Abstract
Methods (functions) are the fundamental components of the software. Programmers usually grasp a method’s behavior by looking at the method’s name. Hence, the name of a method should be a summary of what the method does. There has been a study utilizing Word2Vec, Doc2Vec, and the convolutional neural network (CNN) to evaluate the consistency between a method’s name and body in an automated way. While the conventional evaluation procedure detects inconsistent method names successfully, its CNN-based vectorization model construction requires costly computations. This paper focuses on such a computational cost and proposes to replace it with another lightweight vectorization approach. The comparative study using four alternative approaches proved that the Sent2Vec, one of the alternatives, can build the vectorization model 14 times faster than the conventional one while maintaining the capability of detecting inconsistent method names.
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Notes
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CPU: Intel Core i5-6600 3.3GHz; Memory: 16GB; OS: Linux 4.19.128.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their helpful comments on an earlier version of this paper. This work was supported by JSPS KAKENHI Grant #20H04184, #21K11831, and #21K11833.
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Minehisa, T., Aman, H., Yokogawa, T., Kawahara, M. (2021). A Comparative Study of Vectorization Approaches for Detecting Inconsistent Method Names. In: Lee, R. (eds) Computer and Information Science 2021—Summer . ICIS 2021. Studies in Computational Intelligence, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-79474-3_9
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