A Hybrid Algorithm to Extract Fuzzy Measures for Software Quality Assessment

Being able to assess software quality is essential as software is ubiquitous in every aspect of our dayto-day lives. In this paper, we build upon existing research and metrics for defining software quality and propose a way to automatically assess software quality based on these metrics. In particular, we show that the problem of assessing the quality of software can be viewed as a multi-criteria decisionmaking (MCDM) problem. In Multi-Criteria Decision Making (MCDM), decisions are based on several criteria that are usually conflicting and non-homogenously satisfied. We use non-additive (fuzzy) measures combined with the Choquet integral to solve MCDM problems for they allow to model and aggregate the levels of satisfaction of the several criteria of the problem at hand by considering their relationships. However, in practice, it is difficult to identify such fuzzy measures. An automated process is then necessary and can be used when sample data is available. We propose to automatically assess software by modeling experts’ decision process and extracting the fuzzy measure corresponding to their reasoning process: to do this, we use samples of the target experts’ decision as seed data to automatically extract the fuzzy measure corresponding to the experts’ decision process. In particular, we propose an algorithm to efficiently extract fuzzy measures that is a combination of the Bees algorithm and an interval constraint solver. Our experimental results show that we are able to improve some of the results obtained through previous approaches to automatic software quality assessment that used traditional machine learning techniques. c ©2013 World Academic Press, UK. All rights reserved.