Source Code Hotspots: A Diagnostic Method for Quality Issues
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910021668831232 |
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| author | Muzammil, Saleha Rehman, Mughees Ur Kotti, Zoe Spinellis, Diomidis |
| author_facet | Muzammil, Saleha Rehman, Mughees Ur Kotti, Zoe Spinellis, Diomidis |
| contents | Software source code often harbours "hotspots": small portions of the code that change far more often than the rest of the project and thus concentrate maintenance activity. We mine the complete version histories of 91 evolving, actively developed GitHub repositories and identify 15 recurring line-level hotspot patterns that explain why these hotspots emerge. The three most prevalent patterns are Pinned Version Bump (26%), revealing brittle release practices; Long Line Change (17%), signalling deficient layout; and Formatting Ping-Pong (9%), indicating missing or inconsistent style automation. Surprisingly, automated accounts generate 74% of all hotspot edits, suggesting that bot activity is a dominant but largely avoidable source of noise in change histories. By mapping each pattern to concrete refactoring guidelines and continuous integration checks, our taxonomy equips practitioners with actionable steps to curb hotspots and systematically improve software quality in terms of configurability, stability, and changeability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13170 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Source Code Hotspots: A Diagnostic Method for Quality Issues Muzammil, Saleha Rehman, Mughees Ur Kotti, Zoe Spinellis, Diomidis Software Engineering D.2.7; D.2.8; D.2.3; F.3.2 Software source code often harbours "hotspots": small portions of the code that change far more often than the rest of the project and thus concentrate maintenance activity. We mine the complete version histories of 91 evolving, actively developed GitHub repositories and identify 15 recurring line-level hotspot patterns that explain why these hotspots emerge. The three most prevalent patterns are Pinned Version Bump (26%), revealing brittle release practices; Long Line Change (17%), signalling deficient layout; and Formatting Ping-Pong (9%), indicating missing or inconsistent style automation. Surprisingly, automated accounts generate 74% of all hotspot edits, suggesting that bot activity is a dominant but largely avoidable source of noise in change histories. By mapping each pattern to concrete refactoring guidelines and continuous integration checks, our taxonomy equips practitioners with actionable steps to curb hotspots and systematically improve software quality in terms of configurability, stability, and changeability. |
| title | Source Code Hotspots: A Diagnostic Method for Quality Issues |
| topic | Software Engineering D.2.7; D.2.8; D.2.3; F.3.2 |
| url | https://arxiv.org/abs/2602.13170 |