Identification of Co-Changed Classes in Software Applications Using Software Quality Attributes

Abstract

When changes are made to software applications often, defects can occur in software applications, and eventually leads to expensive operational faults. Comprehensive testing is not feasible with the limited time and resources available. There is a need for test case selection and prioritization so that testing can be completed with maximum confidence in a minimum time. Advance knowledge of co-changed classes in software applications can be very useful during the software maintenance phase. In this article, the authors have proposed a co-change prediction model based upon the combination of structural code measures and dynamic revision history from change repository. Univariate analysis is applied to identify the useful measures in co-change identification. The proposed model is validated using eight open source software applications. The results are promising and indicate that they can be very beneficial in maintenance of software applications.