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

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Abstract

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.

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Notes

  1. This definition has been generalized with respect to Boyd and Vandenberghe (2004), not to be restricted to continuous optimization problems, where

  2. The optimization variable is usually identified by the symbol x, and the inequality and equality constraints are frequently identified by the symbols g and h in the optimization literature. However, we use the symbols a, g and \(g^{\prime \prime }\) here to avoid confusion with the terminology used in data mining, which is introduced later in this section.

  3. Any maximization problem can be re-written as a minimization problem.

  4. Using a process called “engineering judgement”; i.e. guessing.

  5. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html, accessed 30 November 2018.

  6. Total time to process 20 repeated runs across multiple subsets of the data, for multiple data sets.

  7. In “order effects experiments”, the training data is re-arranged at random before running the learner again. In such experiments, a result is “unstable” if the learned model changes just by re-ordering the training data.

  8. The Gini index measures class diversity after a set of examples is divided by some criteria – in this case, the values of an attribute.

  9. The distance to of a predictor’s performance to the “utopia” point of recall= 1, false alarms= 0.

  10. E.g. in Python: scikit-learn and DEAP (Pedregosa et al. 2011; Rainville et al. 2012). E.g. in Java: Weka and (jMetal or SMAC) (Hall et al. 2009; Durillo and Nebro 2011; Hutter et al. 2011).

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Acknowledgments

Earlier work ultimately leading to the present one was inspired by the NII Shonan Meeting on Data-Driven Search-based Software Engineering (goo.gl/f8D3EC), December 11-14, 2017. We thank the organizers of that workshop (Markus Wagner, Leandro L. Minku, Ahmed E. Hassan, and John Clark) for their academic leadership and inspiration. Dr Menzies’ work was partially supported by NSF grant No. 1703487. Dr Minku’s work was partially supported by EPSRC grant Nos. EP/R006660/1 and EP/R006660/2. Dr Wagner’s work was partially supported by the ARC grant DE160100850.

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Correspondence to Leandro L. Minku.

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Communicated by: Yasutaka Kamei and Andy Zaidman

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Agrawal, A., Menzies, T., Minku, L.L. et al. Better software analytics via “DUO”: Data mining algorithms using/used-by optimizers. Empir Software Eng 25, 2099–2136 (2020). https://doi.org/10.1007/s10664-020-09808-9

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