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

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.

[1]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[2]  Karim O. Elish,et al.  Predicting defect-prone software modules using support vector machines , 2008, J. Syst. Softw..

[3]  B. Kitchenham,et al.  Case Studies for Method and Tool Evaluation , 1995, IEEE Softw..

[4]  Elaine J. Weyuker,et al.  How to measure success of fault prediction models , 2007, SOQUA '07.

[5]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[6]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[7]  Filomena Ferrucci,et al.  A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components , 2011, PROFES.

[8]  Ian Witten,et al.  Data Mining , 2000 .

[9]  Lech Madeyski,et al.  Towards identifying software project clusters with regard to defect prediction , 2010, PROMISE '10.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[12]  Thomas J. Ostrand,et al.  \{PROMISE\} Repository of empirical software engineering data , 2007 .

[13]  Bruce Christianson,et al.  Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics , 2009, EANN.

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Emilia Mendes,et al.  How effective is Tabu search to configure support vector regression for effort estimation? , 2010, PROMISE '10.

[16]  Haruhiko Kaiya,et al.  Adapting a fault prediction model to allow inter languagereuse , 2008, PROMISE '08.

[17]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[18]  Lionel C. Briand,et al.  A systematic and comprehensive investigation of methods to build and evaluate fault prediction models , 2010, J. Syst. Softw..

[19]  Iker Gondra,et al.  Applying machine learning to software fault-proneness prediction , 2008, J. Syst. Softw..

[20]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.