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Context: The technical debt (TD) concept describes a tradeoff between short-term and long-term goals in software development. While it is highly useful as a metaphor, it has utility beyond the facilitation of discussion, to inspire a useful set of methods and tools that support the identification, measurement, monitoring, management, and payment of TD. Objective: This study focuses on the identification of TD. We evaluate human elicitation of TD and compare it to automated identification. Method: We asked a development team to identify TD items in artifacts from a software project on which they were working. We provided the participants with a TD template and a short questionnaire. In addition, we also collected the output of three tools to automatically identify TD and compared it to the results of human elicitation. Results: There is little overlap between the TD reported by different developers, so aggregation, rather than consensus, is an appropriate way to combine TD reported by multiple developers. The tools used are especially useful for identifying defect debt but cannot help in identifying many other types of debt, so involving humans in the identification process is necessary. Conclusion: We have conducted a case study that focuses on the practical identification of TD, one area that could be facilitated by tools and techniques. It contributes to the TD landscape, which depicts an understanding of relationships between different types of debt and how they are best discovered.
Zazworka et al. (Sun,) studied this question.
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