Source code-based defect prediction using deep learning and transfer learning

摘要

Ensuring the quality of software products is important for them to be successful. Discovering errors and fixing defective software modules early in the project lifecycle (e.g. in the testing phase) can save resources and enhance software quality. Developers should prioritize testing procedures and continuously maintain their software projects; however, when there are few instances of a new project, it is hard to build an accurate defect prediction model. Different information about software projects is available and can be utilized through open repositories. Developers can leverage the labeled defect information to build a defect prediction model. The abundance of historical software information in similar domains can assist in transferring the knowledge gained from training this information to other domains for cross-project defect prediction models. Deep learning is a promising machine learner. Deep Belief network (DBN) is a deep learning algorithm that can discover latent relationships between input features by training them through multi-hidden layers; however, it is difficult to build a good prediction model from a dataset with few modules or instances. In this research, we utilized auxiliary datasets to initialize a DBN model and transfer the obtained knowledge to train the DBN model using a source project in a cross-project combination. The expressive features generated from the DBN model are used to build a classical classifier from the source class label and test it on other target project instances. Our evaluation of 13 open Java projects from the PROMISE repository shows that our proposed model achieves improvements based on F-measures (3.6%, 4.9%, and 5.1%) for the three settings of the DBN model measured against the best used benchmark model of TCA/TCA+ techniques. Moreover, T_DBN and DBN_Only models achieve improvement in terms of F-measure by (11.1% and 6.2%) against the best used benchmark model of TCA/TCA+ on Relink validation dataset.