Abstract
Repaying all technical debt (TD) in a system may be unviable, as there is typically a shortage of resources allocated for TD repayment activities. Therefore, TD prioritization is essential to best allocate such limited resources. Fortunately, one can utilize a static code analysis tool, such as SonarQube, to aid in expediting the TD prioritization process.
Given that SonarQube is one of the most utilized tools in the context of TD, this exploratory case study seeks to explore how SonarQube-identified TD items are perceived and prioritized for repayment.
The study was designed, replicated, and conducted in four companies and a master’s level course, with a total of 89 participants. The participants were requested to select TD items to include for repayment under a resources constraint.
The results revealed that the overwhelming majority of participants prioritized TD by factoring in a TD item’s value and cost, a smaller number prioritized higher value TD items, and only one participant prioritized lower cost TD items. Furthermore, it was revealed that the value of a TD item is subjective and context-dependent, and the majority of participants perceive the cost estimations provided by SonarQube for repaying TD items to be reliable and trustworthy when prioritizing TD.
Based on the results, one can conclude that there is no silver bullet TD prioritization approach that addresses all of a developer’s objectives and needs. New TD prioritization approaches should be designed without concentrating on a specific prioritization perspective and should be independent of value estimation methods.