A software impact analysis tool based on change history learning and its evaluation
Haruya Iwasaki, Tsuyoshi Nakajima, Ryota Tsukamoto, Kazuko Takahashi, Shuichi Tokumoto · 2022
Software change impact analysis plays an important role in controlling software evolution in the maintenance of continuous software development. We developed a tool for change impact analysis, which machine-learns change histories and directly outputs candidates of the components to be modified for a change request. We applied the tool to real project data to evaluate it with two metrics: coverage range ratio and accuracy in the coverage range. The results show that it works well for software projects having many change histories for one source code base.