Development and Adoption of SATD Detection Tools: A State-of-practice Report

TL;DR

This paper reviews the current state of SATD detection tools, highlighting their importance, common challenges, and the need for improved sustainability and collaboration to enhance software quality management.

Contribution

It provides a systematic review of SATD detection tools, identifies key challenges, and proposes best practices for improving their development and adoption.

Findings

Many SATD tools face obsolescence and poor maintenance.

Limited platform compatibility hinders widespread adoption.

Recommendations for adopting FAIR principles and collaboration are discussed.

Abstract

Self-Admitted Technical Debt (SATD) refers to instances where developers knowingly introduce suboptimal solutions into code and document them, often through textual artifacts. This paper provides a comprehensive state-of-practice report on the development and adoption of SATD detection tools. Through a systematic review of the available literature and tools, we examined their overall accessibility. Our findings reveal that, although SATD detection tools are crucial for maintaining software quality, many face challenges such as technological obsolescence, poor maintenance, and limited platform compatibility. Only a small number of tools are actively maintained, hindering their widespread adoption. This report discusses common anti-patterns in tool development, proposes corrections, and highlights the need for implementing Findable, Accessible, Interoperable, and Reusable (FAIR) principles and fostering greater collaboration between academia and industry to ensure the sustainability and efficacy of these tools. The insights presented here aim to drive more robust management of technical debt and enhance the reliability of SATD tools.