Software reliability forecasting by support vector machines with simulated annealing algorithms

• Published in: The Journal of systems and software Vol. 79; no. 6; pp. 747 - 755
• Main Authors: Pai, Ping-Feng, Hong, Wei-Chiang
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.06.2006; Elsevier Sequoia S.A
• Subjects: Algorithms; Forecasting techniques; Simulated annealing algorithms; Software reliability; Software reliability forecasting; Studies; Support vector machines; Support vector machines; Software reliability forecasting; Simulated annealing algorithms
• ISSN: 0164-1212; 1873-1228
• DOI: 10.1016/j.jss.2005.02.025

Support vector machines (SVMs) have been successfully employed to solve non-linear regression and time series problems. However, SVMs have rarely been applied to forecasting software reliability. This investigation elucidates the feasibility of the use of SVMs to forecast software reliability. Simulated annealing algorithms (SA) are used to select the parameters of an SVM model. Numerical examples taken from the existing literature are used to demonstrate the performance of software reliability forecasting. The experimental results reveal that the SVM model with simulated annealing algorithms (SVMSA) results in better predictions than the other methods. Hence, the proposed model is a valid and promising alternative for forecasting software reliability.