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

## TL;DR

This paper explores the potential of machine learning techniques to improve the accuracy of technical debt estimation in software projects, aiming for more precise and data-driven assessments.

## Contribution

It introduces a preliminary approach using simple regression models to enhance technical debt estimation accuracy compared to existing tools like SonarQube.

## Key findings

- Current techniques can be improved for better accuracy.
- Machine learning shows promise for more precise estimates.
- Early results indicate potential for future advancements.

## 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.