Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 268

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

/*icomment: bugs or bad comments?*/

Tan, Lin, Yuan, Ding, Krishna, Gopal, Zhou, Yuanyuan·2007·Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
DOI 10.1145/1294261.1294276
Open this paper in CoChat — free account

Abstract

Commenting source code has long been a common practice in software development. Compared to source code, comments are more direct, descriptive and easy-to-understand. Comments and sourcecode provide relatively redundant and independent information regarding a program's semantic behavior. As software evolves, they can easily grow out-of-sync, indicating two problems: (1) bugs -the source code does not follow the assumptions and requirements specified by correct program comments; (2) bad comments - comments that are inconsistent with correct code, which can confuse and mislead programmers to introduce bugs in subsequent versions. Unfortunately, as most comments are written in natural language, no solution has been proposed to automatically analyze commentsand detect inconsistencies between comments and source code. This paper takes the first step in automatically analyzing commentswritten in natural language to extract implicit program rulesand use these rules to automatically detect inconsistencies between comments and source code, indicating either bugs or bad comments. Our solution, iComment, combines Natural Language Processing(NLP), Machine Learning, Statistics and Program Analysis techniques to achieve these goals. We evaluate iComment on four large code bases: Linux, Mozilla, Wine and Apache. Our experimental results show that iComment automatically extracts 1832 rules from comments with 90.8-100% accuracy and detects 60 comment-code inconsistencies, 33 newbugs and 27 bad comments, in the latest versions of the four programs. Nineteen of them (12 bugs and 7 bad comments) have already been confirmed by the corresponding developers while the others are currently being analyzed by the developers.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/1294261.1294276

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Research works printed but not linked: 32

  1. 10.1145/945445.945454 · DOI · Paper page
  2. 10.1145/158511.158639 · DOI
  3. 10.1145/945445.945468 · DOI · Paper page
  4. 10.1145/502034.502041 · DOI · Paper page
  5. 10.1145/337180.337240 · doi:10.1145/337180.337240
  6. 10.1109/52.976940 · doi:10.1109/52.976940
  7. 10.1145/512529.512539 · doi:10.1145/512529.512539
  8. 10.1109/32.44365 · doi:10.1109/32.44365
  9. 10.1145/1137983.1138030 · DOI
  10. 10.5555/1247360.1247361 · doi:10.5555/1247360.1247361
  11. T. Kremenek , P. Twohey , G. Back , A. Y. Ng , and D. R. Engler . From uncertainty to belief: Inferring the specification within . In Proceedings of the 7th USENIX Symposium on Operating System Design and Implementation , 2006 . T. Kremenek, P. Twohey, G. Back, A. Y. Ng, and D. R. Engler. From uncertainty to belief: Inferring the specification within. In Proceedings of the 7th USENIX Symposium on Operating System Design and Implementation, 2006.
  12. T. Li , C. Ellis , A. Lebeck , and D. Sorin . On-demand and semantic-free dynamic deadlock detection with speculative execution . In USENIX Annual Technical Conference , 2005 . T. Li, C. Ellis, A. Lebeck, and D. Sorin. On-demand and semantic-free dynamic deadlock detection with speculative execution. In USENIX Annual Technical Conference, 2005.
  13. 10.1145/1081706.1081755 · DOI · Paper page
  14. C. D. Manning and H. Schütze . Foundations Of Statistical Natural Language Processing . The MIT Press , 2001 . C. D. Manning and H. Schütze. Foundations Of Statistical Natural Language Processing. The MIT Press, 2001.
  15. T. Mitchell . Machine Learning . McGraw Hill , 1997 . T. Mitchell. Machine Learning. McGraw Hill, 1997.
  16. 10.5555/1060289.1060297 · doi:10.5555/1060289.1060297
  17. 10.1145/363516.363526 · DOI · Paper page
  18. 10.1145/168619.168630 · doi:10.1145/168619.168630
  19. V. Punyakanok and D. Roth . The use of classifiers in sequential inference . In Proceedings of the Conference on Advances in Neural Information Processing Systems , 2001 . V. Punyakanok and D. Roth. The use of classifiers in sequential inference. In Proceedings of the Conference on Advances in Neural Information Processing Systems, 2001.
  20. V. Punyakanok , D. Roth , and W. Yih . The necessity of syntactic parsing for semantic role labeling . In Proceedings of the International Joint Conference on Artificial Intelligence , 2005 . V. Punyakanok, D. Roth, and W. Yih. The necessity of syntactic parsing for semantic role labeling. In Proceedings of the International Joint Conference on Artificial Intelligence, 2005.
  21. R. J. Quilan . C4.5 : Programs for Machine Learning . Morgan Kaufmann , 1993 . R. J. Quilan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
  22. A. Ratnaparkhi . A maximum entropy model for part-of-speech tagging . In Proceedings of the Conference on Empirical Methods in Natural Language Processing , 1996 . A. Ratnaparkhi. A maximum entropy model for part-of-speech tagging. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 1996.
  23. 10.1145/265924.265927 · DOI · Paper page
  24. 10.1145/237721.237727 · DOI · Paper page
  25. N. Sterling . WARLOCK -- A static data race analysis tool . In USENIX Winter Technical Conference , pages 97 -- 106 , 1993 . N. Sterling. WARLOCK -- A static data race analysis tool. In USENIX Winter Technical Conference, pages 97--106, 1993.
  26. 10.1162/089120102762671936 · DOI · Paper page
  27. I. H. Witten and E. Frank . Data Mining: Practical machine learning tools and techniques ( 2 nd Ed.). Morgan Kaufmann , 2005 . I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques (2nd Ed.). Morgan Kaufmann, 2005.
  28. 10.5555/800078.802534 · doi:10.5555/800078.802534
  29. 10.5555/829515.830562 · doi:10.5555/829515.830562
  30. 10.1145/1083142.1083152 · DOI
  31. 10.1145/1014052.1014150 · DOI · Paper page
  32. F. Zhou , J. Condit , Z. Anderson , I. Bagrak , R. Ennals , M. Harren , G. Necula , and E. Brewer . SafeDrive: Safe and recoverable extensions using language-based techniques . In Proceedings of the 7th Symposium on Operating System Design and Implementation , 2006 . F. Zhou, J. Condit, Z. Anderson, I. Bagrak, R. Ennals, M. Harren, G. Necula, and E. Brewer. SafeDrive: Safe and recoverable extensions using language-based techniques. In Proceedings of the 7th Symposium on Operating System Design and Implementation, 2006.

Cited by

See all 268

Metadata

Authors
Tan, L., Yuan, D., Krishna, G., & Zhou, Y.
Venue
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Publisher
ACM
Published
2007-10-14
Pages
145-158
DOI
10.1145/1294261.1294276
OpenAlex
W2152874840
Cited by
268
References
1
FWCI
4.60
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: /*icomment: bugs or bad comments?*/. https://app.cochat.ai/papers/10.1145/1294261.1294276
The paper
Tan, L., Yuan, D., Krishna, G., & Zhou, Y. (2007). /*icomment: bugs or bad comments?*/. Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles. https://doi.org/10.1145/1294261.1294276

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.