Combining and Adapting Software Quality Predictive Models by Genetic Algorithms

17th IEEE International Conference on Automated Software Engineering (ASE'02) p. 285
Combining and Adapting Software Quality Predictive Models by Genetic Algorithms
Danielle Azar, McGill University
Doina Precup, McGill University
Salah Bouktif, University of Montreal
Balazs Kegl, University of Montreal
Houari Sahraoui, University of Montreal

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ASE.2002.1115031
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Abstract
The goal of quality models is to predict a quality factor starting from a set of direct measures. Selecting an appropriate quality model for a particular software is a difficult, non-trivial decision. In this paper, we propose an approach to combine and/or adapt existing models (experts) in such way that the combined/adapted model works well on the particular system. Test results indicate that the models perform significantly better than individual experts in the pool.
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Citation: Danielle Azar, Doina Precup, Salah Bouktif, Balazs Kegl, Houari Sahraoui, "Combining and Adapting Software Quality Predictive Models by Genetic Algorithms," ase, p. 285, 17th IEEE International Conference on Automated Software Engineering (ASE'02), 2002

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