A Software Impact Analysis Tool based on Change History Learning and its Evaluation

Haruya Iwasaki, Tsuyoshi Nakajima, Ryota Tsukamoto, Kazuko Takahashi, Shuichi Tokumoto

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering
ホスト出版物のサブタイトルSoftware Engineering in Practice, ICSE-SEIP 2022
出版社IEEE Computer Society
ページ11-12
ページ数2
ISBN(電子版)9781665495905
DOI
出版ステータスPublished - 2022
イベント44th ACM/IEEE International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2022 - Pittsburgh, United States
継続期間: 2022 5月 222022 5月 27

出版物シリーズ

名前Proceedings - International Conference on Software Engineering
ISSN(印刷版)0270-5257

Conference

Conference44th ACM/IEEE International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2022
国/地域United States
CityPittsburgh
Period22/5/2222/5/27

ASJC Scopus subject areas

  • ソフトウェア

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