Abstract
Programming is a form of communication between the person who is writing code and the one reading it. Nevertheless, very often developers neglect readability, and even well-written code becomes less understandable as software evolves. Together with the growing complexity of software systems, this creates an increasing need for automated tools for improving the readability of source code. In this work, we focus on method names and study how a descriptive name can be automatically generated from a method’s body. We experiment with two approaches from the field of text summarization: One based on TF-IDF and the other on deep recurrent neural network. We collect a dataset of methods from 50 real world projects. We evaluate our approaches by comparing the generated names to the actual ones and report the result using Precision and Recall metrics. For TF-IDF, we get results as good as 28% precision and 45% recall; and for deep neural network, 46% precision and 32% recall.
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Notes
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- 2.
The actual meaning of the code is not important, but, double quotes delimit comments, pipes delimit local variables declaration, square brackets delimit lambda functions, and caret is a return.
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- 4.
The probability that during training the word generated by the model is substituted by the word from a real name. It is used to make the training smoother.
- 5.
Harmonic mean is more intuitive than the arithmetic mean when computing a mean of ratios.
- 6.
The complete list of stop words that we used in this study can be found here: https://gist.github.com/olekscode/125804150f2a559a171bf695c0a3f809.
References
Allamanis, M., Barr, E.T., Bird, C., Sutton, C.: Suggesting accurate method and class names. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, pp. 38–49. ACM (2015)
Allamanis, M., Barr, E.T., Devanbu, P., Sutton, C.: A survey of machine learning for big code and naturalness. ACM Comput. Surv. (CSUR) 51(4), 81 (2018)
Allamanis, M., Peng, H., Sutton, C.: A convolutional attention network for extreme summarization of source code. In: International Conference on Machine Learning, pp. 2091–2100 (2016)
Alon, U., Zilberstein, M., Levy, O., Yahav, E.: code2vec: learning distributed representations of code. arXiv preprint arXiv:1803.09473 (2018)
Bavishi, R., Pradel, M., Sen, K.: Context2name: a deep learning-based approach to infer natural variable names from usage contexts. arXiv preprint arXiv:1809.05193 (2018)
Beck, K.: Test Driven Development: By Example. Addison-Wesley Longman (2002)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Demeyer, S., Ducasse, S., Nierstrasz, O.: Object-Oriented Reengineering Patterns. Morgan Kaufmann, Burlington (2002)
Fowler, M., Beck, K., Brant, J., Opdyke, W., Roberts, D.: Refactoring: Improving the Design of Existing Code. Addison Wesley, Boston (1999)
Gabel, M., Su, Z.: A study of the uniqueness of source code. In: Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 147–156. ACM (2010)
Hindle, A., Barr, E.T., Gabel, M., Su, Z., Devanbu, P.: On the naturalness of software. Commun. ACM 59(5), 122–131 (2016)
Hindle, A., Barr, E.T., Su, Z., Gabel, M., Devanbu, P.: On the naturalness of software. In: 2012 34th International Conference on Software Engineering (ICSE), pp. 837–847. IEEE (2012)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Iyer, S., Konstas, I., Cheung, A., Zettlemoyer, L.: Summarizing source code using a neural attention model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2073–2083 (2016)
Knuth, D.E.: Literate programming. Comput. J. 27(2), 97–111 (1984)
Koenig, A.: Patterns and antipatterns. J. Object-Oriented Program. 8(1), 46–48 (1995)
Lehman, M., Belady, L.: Program Evolution: Processes of Software Change. London Academic Press, London (1985). ftp://ftp.umh.ac.be/pub/ftp_infofs/1985/ProgramEvolution.pdf
Martin, R.C.: Clean Code: A Handbook of Agile Software Craftsmanship. Pearson Education, London (2009)
Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133–142 (2003)
Raychev, V., Vechev, M., Yahav, E.: Code completion with statistical language models. In: ACM SIGPLAN Notices, vol. 49, pp. 419–428. ACM (2014)
Rush, A.M., Harvard, S., Chopra, S., Weston, J.: A neural attention model for sentence summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, ACLWeb (2017)
Sasaki, Y., et al.: The truth of the F-measure. Teach Tutor Mater 1(5), 1–5 (2007)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
White, M., Vendome, C., Linares-Vásquez, M., Poshyvanyk, D.: Toward deep learning software repositories. In: Proceedings of the 12th Working Conference on Mining Software Repositories, pp. 334–345. IEEE Press (2015)
Zaitsev, O.: Aspects of software naturalness through the generation of identifier names. Master’s thesis, Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences, Lviv, Ukraine (January 2019). http://er.ucu.edu.ua/handle/1/1338. under sup. of Stéphane Ducasse and Alexandre Bergel
Zaitsev, O., Ducasse, S., Anquetil, N.: Characterizing pharo code: a technical report. Technical report, Inria Lille Nord Europe - Laboratoire CRIStAL - Université de Lille; Arolla (January 2020). https://hal.inria.fr/hal-02440055
Acknowledgements
This work is based on the Master’s thesis of Oleksandr Zaitsev defended at the Ukrainian Catholic University [25]. Oleksandr would like to thank the University of Chile, Inria Lille, Pharo Association, and Arolla for financial support. Alexandre Bergel thanks the financial sponsor of Lam Research and project FONDECYT Regular 1200067.
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Zaitsev, O., Ducasse, S., Bergel, A., Eveillard, M. (2020). Suggesting Descriptive Method Names: An Exploratory Study of Two Machine Learning Approaches. In: Shepperd, M., Brito e Abreu, F., Rodrigues da Silva, A., Pérez-Castillo, R. (eds) Quality of Information and Communications Technology. QUATIC 2020. Communications in Computer and Information Science, vol 1266. Springer, Cham. https://doi.org/10.1007/978-3-030-58793-2_8
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