AIRCC's International Journal of Computer Science and Information Technology

Jie Xu ¹, Danny Ho ², Luiz Fernando Capretz ¹

Affiliations
1 Department of Electrical and Computer Engineering, University of Western Ontario, London, Ontario, N6A 5B9, CA
2 NFA Estimation Inc., Richmond Hill, Ontario, CA

Abstract

Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value.

Keywords

Software Quality Estimation, Software Metrics, Regression, Neural Networks, Fuzzy Logic.

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