  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.
