[研究発表] Untangling Composite Changes Using Tree-based Convolution Neural Network

小林研M2の李さんが、3月3日から2日間オンライン開催された電子情報通信学会 ソフトウェアサイエンス研究会にて研究発表を行いました。

著者： 李聡・小林隆志（東工大）
題目： Untangling Composite Changes Using Tree-based Convolution Neural Network
掲載誌： 信学技報, vol. 120, no. 407, SS2020-46, pp. 108-113, 2021年3月.
概要：
Developers often bundle unrelated changes in a single commit, thus creating a so-called composite commit. Composite commit is problematic because it makes code review, reversion, and integration of these commits harder. Recent
researches have attempted to use the information of Abstract Syntax Tree (AST) to untangling composite commits. However, they did not make full use of the AST structure information. To make full use of AST structure information to untangle a
composite commit. First, we predict the relationship between two code fragments using a Tree-based CNN model, which can capture both the structural and lexical information of the code fragment. Second, we cluster these code fragments according to
their relationship. Third, we evaluated whether our approach can untangle composite commits correctly.