Analyzing Technical Debt Identification Strategies

• Isabela Oliveira Pontifical Catholic University of Minas Gerais
• Humberto T. Marques-Neto Pontifical Catholic University of Minas Gerais
• Laerte Xavier Pontifical Catholic University of Minas Gerais

DOI: https://doi.org/10.5753/vem.2020.14523

Abstract

Technical Debt (TD) is a constant in software development. To deal with the pressure of fast deliveries, developers constantly compromise the quality of the delivered system, postponing TD payment to later stages. In order to reduce its negative effects, several solutions are proposed in the literature for the identification, management and payment of these debts. Among them, we highlight the approaches based on static analysis (code smells), or on code comments (self-admitted technical debt, or SATD). However, the real intersection between such approaches is not yet clear, nor are the characteristics of the debts best identified by each of them. In this work, we perform a comparison between both approaches through the application of SonarQube and SATDDetector in a dataset of 1,000 popular GitHub repositories. As a result, we observe that the intersection between them is approximately 19%, and that in 7% of the cases SonarQube is not able to identify SATDs. In addition, the debts identified by both approaches are related to larger files (in terms of lines of code), more complex (cyclomatic and cognitive complexity) and more smelly (code smells).

Keywords: Technical Debt, Self-Admitted Technical Debt, SonarQube, SATD

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