Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929687907794944 |
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| author | Menzies, Tim |
| author_facet | Menzies, Tim |
| contents | Industry can get any research it wants, just by publishing a baseline result along with the data and scripts need to reproduce that work. For instance, the paper ``Data Mining Static Code Attributes to Learn Defect Predictors'' presented such a baseline, using static code attributes from NASA projects. Those result were enthusiastically embraced by a software engineering research community, hungry for data. At its peak (2016) this paper was SE's most cited paper (per month). By 2018, twenty percent of leading TSE papers (according to Google Scholar Metrics), incorporated artifacts introduced and disseminated by this research. This brief note reflects on what we should remember, and what we should forget, from that paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Menzies, Tim Software Engineering Industry can get any research it wants, just by publishing a baseline result along with the data and scripts need to reproduce that work. For instance, the paper ``Data Mining Static Code Attributes to Learn Defect Predictors'' presented such a baseline, using static code attributes from NASA projects. Those result were enthusiastically embraced by a software engineering research community, hungry for data. At its peak (2016) this paper was SE's most cited paper (per month). By 2018, twenty percent of leading TSE papers (according to Google Scholar Metrics), incorporated artifacts introduced and disseminated by this research. This brief note reflects on what we should remember, and what we should forget, from that paper. |
| title | Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2501.15662 |