Abstract
Technical debt management is a critical activity that is gaining the attention of both practitioners and researchers. Several tools providing automatic support for technical debt management have been introduced over the last years. SonarQube is one of the most widely applied tools to automatically measure technical debt in software systems. SonarQube has been adopted to quantify the diffuseness of technical debt in projects of the Apache Software Foundation ecosystem. Lenarduzzi et al. [1] found that the vast majority of technical debt issues in the code are code smells and that, surprisingly, developers tend to take more time to remove severe issues than the less-severe ones. While this study provides very interesting insights both for researchers and practitioners interested in technical debt management, we identified some major limitations that could have led to results that do not perfectly reflect reality. This study aims to address such limitations by presenting a differentiated replication study. Our findings have pointed out significant differences with the reference work. The results show that technical debt issues appear much more rarely than what the reference work reported.In this study, we implemented a new methodology to calculate the diffuseness of SonarQube issues at project and commit level, based on the reconstruction of the SonarQube quality profile in order to understand how the quality profile has evolved and to compare the number of active rules per category and severity level with the respective number of issues found. The results show that over 50% of rules active in the quality profile, are Code Smell rules and that over 90% of the issues belong to Code Smell category. Furthermore, analyzing the life span of the issues, we found that developers take into account the level of severity of the issues only for the Bug category, thus fixing the issues starting from the most severe, which is not the case for the other categories.
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