Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3510457.3519017 | 2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) |
Keywords | DocType | ISBN |
continuous software development,machine-learns change histories,software projects,software impact analysis tool,history learning,software change impact analysis,software evolution | Conference | 978-1-6654-9591-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haruya Iwasaki | 1 | 0 | 0.34 |
| Tsuyoshi Nakajima | 2 | 0 | 0.34 |
| Ryota Tsukamoto | 3 | 0 | 0.34 |
| Kazuko Takahashi | 4 | 0 | 0.34 |
| Shuichi Tokumoto | 5 | 0 | 0.34 |