Identifying Technical Debt through a Code Comment Mining Tool

Context: The software industry often has to deal with several challenges to deliver and maintain products, such as providing useful software with high quality, on time, and on the budget. This challenge is difficult, if not impossible, to overcome, and software engineers end up developing immature artifacts that cause unexpected delays and make the whole system difficult to maintain and evolve in the future. That is what the Software Engineering (SE) community now calls Technical Debts. Objective: The main goal of this paper is to propose an approach to support and automate the identification of different types of TD through code comment analysis, as well as to propose and evaluate the eXcomment. Method: We carry out a proof-of-concept study in two Open Source Projects: ArgoUML and JFreeChart. Results: Our findings indicate that the eXcomment make it possible to select a list of suitable comments to support TD identification automatically. The study provided new evidence on how software engineers can use code comments to detect and classify TD items automatically. Conclusion: This work contributes to bridge the gap between the TD identification area and code comment analysis, successfully using code comments to detect several types of TD.