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Technical Debt Prioritization: State of the Art. A Systematic Literature Review

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arxiv 1904.12538 v2 pith:LQ2X4WJZ submitted 2019-04-29 cs.SE

Technical Debt Prioritization: State of the Art. A Systematic Literature Review

classification cs.SE
keywords debttechnicaldifferentprioritizationfactorsproposedresearchsoftware
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Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations.

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  1. Reducing Labeling Effort in Architecture Technical Debt Detection through Active Learning and Explainable AI

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    Combining keyword pre-filtering with Breaking-Ties active learning labels 51% of a Jira dataset to detect architecture technical debt at F1 0.72; domain experts prefer LIME over SHAP for explaining predictions.