Characterizing Software Maintainability in Issue Summaries using a Fuzzy Classifier

Despite the importance of software maintainability in the life cycle of software systems, accurate measurement remains difficult to achieve. Previous work has shown how bug reports can be classified by expressed quality concerns which can give insight into maintainability across domains and over time. However, the amount of manual effort required to produce such classifications limits its usage. In this paper, we build a fuzzy classifier with linguistic patterns to automatically map issue summaries into the seven subgroup SQ classifications provided in a software maintainability ontology. We investigate how long it takes to generate a stable set of rules and evaluate the performance of the rule set on both rule generating and nonrule generating projects. The results validate the generalizability of the fuzzy classifier in correctly and automatically identifying the subgroup SQ classifications from given issue summaries. This provides a building block for analyzing project maintainability on a larger scale.