Improving the detection of technical debt in Java source code with an enriched dataset

• URL: http://arxiv.org/abs/2411.05457v1
• Date: Fri, 08 Nov 2024 10:12:33 GMT
• Title: Improving the detection of technical debt in Java source code with an enriched dataset
• Authors: Nam Le Hai, Anh M. T. Bui, Phuong T. Nguyen, Davide Di Ruscio, Rick Kazman,
• Abstract summary: Technical debt (TD) is the additional work and costs that emerge when developers opt for a quick and easy solution to a problem. Recent research has focused on detecting Self-Admitted Technical Debts (SATDs) by analyzing comments embedded in source code. We curated the first ever dataset of TD identified by code comments, coupled with its associated source code.
• Score: 12.07607688189035
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• Abstract: Technical debt (TD) is a term used to describe the additional work and 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 self-admitted comments are a useful tool for identifying technical debts, most of the existing approaches focus on capturing crucial tokens associated with various categories of TD, neglecting the rich information embedded within the source code itself. Recent research has focused on detecting SATDs by analyzing comments embedded in source code, and there has been little work dealing with technical debts contained in the source code. To fill such a gap, in this study, through the analysis of comments and their associated source code from 974 Java projects hosted in the Stack corpus, we curated the first ever dataset of TD identified by code comments, coupled with its associated source code. Through an empirical evaluation, we found out that the comments of the resulting dataset help enhance the prediction performance of state-of-the-art SATD detection models. More importantly, including the classified source code significantly improves the accuracy in predicting various types of technical debt. In this respect, our work is two-fold: (i) We believe that our dataset will catalyze future work in the domain, inspiring various research issues related to the recognition of technical debt; (ii) The proposed classifiers may serve as baselines for other studies on the detection of TD by means of the curated dataset.

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