Using deep temporal convolutional networks to just-in-time forecast technical debt principal

Technical debt is a widely used metaphor to summarize all the consequences of poorly written code. Managing technical debt is important for software developers to allow adequate planning for software maintenance and improvement activities, such as refactoring and preventing system degradation. Several studies in the literature investigate the identification of technical debt and its consequences. This work aims to explore a deep learning approach to just-in-time predict the impact on technical debt when changes are performed on the source code. In this way the developer can work better, trying to improve the quality of the code that is being modified. Knowing what the TD trend will be in just-in-time source code with the change made is the key to avoiding a project taking a long time to remediate or improve. The model exploits the knowledge of quality and ad-hoc process metrics evolution over time. To validate the approach, a large dataset, including metrics evaluated from commits of ten Java software projects, was built. The results obtained show the effectiveness of the proposed approach in predicting the Technical Debt accumulation within the source code.