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

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.Amritanshu Agrawal, Tim Menzies, Leandro L. Minku, Markus Wagner and Zhe Y

Exploiting generative models for performance predictions of 3D car designs

Algorithms and the Foundations of Software technolog

Dynamic selection of evolutionary algorithm operators based on online learning and fitness landscape metrics

Abstract. Self-adaptive mechanisms for the identification of the most suitable variation operator in Evolutionary meta-heuristics rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel Adaptive Operator Selection mechanism which uses a set of four Fitness Landscape Analysis techniques and an online learning al-gorithm, Dynamic Weighted Majority, to provide more detailed infor-mations about the search space in order to better determine the most suitable crossover operator on a set of Capacitated Arc Routing Prob-lem (CARP) instances. Extensive comparison with a state of the art approach has proved that this technique is able to produce comparable results on the set of benchmark problems.