Less Noise, More Signal: the DRR Effect for Better Optimizations of a Range of SE Tasks

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