Selecting optimal software code descriptors—The case of Java

Over the last 25 years, a considerable proliferation of software metrics and a plethora of tools have emerged to extract them. While this is indeed positive concerning the previous situations of limited data, it still leads to a significant problem arising both from a theoretical and a practical standpoint. From a theoretical perspective, several metrics are likely to result in collinearity, overfitting, etc. From a practical perspective, such a set of metrics is difficult to manage and companies, especially small ones, may feel overwhelmed and unable to select a viable subset of them. Still, so far it has not been fully understood what is a viable subset of metrics suitable to properly manage software projects and products. In this paper, we attempt to address this issue. We focus on the case of programs written in Java and we consider classes and methods. We use Sammon error as a measure of the similarity of metrics. Utilizing both Particle Swarm Optimization and Genetic Algorithm, we adapted a method for the identification of a viable subset of such metrics that could solve the mentioned problem. Furthermore, we experiment with our approach on 800 projects coming from GitHub and validate the results on 200 projects. With the proposed method we got optimal subsets of software engineering metrics. These subsets gave us low values of Sammon error at more than 70\% at class and method levels on a validation dataset.