On the Uncertainty of Technical Debt Measurements

Izurieta, Clemente; Griffith, Isaac; Reimanis, Derek; Luhr, Rachael

doi:10.1109/icisa.2013.6579461

Cited by 11 publications

(10 citation statements)

References 10 publications

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“…One reason could be the complexity/time required to implement the methods and the second reason about the comparability of the metrics defined. Furthermore, Izurieta et al [46] note that it can be difficult to compare alternative TD measurements methods due to missing ground truth and the uncertainties of the measurement process. One of the earliest studies to compare metrics for TD identification was the study by Zazworka et al [11], comparing four alternative methods across Griffith et al [50] compared ten releases of ten open source systems with three methods of TD identification ((i) SonarQube TD plug-in, (ii) a method based on TD identification using a cost model based on detected violations, and (iii) and one method defining design disharmonies to derive issues in quality).…”

Section: Related Workmentioning

confidence: 99%

Comparing maintainability index, SIG Method, and SQALE for technical debt identification

Strečanský

Chren

Rossi

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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ere are many definitions of software Technical Debt (TD) that were proposed over time. While many techniques to measure TD emerged in recent times, there is still not a clear understanding about how different techniques compare when applied to software projects. e goal of this paper is to shed some light on this aspect, by comparing three techniques about TD identification that were proposed over time: (i) the Maintainability Index (MI), (ii) SIG TD models, and (iii) SQALE analysis. Considering 20 open source Python libraries, we compare the TD measurements time series in terms of trends and evolution according to different sets of releases (major, minor, and micro), to see if the perception of practitioners about TD evolution could be impacted. While all methods report generally growing trends of TD over time, there are different patterns. SQALE reports more periods of steady states compared to MI and SIG TD. MI is the method that reports more repayments of TD compared to the other methods. SIG TD and MI are the models that show more similarity in the way TD evolves, while SQALE and MI are less similar. e implications are that each method gives slightly a different perception about TD evolution.

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Section: Related Workmentioning

confidence: 99%

Comparing maintainability index, SIG Method, and SQALE for technical debt identification

Strečanský

Chren

Rossi

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

show abstract

“…e SIG TD models were reimplemented based on the information available in the papers published about the models (e.g., [33]). Either in our or the adopted implementation, there might be some inconsistencies or issues in the calculations of the metrics, being difficult to have some tests for TD ground truth [46].…”

Section: Internal Validitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Comparing Maintainability Index, SIG Method, and SQALE for Technical Debt Identification

Strečanský

Chren

Rossi

2020

Scientific Programming

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There are many definitions of software Technical Debt (TD) that were proposed over time. While many techniques to measure TD emerged in recent times, there is still not a clear understanding about how different techniques compare when applied to software projects. The goal of this paper is to shed some light on this aspect, by comparing three techniques about TD identification that were proposed over time: (i) the Maintainability Index (MI), (ii) SIG TD models, and (iii) SQALE analysis. Considering 20 open source Python libraries, we compare the TD measurements time series in terms of trends and evolution according to different sets of releases (major, minor, and micro), to see if the perception of practitioners about TD evolution could be impacted. While all methods report generally growing trends of TD over time, there are different patterns. SQALE reports more periods of steady states compared to MI and SIG TD. MI is the method that reports more repayments of TD compared to the other methods. SIG TD and MI are the models that show more similarity in the way TD evolves, while SQALE and MI are less similar. The implications are that each method gives slightly a different perception about TD evolution.

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“…Izurieta et al (Izurieta et al, 2013) uses Nugroho et al (Nugroho et al, 2011) to exemplify the methodology.…”

Section: Rq3: Are There Any Empiricalmentioning

confidence: 99%

Automated Measurement of Technical Debt: A Systematic Literature Review

Khomyakov

Makhmutov

Mirgalimova

et al. 2019

Proceedings of the 21st International Conference on Enterprise Information Systems

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Background: Technical Debt (TD) is a quite complex concept that includes several aspect of software development. Often, people talk about TD as the amount of postponed work but this is just a basic approximation of the concept that includes many aspects that are technical and managerial. If TD is managed properly, it can provide a huge advantage but it can also make projects unmaintainable, if not. Therefore, being able of measuring TD is a very important aspect for a proper management of the development process. However, due to the complexity of the concept and the different aspects that are involved, such measurement it not easy and there are several different approaches in literature. Goals: This work aims at investigating the existing approaches to the measurement and the analysis of TD focusing on quantitative methods that could also be automated. Method: The Systematic Literature Review (SLR) approach was applied to 331 studies obtained from the three largest digital libraries and databases. Results: After applying all filtering stages, 21 papers out of 331 were selected and deeply analyzed. The majority of them suggested new approaches to measure TD using different criteria not built on top of existing ones. Conclusions: Existing studies related to the measurement of TD were observed and analyzed. The findings have shown that the field is not mature and there are several models that have almost no independent validation. Moreover few tools for helping to automate the evaluation process exist.

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On the Uncertainty of Technical Debt Measurements

Cited by 11 publications

References 10 publications

Comparing maintainability index, SIG Method, and SQALE for technical debt identification

Comparing maintainability index, SIG Method, and SQALE for technical debt identification

Comparing Maintainability Index, SIG Method, and SQALE for Technical Debt Identification

Automated Measurement of Technical Debt: A Systematic Literature Review

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