Deep Just-In-Time Inconsistency Detection Between Comments and Source Code
Open accessAbstract
Natural language comments convey key aspects of source code such as implementation, usage, and pre- and post-conditions. Failure to update comments accordingly when the corresponding code is modified introduces inconsistencies, which is known to lead to confusion and software bugs. In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code, in order to catch potential inconsistencies just-in-time, i.e., before they are committed to a code base. To achieve this, we develop a deep-learning approach that learns to correlate a comment with code changes. By evaluating on a large corpus of comment/code pairs spanning various comment types, we show that our model outperforms multiple baselines by significant margins. For extrinsic evaluation, we show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system which can both detect and resolve inconsistent comments based on code changes.
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Cites
The adverse effects of code duplication in machine learning models of code
Allamanis, M. (2019) · Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software · doi:10.1145/3359591.3359735 · Cited by 284 · see citing works
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al. (2020) · arXiv · doi:10.48550/arxiv.2002.08155 · Cited by 243 · see citing works
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Program Synthesis with Large Language Models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07732 · Cited by 388 · see citing works
Exploring the Capabilities of LLMs for Code-Change-Related Tasks
Fan, L., Liu, J., Liu, Z., Lo, D., Xia, X., & Li, S. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3709358 · Cited by 41 · see citing works
“To perform our empirical study, we consider three emerging code-change-related tasks that have been popularly researched in recent years, namely, code review generation [37, 69], commit message generation [13, 45], and just-in-time comment update [60, 87].”
Predictive Comment Updating With Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach
Lin, B., Wang, S., Liu, Z., Xia, X., & Mao, X. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3185458 · Cited by 27 · see citing works
- Authors
- Panthaplackel, S., Li, J. J., Gligoric, M., & Mooney, R. J.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2021-05-18
- Volume
- 35
- Issue
- 1
- Pages
- 427-435
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W3089384363
- Cited by
- 2
- References
- 2
- Citations by year
- 2023: 1 · 2022: 1
- FWCI
- 0.40
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- code comments, code amendments, just-in-time feedback, deep learning, software documentation quality, preconditions and postconditions
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