Enhancing Code Quality with Generative AI: Boosting Developer Warning Compliance
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912380856827904 |
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| author | Chang, Hansen DeLozier, Christian |
| author_facet | Chang, Hansen DeLozier, Christian |
| contents | Programmers have long ignored warnings, especially those generated by static analysis tools, due to the potential for false-positives. In some cases, warnings may be indicative of larger issues, but programmers may not understand how a seemingly unimportant warning can grow into a vulnerability. Because these messages tend to be long and confusing, programmers tend to ignore them if they do not cause readily identifiable issues. Large language models can simplify these warnings, explain the gravity of important warnings, and suggest potential fixes to increase developer compliance with fixing warnings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Code Quality with Generative AI: Boosting Developer Warning Compliance Chang, Hansen DeLozier, Christian Software Engineering Machine Learning Programmers have long ignored warnings, especially those generated by static analysis tools, due to the potential for false-positives. In some cases, warnings may be indicative of larger issues, but programmers may not understand how a seemingly unimportant warning can grow into a vulnerability. Because these messages tend to be long and confusing, programmers tend to ignore them if they do not cause readily identifiable issues. Large language models can simplify these warnings, explain the gravity of important warnings, and suggest potential fixes to increase developer compliance with fixing warnings. |
| title | Enhancing Code Quality with Generative AI: Boosting Developer Warning Compliance |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2505.11677 |