Combining and adapting software quality predictive models by genetic algorithms

Azar, D.; Precup, D.; Boukif, S.; Kegl, B.; Sahraoui, H.

Combining and adapting software quality predictive models by genetic algorithms

Azar, D.; Precup, D.; Boukif, S.; Kegl, B.; Sahraoui, H.

URI: http://hdl.handle.net/10725/5371

URL: http://ieeexplore.ieee.org/abstract/document/1115031/

DOI: http://dx.doi.org/10.1109/ASE.2002.1115031

Date: 2017-03-15

Terms of Use: This item is made available under the terms and conditions applicable to " Conference Paper / Proceeding ", as set forth at: http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php

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.

Citation:

Azar, D., Precup, D., Bouktif, S., Kégl, B., & Sahraoui, H. (2002). Combining and adapting software quality predictive models by genetic algorithms. In Automated Software Engineering, 2002. Proceedings. ASE 2002. 17th IEEE International Conference on (pp. 285-288). IEEE.