · Cited by 14 · see citing works
A 2030 Roadmap for Software Engineering
Pezzè, M., Abrahão, S., Penzenstadler, B., Poshyvanyk, D., Roychoudhury, A., & Yue, T. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3731559 · Cited by 13 · see citing works
Assessing and Analyzing the Correctness of GitHub Copilot's Code Suggestions
Mo, R., Wang, D., Zhan, W., Jiang, Y., Wang, Y., Zhao, Y., Li, Z., & Ma, Y. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715108 · Cited by 11 · see citing works
Threats to scientific software from over-reliance on AI code assistants
O’Brien, G. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00845-2 · Cited by 6 · see citing works
CiteME: Can Language Models Accurately Cite Scientific Claims?
Press, O., Hochlehnert, A., Prabhu, A., Udandarao, V., Press, O., & Bethge, M. (2024) · arXiv · doi:10.48550/arxiv.2407.12861 · Cited by 4
Xu, F., Medappa, P. K., Tunc, M. M., Vroegindeweij, M., & Fransoo, J. C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5521379 · Cited by 2 · see citing works
Xu, F., Medappa, P. K., Tunc, M. M., Vroegindeweij, M., & Fransoo, J. C. (2025) · arXiv · doi:10.48550/arxiv.2510.10165 · Cited by 1 · see citing works
A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation
Dickey, E., Bejarano, A., Kuperus, R., & Fagundes, B. (2025) · arXiv
Jiang, J., Shen, J., Kim, S., Yoo, K. M., Kim, J., & Kim, S. (2026) · arXiv · doi:10.48550/arxiv.2603.05863
- Authors
- Moradi Dakhel, A., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., & Jiang, Z. M. (.
- Affiliations
- Polytechnique Montréal (CA)
- York University (CA)
- Corresponding author
- Majdinasab, Vahid
- ORCID
- Venue
- Journal of Systems and Software
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 203
- Pages
- 111734
- ISSN
- 0164-1212 (ISSN-L)
- OpenAlex
- W4367672983
- Cited by
- 417
- References
- 14
- Citations by year
- 2026: 84 · 2025: 155 · 2024: 135 · 2023: 43
- FWCI
- 97.86
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Journal of Systems and Software (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Fonds de recherche du Québec
- Canadian Institute for Advanced Research
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- GitHub Copilot, AI-assisted programming, program synthesis, algorithmic problem solving, program correctness, reproducibility, novice programmers, sorting algorithms, data structure implementation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
