Towards Surgically-Precise Technical Debt Estimation: Early Results and Research Roadmap

Abstract

The concept of technical debt has been explored from many perspectives but its precise estimation is still under heavy empirical and experimental inquiry. We aim to understand whether, by harnessing approximate, data-driven, machine-learning approaches it is possible to improve the current techniques for technical debt estimation, as represented by a top industry quality analysis tool such as SonarQube. For the sake of simplicity, we focus on relatively simple regression modelling techniques and apply them to modelling the additional project cost connected to the sub-optimal conditions existing in the projects under study. Our results shows that current techniques can be improved towards a more precise estimation of technical debt and the case study shows promising results towards the identification of more accurate estimation of technical debt.

arXiv comment6 pages

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• The Technical Debt Dataset
• Some SonarQube Issues have a Significant but SmallEffect on Faults and Changes. A large-scale empirical study
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• Exploring the Advances in Using Machine Learning to Identify Technical Debt and Self-Admitted Technical Debt