Detection of Technical Debt in Java Source Code

TL;DR

This paper introduces a novel dataset of technical debt in Java source code identified through comments and code analysis, demonstrating improved detection accuracy and providing a resource for future research.

Contribution

The study creates the first dataset combining code comments and source code for technical debt detection in Java, enhancing detection accuracy and serving as a baseline for future work.

Findings

Including code improves detection accuracy

First dataset combining comments and code for TD

Classifiers serve as baselines for future research

Abstract

Technical debt (TD) describes the additional costs that emerge when developers have opted for a quick and easy solution to a problem, rather than a more effective and well-designed, but time-consuming approach. Self-Admitted Technical Debts (SATDs) are a specific type of technical debts that developers intentionally document and acknowledge, typically via textual comments. While these comments are a useful tool for identifying TD, most of the existing approaches focus on capturing tokens associated with various categories of TD, neglecting the rich information embedded within the source code. Recent research has focused on detecting SATDs by analyzing comments, and there has been little work dealing with TD contained in the source code. In this study, through the analysis of comments and their source code from 974 Java projects, we curated the first ever dataset of TD identified by code comments, coupled with its code. We found that including the classified code significantly improves the accuracy in predicting various types of technical debt. We believe that our dataset will catalyze future work in the domain, inspiring various research related to the recognition of technical debt; The proposed classifiers may serve as baselines for studies on the detection of TD.