Self-learning Change-prone Class Prediction

Software change-prone class prediction can enhance software decision making activities during software maintenance (e.g., resource allocating). Many change-prone class prediction approaches have been proposed and most are effective in interversion prediction within a project. These approaches usually build a supervised prediction model by learning from historical labeled dataset. However, a major challenge which remains is that this typical change-prone prediction setting cannot be used for new projects or projects with limited historical data. To address this challenge, we propose to tackle this task by adopting a novel prediction method which has not been used in changeprone prediction, namely self-learning method. The key idea of the self-learning method is to enable the change-prone prediction on new projects or projects with limited historical dataset by learning from itself. In this paper, we apply a state-of-art selflearning method, CLAMI, to change-prone prediction. In addition, we propose a novel self-learning approach CLAMI+ by extending CLAMI. The experiments among 14 open source projects show that the self-learning methods achieve comparable results to four typical inter-version baselines and the proposed CLAMI+ slightly improves the CLAMI method on average. Keywords-software maintenance; change-prone prediction; self-learning; empirical software engineering