An Overview and Comparison of Technical Debt Measurement Tools
Artikel i vetenskaplig tidskrift, 2021

There are numerous commercial tools and research prototypes that offer support for measuring technical debt. However, different tools adopt different terms, metrics, and ways to identify and measure technical debt. These tools offer diverse features, and their popularity / community support varies significantly. Therefore, (a) practitioners face difficulties when trying to select a tool matching their needs; and (b) the concept of technical debt and its role in software development is blurred. We attempt to clarify the situation by comparing the features and popularity of technical debt measurement tools, and analyzing the existing empirical evidence on their validity. Our findings can help practitioners to find the most suitable tool for their purposes, and researchers by highlighting the current tool shortcomings.


Software Quality

Source Code Analysis

Technical Debt


Paris C Avgeriou

Rijksuniversiteit Groningen

Davide Taibi

Tampereen Yliopisto

Apostolos Ampatzoglou

University of Macedonia

Francesca Arcelli Fontana

Universita' degli Studi di Milano-Bicocca

Terese Besker

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Testing, Requirements, Innovation and Psychology

Alexandros Chatzigeorgiou

University of Macedonia

Valentina Lenarduzzi

Tampereen Yliopisto

Antonio Martini

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Nasia Moschou

University of Macedonia

Ilaria Pigazzini

Universita' degli Studi di Milano-Bicocca

Nyyti Saarimaki

Tampereen Yliopisto

Darius Sas

Rijksuniversiteit Groningen

Saulo Soares de Toledo

Universitetet i Oslo

Angeliki Agathi Tsintzira

University of Macedonia

IEEE Software

0740-7459 (ISSN)

Vol. 38 3 61-71




Datavetenskap (datalogi)



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