A further analysis on the use of Genetic Algorithm to configure Support Vector Machines for inter-release fault prediction

Authors:
F. Sarro;S. Di Martino;F. Ferrucci;C. Gravino
Affiliations:
University of Salerno, Fisciano (SA), Italy;University of Naples "Federico II", Naples, Italy;University of Salerno, Fisciano (SA), Italy;University of Salerno, Fisciano (SA), Italy
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

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Abstract

Some studies have reported promising results on the use of Support Vector Machines (SVMs) for predicting fault-prone software components. Nevertheless, the performance of the method heavily depends on the setting of some parameters. To address this issue, we investigated the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs to be used for inter-release fault prediction. In particular, we report on an assessment of the method on five software systems. As benchmarks we exploited SVMs with random and Grid-search configuration strategies and several other machine learning techniques. The results show that the combined use of GA and SVMs is effective for inter-release fault prediction.