Detecting Neglected Conditions in Software through Data Mining

Recent studies have shown that neglected conditions are common and can have dramatic consequences to the operations of a Software defects. Neglected conditions in software can compromise the accuracy of a software product or even cause global disruptions to the quality of a Product. Several solutions have been proposed. However, these solutions share a common limitation: they are based on rules which need to be known beforehand. Violations of these rules are deemed to the problem of detecting the neglected conditions. As policies typically differ among different software’s, these approaches are limited in the scope of mistakes they can detect. In this paper, we address the problem of detecting neglected conditions in software using data mining technique. We apply association rules mining to the program files of the software product across an administrative domain to discover local, software product-specific policies. Deviations from these local policies are potential for detecting the neglected conditions. In this evaluation, we focused on three aspects of the configurations: syntax, interfaces and function calls. Here the advanced data mining techniques is used to discover implicit conditional rules in a code base and to discover rule violations that indicate neglected conditions. More interestingly, our empirical study indicates that our approaches are able to discover ordering rules, which involve ordering of function calls, and the corresponding rule violations also.