Revisiting Method-Level Change Prediction: A Comparative Evaluation at Different Granularities
Abstract
To improve the efficiency of software maintenance, change prediction techniques have been proposed to predict frequently changing modules. Whereas existing techniques focus primarily on class-level prediction, method-level prediction allows for more direct identification of change locations. Method-level prediction can be useful, but it may also negatively affect prediction performance, leading to a trade-off. This makes it unclear which level of granularity users should select for their predictions. In this paper, we evaluated the performance of method-level change prediction compared with that of class-level prediction from three perspectives: direct comparison, method-level comparison, and maintenance effort-aware comparison. The results from 15 open source projects show that, although method-level prediction exhibited lower performance than class-level prediction in the direct comparison, method-level prediction outperformed class-level prediction when both were evaluated at method-level, leading to a median difference of 0.26 in accuracy. Furthermore, effort-aware comparison shows that method-level prediction performed significantly better when the acceptable maintenance effort is little.
Cite
@article{arxiv.2502.17908,
title = {Revisiting Method-Level Change Prediction: A Comparative Evaluation at Different Granularities},
author = {Hiroto Sugimori and Shinpei Hayashi},
journal= {arXiv preprint arXiv:2502.17908},
year = {2025}
}