A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components

Author:

Di Martino Sergio,Ferrucci Filomena,Gravino Carmine,Sarro Federica

Publisher

Springer Berlin Heidelberg

Reference30 articles.

1. Arisholm, E., Briand, L., Johannessen, B.: Data mining techniques, candidate measures and evaluation methods for building practically useful fault-proneness prediction models. Simula Research Laboratory Technical Report, 2008-06

2. Arisholm, E., Briand, L., Johannessen, B.: A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Journal of Systems and Software 83, 2–17 (2010)

3. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)

4. Briand, L., Langley, T., Wiekzorek, I.: A Replicated Assessment and Comparison of Common Software Cost Modeling Techniques. In: Procs of the International Conference on Software Engineering, pp. 377–386. IEEE press, Los Alamitos (2000)

5. Corazza, A., Di Martino, S., Ferrucci, F., Gravino, C., Sarro, F., Mendes, E.: How Effective is Tabu Search to Configure Support Vector Regression for Effort Estimation? In: Procs of the International Conference on Predictive Models in Software Engineering, p. 4 (2010)

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