A Framework for Conditional Statement Technical Debt Identification and Description

TL;DR

This paper introduces a framework called SATDID that detects and describes hidden technical debt in conditional statements, especially when such debt isn't explicitly acknowledged in code comments, improving maintainability.

Contribution

The study presents a novel framework for identifying and automatically generating descriptive comments for hidden technical debt in code, focusing on conditional statements.

Findings

Achieved over 21% improvement in Precision

Achieved over 59% improvement in Recall

Generated comments with acceptable levels of understandability

Abstract

Technical Debt occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.35%, 59.36%, 31.78%, and 583.33% improvements in terms of Precision, Recall, F-1, and Bleu-4 scores, respectively. In addition, we conduct human evaluation to the SATD comments generated by SATDID. In 1-5 and 0-5 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively.