Genetic granular classifiers in modeling software quality

Hyperbox classifiers are one of the most appealing and intuitively transparent classification schemes. As the name itself stipulates, these classifiers are based on a collection of hyperboxes--generic and highly interpretable geometric descriptors of data belonging to a given class. The hyperboxes translate into conditional statements (rules) of the form “if feature1 is in [a, b] and feature2 is in [d, f] and … and featuren is in [w, z] then class ω” where the intervals ([a, b], … , [w, z]) are the respective edges of the hyperbox. The proposed design process of hyperboxes comprises of two main phases. In the first phase, a collection of “seeds” of the hyperboxes is formed through data clustering (realized by means of the Fuzzy C-Means algorithm, FCM). In the second phase, the hyperboxes are “grown” (expanded) by applying mechanisms of genetic optimization (and genetic algorithm, in particular). We reveal how the underlying geometry of the hyperboxes supports an immediate interpretation of software data concerning software maintenance and dealing with rules describing a number of changes made to software modules and their linkages with various software measures (such as size of code, McCabe cyclomatic complexity, number of comments, number of characters, etc.).