Automated software fault-proneness prediction based on fuzzy inference system

The identification of a module´s fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between inspection metrics and module´s fault-proneness, hiding in the observed data, has been focused by very researches. In this paper, we propose the use of a fuzzy inference system for this purpose. In order to empirically evaluate the effectiveness of proposed approach, we apply it on empirical data published by Ebenau and NASA´s Metrics Data Program data repository, respectively. Experiments results confirm that proposed approach is very effective for establishing relationship between inspection metrics and fault-proneness, and that its implementation don´t require neither extra cost nor expert´s knowledge, and it is completely automated. Novel approach can provide software project managers with reasonably suggestion and much-needed insights.