The Correspondence Between Software Quality Models and Technical Debt Estimation Approaches

Abstract

Technical debt has recently become a major concern in the software industry. While it has been shown that technical debt has an adverse effect on the quality of a software system, there has been little work to explore this relationship. Further, with the growing number of approaches to estimate the technical debt principal of a software system, there is a dearth of work to empirically validate the relationship between technical debt scores produced by practical tools against established theoretical quality models. We conducted a case study across 10 releases of 10 open source systems in order to evaluate three proposed methods of technical debt principal estimation. The evaluation compares each technique against an external quality model. We found that only one estimation technique had a strong correlation to the quality attributes reusability and understand ability. In a multiple linear regression analysis we also found that a different estimation technique had a significant relationship to the quality attributes effectiveness and functionality. These results indicate that it is important that industry practitioners, ensure that the technical debt estimate they employ accurately depicts the effects of technical debt as viewed from their quality model.