On the Technical Debt Prioritization and Cost Estimation with SonarQube tool

Abstract

Software developers commonly faced with situations to compromise internal quality ta=hat achieve short term goals, e.g. time-tomarket. In software engineering, such compromises are described with Technical Debt (TD) concept. TD implies cost of additional rework| usually expressed as eort|and when the code is compromised, it is called code debt. One of the most popular tools for identifying TD items and estimating eort for solving them is SonarQube. However, there is still a need for empirical validations of remediation times that SonarQube estimates. The objective of this research is to validate the usefulness of the tool for junior developers in terms of understanding SonarQube's categorization of the criticality levels of TD issues and the accuracy of estimated remediation times. We designed and conducted an empirical study with 185 students in a context of university software engineering courses in Finland and Serbia. The estimates provided by SonarQube are more pessimistic, i.e. only in 3% of cases the actual xing time was higher that the estimated one. Our results also indicate that participants' perception of the criticality levels of the TD issues is misaligned with SonarQube's classication and prioritization.