[Submitted on 27 Mar 2025 (v1), last revised 22 Jul 2026 (this version, v2)]

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Abstract: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) effect, a frequently observed
empirical effect indicating where effective optimization might be two orders of magnitude faster.
The DRR effect is an empirical observation, not some universal law. With the datasets used in this study, we can comment on SE tasks expressible as classification or regression tasks (where regression may be exploring $N \ge 1$ goals). These tasks include software configuration optimization; cloud resource management; project health prediction (commits, PRs, issues); and process models (effort/defect/schedule estimation). Given the prevalence of the DRR effect in this sample, we conjecture it might hold for other SE tasks, but that is a matter for further research. Hence we recommend practitioners check for high DRR before deploying expensive optimization methods. This simple diagnostic could save orders of magnitude in computational cost.\BLACK

Submission history

From: Andre Lustosa Motta [view email]
[v1] Thu, 27 Mar 2025 02:02:06 UTC (1,899 KB)
[v2] Wed, 22 Jul 2026 23:31:26 UTC (684 KB)