Less Noise, More Signal: DRR for Better Optimizations of SE Tasks
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917968752934912 |
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| author | Lustosa, Andre Menzies, Tim |
| author_facet | Lustosa, Andre Menzies, Tim |
| contents | SE analytics problems do not always need complex AI. Better and faster solutions can sometimes be obtained by matching the complexity of the problem to the complexity of the solution. This paper introduces the Dimensionality Reduction Ratio (DRR), a new metric for predicting when lightweight algorithms suffice. Analyzing SE optimization problems from software configuration to process decisions and open-source project health we show that DRR pinpoints "simple" tasks where costly methods like DEHB (a state-of-the-art evolutionary optimizer) are overkill. For high-DRR problems, simpler methods can be just as effective and run two orders of magnitude faster. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Less Noise, More Signal: DRR for Better Optimizations of SE Tasks Lustosa, Andre Menzies, Tim Software Engineering SE analytics problems do not always need complex AI. Better and faster solutions can sometimes be obtained by matching the complexity of the problem to the complexity of the solution. This paper introduces the Dimensionality Reduction Ratio (DRR), a new metric for predicting when lightweight algorithms suffice. Analyzing SE optimization problems from software configuration to process decisions and open-source project health we show that DRR pinpoints "simple" tasks where costly methods like DEHB (a state-of-the-art evolutionary optimizer) are overkill. For high-DRR problems, simpler methods can be just as effective and run two orders of magnitude faster. |
| title | Less Noise, More Signal: DRR for Better Optimizations of SE Tasks |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.21086 |