Technical Debt Prioritization: State of the Art. A Systematic Literature Review
Artikel i vetenskaplig tidskrift, 2020

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring of 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 of 557 unique papers published until 2019, following a consolidated methodology applied in software engineering. We included 44 primary studies.

Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and proposing optimization regarding different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We present 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 the important factors are and how to measure them. Consequently, we cannot consider current research conclusive. In this paper, we therefore outline different directions for necessary future investigations.

Technical Debt

Systematic Literature Review

Technical Debt Prioritization

Författare

Valentina Lenarduzzi

Lappeenrannan-Lahden teknillinen yliopisto (LUT)

Terese Besker

Chalmers, Data- och informationsteknik, Software Engineering

Davide Taibi

Tampereen Yliopisto

Antonio Martini

Chalmers, Data- och informationsteknik, Software Engineering

Francesca Arcelli Fontana

Universita' degli Studi di Milano-Bicocca

Journal of Systems and Software

0164-1212 (ISSN)

Vol. 171 110827

Ämneskategorier

Inbäddad systemteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1016/j.jss.2020.110827

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Senast uppdaterat

2021-02-17