From SATD Recognition to an Interpretation Method Based on the Dataset

Abstract

Technical debt describes a trade-off between short-term goals and long-term code quality during software development. Self-admitted technical debt (SATD), a type of technical debt, is intentionally introduced by developers. The existence of SATD is likely to leave hidden dangers for future changes in software systems, so identifying SATD is an essential task. Before this, many methods for recognizing SATD (such as pattern matching-based, natural language processing-based, text mining-based, etc.) have been proposed. This paper will present a pre-trained deep learning model to complete the SATD recognition task. An efficient deep learning model interpretation tool Captum can be used to understand the experimental results. At the same time, a new interpretation view is proposed for the matching-based model. Finally, combined with the research in this paper, reasonable suggestions are put forward for future SATD recognition tasks.