Better software analytics via “DUO”: Data mining algorithms using/used-by optimizers

• Published in: Empirical software engineering : an international journal Vol. 25; no. 3; pp. 2099 - 2136
• Main Authors: Agrawal, Amritanshu, Menzies, Tim, Minku, Leandro L., Wagner, Markus, Yu, Zhe
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2020; Springer Nature B.V
• Subjects: Algorithms; Compilers; Computer Science; Data mining; Empirical analysis; Engineering research; Interpreters; Miners; Model accuracy; Optimization; Prediction models; Programming Languages; Software; Software engineering; Software Engineering/Programming and Operating Systems; Software analytics; Evolutionary algorithms; Data mining; Optimization
• ISSN: 1382-3256; 1573-7616
• DOI: 10.1007/s10664-020-09808-9

This paper claims that a new field of empirical software engineering research and practice is emerging: data mining using/used-by optimizers for empirical studies, or DUO. For example, data miners can generate models that are explored by optimizers. Also, optimizers can advise how to best adjust the control parameters of a data miner. This combined approach acts like an agent leaning over the shoulder of an analyst that advises “ask this question next” or “ignore that problem, it is not relevant to your goals”. Further, those agents can help us build “better” predictive models, where “better” can be either greater predictive accuracy or faster modeling time (which, in turn, enables the exploration of a wider range of options). We also caution that the era of papers that just use data miners is coming to an end. Results obtained from an unoptimized data miner can be quickly refuted, just by applying an optimizer to produce a different (and better performing) model. Our conclusion, hence, is that for software analytics it is possible, useful and necessary to combine data mining and optimization using DUO.