Simulated annealing for improving software quality prediction

  • Authors:

  • Salah Bouktif;Houari Sahraoui;Giuliano Antoniol

  • Affiliations:

  • École Polytechnique de Montréal, Montréal (Québec), Canada;University of Montreal, Montréal (Québec), Canada;École Polytechnique de Montréal, Montréal (Québec), Canada

  • Venue:
  • Proceedings of the 8th annual conference on Genetic and evolutionary computation
  • Year:
  • 2006

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Abstract

In this paper, we propose an approach for the combination and adaptation of software quality predictive models. Quality models are decomposed into sets of expertise. The approach can be seen as a search for a valuable set of expertise that when combined form a model with an optimal predictive accuracy. Since, in general, there will be several experts available and each expert will provide his expertise, the problem can be reformulated as an optimization and search problem in a large space of solutions.We present how the general problem of combining quality experts, modeled as Bayesian classifiers, can be tackled via a simulated annealing algorithm customization. The general approach was applied to build an expert predicting object-oriented software stability, a facet of software quality. Our findings demonstrate that, on available data, composed expert predictive accuracy outperforms the best available expert and it compares favorably with the expert build via a customized genetic algorithm.