Technical Debt Prediction

Technical debt prediction involves anticipating prob-lems and challenges in software projects. Because of lousy coding choices, these projects are often far from the optimal design. These issues can accumulate over time and affect long-term maintainability and stability. Numerous studies are available for predicting technical debt. For instance, some studies utilize static analysis tools like Pylint<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://pypi.org/project/pylint/, and others use dynamic code analysis tools like SZZ [1]. This study addresses AI predictions using data obtained from static analysis tools. This paper presents technical debt prediction using static code analysis data generated with SonarQube<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>https://docs.sonarsource.com!sonarqube/latest/ and Radon<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>https://pypi.org/project/radon/. It covers both short-term and long-term predictions by analyzing code quality metrics such as complexity, duplication, and adherence to standards. The study also integrates git commit classification to analyze commit history, providing valuable insights into the evolution and management of technical debt within the project.