Year: 2024
Type: article
Abstract: Self-admitted technical debts (SATDs) refer to a solution in software development that selects suboptimal solutions to meet the current requirements and are intentionally introduced and documented by developers. SATDs in issue-tracking systems are a complement to those within source code comments. The effective identification of SATDs is crucial for software quality assurance and maintenance. Current studies focus on whether issue sections contain debt, but overlook specific SATD types. Meanwhile, they lack solutions for the challenge that SATD features are hard to learn due to the scarcity of instances containing SATDs. To address these problems, we propose a novel method, which is a weighted prompt tuning to identify SATDs, called WPTD. Specifically, WPTD employs a weighted prompt tuning to adapt the model with few-shot samples for insufficient training data. Moreover, to improve the performance of the model, WPTD constructs an SATD verbalizer by extracting keywords through mutual information and refining it with prior contextual information. Furthermore, it also improves SATD representation by extracting weights using the chi-square method and integrating them into the text. Finally, to reduce bias, WPTD computes the average score of results as final predicted distributions. We conduct comprehensive experiments on seven projects and the results show that our method significantly outperforms baseline approaches. In addition, we summarize the project-specific keywords, which can help developers better understand SATDs.
Source: Electronics
Authors: Jiaojiao Yu, Hao Tian, Ruiheng Li, Qiankun Zuo, Yi Di
Institution: Hubei University Of Economics
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Awards: G6620462366, G6740996341
OpenAlex ID: W4404827250