Class Change Prediction by Incorporating Community Smell: An Empirical Study

Abstract

To adapt to changing software requirements, developers need to maintain and modify software through code changes. Predicting change-prone code can help developers to reduce the cost of software maintenance in advance. Prior work confirmed code smell intensity is a reliable metric for predicting change-prone classes. Community smell is a derivation of the concept of code smell in open-source software development community, it refers to poor communication and collaboration problems among developers. We add community smell to existing change prediction models, and propose a software class change prediction model integrating process metrics, code smell intensity metrics, anti-pattern metrics and community smell metrics, which takes into account the technicality and organizational aspects of software development. Experimental results demonstrate that when Multilayer Perceptron is used to build a change prediction model, community smell improves the baseline model by 4.4% and 31.5% in terms of [Formula: see text]-Measure and Recall. In addition, community smell improves baseline model performance to a greater extent in terms of Recall and Precision than code smell-related information.