Abstract

Technical debt presents sub-optimal choices made in development, which are beneficial in the short term but not in the long run. Consciously admitted debt, which is marked with a keyword, e.g., TODO, is called keyword-labeled self-admitted technical debt (KL-SATD). KL-SATD can lead to adverse effects in software development, e.g., to a rise in complexity within the developed software. We investigated the relationship between KL-SATD from source code comments and reports from the highly popular industrial program analysis tool SonarQube. The goal was to find which SonarQube metrics and issues are related to KL-SATD introduction and removal and how many KL-SATD in the context of an issue addresses that issue. We performed a study with 33 software repositories. We analyzed the changes in SonarQube reports (sqale index, reliability and security remediation metrics, and SonarQube issues) and the relationship to KL-SATD addition and removal with mixed model analysis. We manually annotated a sample to investigate how many KL-SATD comments are in the context of SonarQube issues and how many address them directly. KL-SATD is associated with a reduction in code maintainability measured with SonarQube’s sqale index. KL-SATD removal is associated with an increase in code maintainability (sqale index) and reliability measured with SonarQube’s reliability remediation effort. The introduction and removal of KL-SATD have a predominantly relationship with code smells, and not with vulnerabilities and bugs. Manual annotation revealed that 36% of KL-SATD comments are in the context of a SonarQube issue, but only 15% of the comment address an issue. This means that despite of statistical relationship between KL-SATD comments and SonarQube reports there is a large set of KL-SATD comments that are in areas that Sonarqube reports as clean or free of maintainability issues. KL-SATD introduction and removal are connected mainly to code smells, connecting them to maintainability rather than reliability or security. This is reinforced by the relationship with the sqale index, as well as the dominance of code smells in SonarQube issues. Many KL-SATD issues have characteristics going beyond static analysis tools and require future studies extending the capabilities of the current tools. As KL-SATD comments and SonarQube reports appear to have limited overlap, it suggests that they are complementary and both are needed for getting a comprehensive view coverage of code maintainability. The study also presents rules violations developers should be aware of regarding KL-SATD introduction and removal.