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A Neural Model for Generating Natural Language Summaries of Program Subroutines

Open access
LeClair, Alexander, Jiang, Siyuan, McMillan, Collin·2019·2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

Abstract

Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature.

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Research works printed but not linked: 49

  1. 10.1007/s11390-016-1671-1 · DOI
  2. sutskever, 2011, Generating text with recurrent neural networks, Proceedings of the 28th International Conference on Machine Learning (ICML-11), 1017
  3. 10.1145/3180155.3180167 · DOI · Paper page
  4. barone, 2017, A parallel corpus of python functions and documentation strings for automated code documentation and code generation, ArXiv Preprint
  5. allamanis, 2015, Bimodal modelling of source code and natural language, International Conference on Machine Learning, 2123
  6. movshovitz-attias, 2013, Natural language models for predicting programming comments, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2 Short Papers), 2, 35
  7. 10.1145/1985793.1985808 · DOI · Paper page
  8. bahdanau, 2014, Neural machine translation by jointly learning to align and translate, ArXiv Preprint
  9. 10.1145/3178876.3186081 · DOI
  10. garmash, 2016, Ensemble learning for multi-source neural machine translation, Proceedings of COLING 2016 the 26th International Conference on Computational Linguistics Technical Papers, 1409
  11. 10.24963/ijcai.2018/314 · DOI · Paper page
  12. 10.1145/3172871.3172891 · DOI
  13. 10.1145/318372.318577 · DOI
  14. 10.1109/TSE.2015.2465386 · DOI
  15. 10.1109/ASE.2017.8115626 · DOI · Paper page
  16. 10.1145/3196321.3196334 · DOI · Paper page
  17. lopes, 2010, UCI Source Code Data Sets
  18. cohen, 2018, Workshop on nlp for software engineering
  19. 10.18653/v1/W17-3516 · DOI
  20. 10.1109/ICSE.2003.1201194 · DOI · Paper page
  21. 10.1145/1985793.1985809 · DOI · Paper page
  22. grnroos, 2018, Opennmt ensemble decoding
  23. chin yee lee, 2018, Nltk translate bleu score calculator v3 3
  24. louis, 2018, Deep learning to detect redundant method comments, CoRR, abs 1806 4616
  25. 10.1109/ICST.2012.106 · DOI
  26. 10.1109/SCAM.2011.19 · DOI
  27. 10.1109/IWESEP.2016.18 · DOI
  28. 10.1109/ICSE.2009.5070524 · DOI
  29. 10.1109/WCRE.2010.13 · DOI · Paper page
  30. 10.1109/ICSM.2011.6080777 · DOI
  31. 10.1109/ICPC.2013.6613830 · DOI · Paper page
  32. 10.1109/TSE.2015.2442238 · DOI
  33. 10.1109/ASE.2015.36 · DOI
  34. allamanis, 2016, A convolutional attention network for extreme summarization of source code, International Conference on Machine Learning, 2091
  35. 10.18653/v1/P16-1195 · DOI · Paper page
  36. 10.1016/0164-1212(87)90032-X · DOI · Paper page
  37. 10.1109/2.402076 · DOI · Paper page
  38. maletic, 2001, Supporting program comprehension using semantic and structural information, Proceedings of the 23rd International Conference on Software Engineering, 103
  39. 10.1109/TSE.2009.28 · DOI · Paper page
  40. van heesch, 2018, Doxygen website
  41. mao, 2014, Deep captioning with multimodal recurrent neural networks (m-rnn), ArXiv Preprint
  42. 10.1109/ICPC.2013.6613829 · DOI · Paper page
  43. chen, 2015, Abc-cnn: An attention based convolutional neural network for visual question answering, ArXiv Preprint
  44. 10.1162/tacl_a_00065 · DOI · Paper page
  45. 10.1109/CVPR.2016.494 · DOI · Paper page
  46. 10.1109/CVPR.2016.10 · DOI · Paper page
  47. sutskever, 2014, Sequence to sequence learning with neural networks, Advances in neural information processing systems, 3104
  48. vaswani, 2017, Attention is all you need, Advances in neural information processing systems, 5998
  49. goodfellow, 2016, Deep Learning, 1

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Metadata

Authors
LeClair, A., Jiang, S., & McMillan, C.
Venue
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
Publisher
IEEE
Published
2019-05
Pages
795-806
OpenAlex
W2914501196
Cited by
294
References
11
FWCI
33.15
Type
conference paper
Language
en
Open access
green

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LeClair, A., Jiang, S., & McMillan, C. (2019). A Neural Model for Generating Natural Language Summaries of Program Subroutines. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). https://doi.org/10.1109/icse.2019.00087

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