Simulated annealing for improving software quality prediction

Salah Bouktif, Houari Sahraoui, Giuliano Antoniol

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)1595931864, 9781595931863
Publication statusPublished - 2006
Externally publishedYes
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: Jul 8 2006Jul 12 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference


Other8th Annual Genetic and Evolutionary Computation Conference 2006
Country/TerritoryUnited States
CitySeattle, WA


  • Bayesian Classifiers
  • Expertise reuse
  • Predictive models
  • Simulated annealing
  • Software quality

ASJC Scopus subject areas

  • General Engineering


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