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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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.
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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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