The Correspondence Between Software Quality Models and Technical Debt Estimation Approaches

Griffith, Isaac; Reimanis, Derek; Izurieta, Clemente; Codabux, Zadia; Deo, Ajay; Williams, Byron

doi:10.1109/mtd.2014.13

Cited by 29 publications

(22 citation statements)

References 18 publications

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“…For instance, metrics such as cyclomatic complexity, code duplication or low test coverage (i.e. the absence of sufficient code tests) have been widely used to predict software maintainability decay and, in turn, the TD of a software application [25,29,43]. In addition, code issues that can negatively affect the quality of a software, such as code violations or bugs, have also been studied for their impact on TD [31,65,69].…”

Section: Datasetmentioning

confidence: 99%

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

et al. 2021

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Technical debt (TD) describes quality compromises that can yield short-term benefits but may negatively affect the quality of software products in the long run. A wide range of tools and techniques have been introduced over the years in order for the developers to be able to determine and manage TD. However, being able to also predict its future evolution is of equal importance to avoid its accumulation, and, in turn, the unlikely event of making the project unmaintainable. Although recent research endeavors have showcased the feasibility of building accurate project-specific TD forecasting models, there is a gap in the field regarding cross-project TD forecasting. Cross-project TD forecasting is of practical importance, since it would enable the application of pre-existing forecasting models on previously unknown software projects, especially new projects that do not exhibit sufficient commit history to enable the construction of project-specific models. To this end, in the present paper, we focus on cross-project TD forecasting, and we examine whether the consideration of similarities between software projects could be the key for more accurate forecasting. More specifically, we propose an approach based on data clustering. In fact, a relatively large repository of software projects is divided into clusters of similar projects with respect to their TD aspects, and specific TD forecasting models are built for each cluster, using regression algorithms. According to our approach, previously unknown software projects are assigned to one of the defined clusters and the cluster-specific TD forecasting model is applied to predict future TD values. The approach was evaluated through several experiments based on real-world applications. The results of the analysis suggest that the proposed approach comprises a promising solution for accurate cross-project TD forecasting.

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Section: Datasetmentioning

confidence: 99%

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

et al. 2021

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“…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

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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“…The empirical studies performed to validate the identified techniques are summarized in Table 4. (Griffith et al, 2014) assessed three methods ( (Letouzey, 2012) (Curtis et al, 2012) (Marinescu, 2012)) to find out if they effectively describe the relationship between the quality of the system and the level of TD.…”

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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The Correspondence Between Software Quality Models and Technical Debt Estimation Approaches

Cited by 29 publications

References 18 publications

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

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

Automated Measurement of Technical Debt: A Systematic Literature Review

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