Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 255

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Automatically generating commit messages from diffs using neural machine translation

Open access
Jiang, Siyuan, Armaly, Ameer, McMillan, Collin·2017·2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)
DOI 10.1109/ase.2017.8115626
Open this paper in CoChat — free account

Abstract

Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are often verbose and lack context for understanding the rationale behind a change. In contrast, humans write messages that are short and summarize the high level rationale. In this paper, we adapt Neural Machine Translation (NMT) to automatically "translate" diffs into commit messages. We trained an NMT algorithm using a corpus of diffs and human-written commit messages from the top 1k Github projects. We designed a filter to help ensure that we only trained the algorithm on higher-quality commit messages. Our evaluation uncovered a pattern in which the messages we generate tend to be either very high or very low quality. Therefore, we created a quality-assurance filter to detect cases in which we are unable to produce good messages, and return a warning instead.

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 · 10.1109/ase.2017.8115626

Cites

10 of 10 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 10 references

Research works printed but not linked: 50

  1. 10.3115/v1/P14-5010 · DOI · Paper page
  2. 10.1109/MSR.2010.5463344 · DOI
  3. 10.1109/ICPC.2015.13 · DOI
  4. lam, 2015, Combining deep learning with information retrieval to localize buggy files for bug reports (n), 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, 476
  5. 10.1109/WCRE.2001.957835 · DOI · Paper page
  6. 10.18653/v1/P16-1100 · DOI · Paper page
  7. 10.1109/ICSE.2015.229 · DOI
  8. 10.18653/v1/P17-4012 · DOI · Paper page
  9. 10.1109/TSE.2002.1019480 · DOI · Paper page
  10. koehn, 2007, Factored translation models, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 868
  11. 0, Stackoverflow developer survey results 2016
  12. 0, PYPL PopularitY of Programming Language
  13. heafield, 2013, Scalable modified Kneser-Ney language model estimation, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 8
  14. 10.1109/ICSE.2012.6227135 · DOI · Paper page
  15. 10.1109/ICPC.2017.12 · DOI
  16. 10.18653/v1/P16-1195 · DOI · Paper page
  17. 10.1023/B:LIDA.0000048322.42751.ca · DOI
  18. 10.1109/ICSM.2015.7332508 · DOI
  19. 10.18653/v1/E17-3017 · DOI · Paper page
  20. 10.18653/v1/E17-3017 · DOI · Paper page
  21. 10.1109/MSR.2015.38 · DOI
  22. 10.1145/2970276.2970326 · DOI · Paper page
  23. 2016, Theano Development Team, Theano A Python framework for fast computation of mathematical expressions
  24. sutskever, 2014, Sequence to sequence learning with neural networks, Advances in neural information processing systems, 27, 3104
  25. 10.18653/v1/W16-2323 · DOI
  26. 10.18653/v1/W16-2209 · DOI
  27. 10.3115/v1/D14-1179 · DOI · Paper page
  28. 10.1109/SCAM.2014.14 · DOI · Paper page
  29. 10.1109/ICSM.2000.883028 · DOI · Paper page
  30. 10.1109/ICSE.2013.6606588 · DOI · Paper page
  31. 10.1109/WCRE.2007.21 · DOI
  32. goodfellow, 2016, Deep Learning
  33. 10.1145/2950290.2950334 · DOI · Paper page
  34. 10.1109/ICSE.2017.9 · DOI
  35. 10.1109/WCRE.2010.13 · DOI · Paper page
  36. 10.1109/WCRE.2010.13 · DOI · Paper page
  37. heafield, 2011, Kenlm: Faster and smaller language model queries, Proceedings of the Sixth Workshop on Statistical Machine Translation WMT ‘11, 187
  38. 2012, Moses Specification, Deliverable D1 1, 5
  39. 0, TIOBE index for may 2017
  40. bahdanau, 2014, Neural machine translation by jointly learning to align and translate, CoRR abs/1409 0473
  41. 10.1109/ICPC.2017.11 · DOI
  42. callison-burch, 2011, Findings of the 2011 workshop on statistical machine translation, Proceedings of the Sixth Workshop on Statistical Machine Translation WMT ‘11, 22
  43. rubinstein, 2013, The Cross-Entropy Method A Unified Approach to Combinatorial Optimization Monte-Carlo Simulation and Machine Learning
  44. 10.1145/2568225.2568286 · DOI · Paper page
  45. 10.1109/ICSE.2012.6227188 · DOI
  46. 10.1109/ICSE.2013.6606676 · DOI
  47. 10.1007/s11390-016-1671-1 · DOI
  48. 10.1109/TSE.2016.2591536 · DOI
  49. 10.1162/089120103321337421 · DOI · Paper page
  50. 10.1145/2804381.2804387 · DOI

Cited by

Metadata

Authors
Jiang, S., Armaly, A., & McMillan, C.
Venue
2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)
Publisher
IEEE
Published
2017-10
Pages
135-146
DOI
10.1109/ase.2017.8115626
OpenAlex
W2964322208
Cited by
255
References
10
FWCI
14.82
Type
conference paper
Language
en
Open access
green

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

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.