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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering
Subtitle of host publicationSoftware Engineering in Practice, ICSE-SEIP 2022
PublisherIEEE Computer Society
Pages11-12
Number of pages2
ISBN (Electronic)9781665495905
DOIs
Publication statusPublished - 2022
Event44th ACM/IEEE International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2022 - Pittsburgh, United States
Duration: 2022 May 222022 May 27

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

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

ASJC Scopus subject areas

  • Software

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