Name
Abstract | ||
|---|---|---|
Code review is key to ensuring the absence of potential issues in source code. Code reviewers spend a large amount of time to manually check submitted patches based on their knowledge. Since a number of patches sometimes have similar potential issues, code reviewers need to suggest similar source code changes to patch authors. If patch authors notice similar code improvement patterns by themselves before submitting to code review, reviewers’ cost would be reduced. In order to detect similar code changes patterns, this study employs a sequential pattern mining to detect source code improvement patterns that frequently appear in code review history. In a case study using a code review dataset of the OpenStack project, we found that the detected patterns by our proposed approach included effective examples to improve patches without reviewers’ manual check. We also found that the patterns have been changed in time series; our pattern mining approach timely achieves to track the effective code improvement patterns. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IWSC.2019.8665852 | 2019 IEEE 13th International Workshop on Software Clones (IWSC) |
Keywords | Field | DocType |
Encoding,Guidelines,Software,Training,History,Manuals,Time series analysis | Data mining,Time series,Source code,Computer science,Software,Notice,Code (cryptography),Sequential Pattern Mining,Code review,Encoding (memory) | Conference |
ISSN | ISBN | Citations |
2329-0595 | 978-1-7281-1805-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuki Ueda | 1 | 0 | 1.69 |
| Takashi Ishio | 2 | 211 | 28.48 |
| Akinori Ihara | 3 | 238 | 19.84 |
| Ken-ichi Matsumoto | 4 | 1396 | 131.56 |