On the Effectiveness of Concern Metrics to Detect Code Smells: An Empirical Study

Traditional software metrics have been used to evaluate the maintainability of software programs by supporting the identification of code smells. Recently, concern metrics have also been proposed with this purpose. While traditional metrics quantify properties of software modules, concern metrics quantify concern properties, such as scattering and tangling. Despite being increasingly used in empirical studies, there is a lack of empirical knowledge about the effectiveness of concern metrics to detect code smells. This paper reports the results of an empirical study to investigate whether concern metrics can be useful indicators of three code smells, namely Divergent Change, Shotgun Surgery, and God Class. In this study, 54 subjects from two different institutions have analyzed traditional and concern metrics aiming to detect instances of these code smells in two systems. The study results indicate that, in general, concern metrics support developers detecting code smells. In particular, we observed that (i) the time spent in code smell detection is more relevant than the developers’ expertise; (ii) concern metrics are clearly useful to detect Divergent Change and God Class; and (iii) the concern metric Number of Concerns per Component is a reliable indicator of Divergent Change.