Comparing four approaches for technical debt identification

Abstract

Software systems accumulate technical debt (TD) when short-term goals in software development are traded for long-term goals (e.g., quick-and-dirty implementation to reach a release date versus a well-refactored implementation that supports the long-term health of the project). Some forms of TD accumulate over time in the form of source code that is difficult to work with and exhibits a variety of anomalies. A number of source code analysis techniques and tools have been proposed to potentially identify the code-level debt accumulated in a system. What has not yet been studied is if using multiple tools to detect TD can lead to benefits, that is, if different tools will flag the same or different source code components. Further, these techniques also lack investigation into the symptoms of TD “interest” that they lead to. To address this latter question, we also investigated whether TD, as identified by the source code analysis techniques, correlates with interest payments in the form of increased defect- and change-proneness. Comparing the results of different TD identification approaches to understand their commonalities and differences and to evaluate their relationship to indicators of future TD “interest.” We selected four different TD identification techniques (code smells, automatic static analysis issues, grime buildup, and Modularity violations) and applied them to 13 versions of the Apache Hadoop open source software project. We collected and aggregated statistical measures to investigate whether the different techniques identified TD indicators in the same or different classes and whether those classes in turn exhibited high interest (in the form of a large number of defects and higher change-proneness). The outputs of the four approaches have very little overlap and are therefore pointing to different problems in the source code. Dispersed Coupling and Modularity violations were co-located in classes with higher defect-proneness. We also observed a strong relationship between Modularity violations and change-proneness. Our main contribution is an initial overview of the TD landscape, showing that different TD techniques are loosely coupled and therefore indicate problems in different locations of the source code. Moreover, our proxy interest indicators (change- and defect-proneness) correlate with only a small subset of TD indicators

Supporting Technical Debt Cataloging with TD-Tracker Tool

“…TD-Tracker allows multiple TD identification tools to be used instead of only one of the mentioned and the adoption of different tools is strongly recommended, since they point to different problems in a software artifact [20]. The use of a single tool or single indicator (e.g., single code smells) will only in rare cases point to all important TD issues in a project.…”

“…In this case, one in charge of cataloging can postpone the task of fixing it. In previous work Vetro et al [19] analyzed the issues detected by FindBugs in two pools of similar small programs, Sonar [20].…”

An Open Tool for Assisting in Technical Debt Management

“…In the case of Findbugs, each release has to be compiled, so it is necessary to have installed additional tools to build the project, for example Maven and Gradle. Other metrics have been directly implemented, for instance, the probability of change and expected size of change as are defined in [7]. Data are collected for all the releases of the software so that metrics evolution can be analyzed.…”

“…Analyzers can be implemented using Java and R. Currently, two analyzers are available to use PMD [9] and Findbugs [11] to analyze projects. Other metrics have been directly implemented, for instance, the probability of change and expected size of change as are defined in [7]. All these implementations were done to demonstrate TEDMA integration capacity.…”

“…Also, there are some tools that provide different TD indexes [6]. It must be noted that it is not completely clear in literature which of the current metrics must be selected, or how they must be combined, since they might be indicators of different types of TD [7].…”