The nature of statistical learning theory
The nature of statistical learning theory
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Tabu Search
Modern Information Retrieval
Case Studies for Method and Tool Evaluation
IEEE Software
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Metrics Are Fitness Functions Too
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
How to measure success of fault prediction models
Fourth international workshop on Software quality assurance: in conjunction with the 6th ESEC/FSE joint meeting
Applying machine learning to software fault-proneness prediction
Journal of Systems and Software
Predicting defect-prone software modules using support vector machines
Journal of Systems and Software
Adapting a fault prediction model to allow inter languagereuse
Proceedings of the 4th international workshop on Predictor models in software engineering
Application of support vector machine to predict fault prone classes
ACM SIGSOFT Software Engineering Notes
Application of Random Forest in Predicting Fault-Prone Classes
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Journal of Systems and Software
How effective is Tabu search to configure support vector regression for effort estimation?
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
Software defect prediction using fuzzy support vector regression
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
WSEAS TRANSACTIONS on SYSTEMS
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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In some studies, Support Vector Machines (SVMs) have been turned out to be promising for predicting fault-prone software components. Nevertheless, the performance of the method depends on the setting of some parameters. To address this issue, we propose the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs parameters that allows us to obtain optimal prediction performance. The approach has been assessed carrying out an empirical analysis based on jEdit data from the PROMISE repository. We analyzed both the inter- and the intra-release performance of the proposed method. As benchmarks we exploited SVMs with Grid-search and several other machine learning techniques. The results show that the proposed approach let us to obtain an improvement of the performance with an increasing of the Recall measure without worsening the Precision one. This behavior was especially remarkable for the inter-release use with respect to the other prediction techniques.