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Technical Debt Quantification through Metrics: An Industrial Validation

Authors: Angeliki-Agathi Tsintzira; Ampatzoglou, Areti; Matei, Oliviu; Ampatzoglou, Apostolos; Chatzigeorgiou, Alexander; Heb, Robert;

Technical Debt Quantification through Metrics: An Industrial Validation

Abstract

Technical Debt is a software engineering metaphor that refers to the intentional or unintentional production of software at a lower quality, to achieve business goals (e.g., shorten time to market). Nevertheless, similarly to financial debt, technical debt does not come without negative consequences. The accumulation of technical debt leads to additional maintenance. The technical debt metaphor is built around three major concepts: principal, interest, and interest probability. The quantification of these notions is the first step towards the efficient management of technical debt, in the sense that “you cannot control what you cannot measure”. In this paper, we employ an established method for quantifying technical debt, namely FITTED, to measure the technical debt of an industrial software product, and contrast it to the perception of the software engineers. The main contribution of this work is the validation of FITTED in an industrial setting, and particularly in the Embedded Low Power Systems domain. The results of the study suggest that FITTED is able of accurately ranking software components, with respect to their principal, interest, and interest probability.

Keywords

technical debt, industrial, case study, metrics

13 references, page 1 of 2

[1] Ampatzoglou, Ar., Ampatzoglou, Ap., Chatzigeorgiou, A. and Avgeriou, P., “The financial aspect of managing technical debt: A systematic literature review”, Information and Software Technology, 70, pp. 100-121, 2015 [OpenAIRE]

[2] Ampatzoglou, Ar., Ampatzoglou, Ap., Avgeriou, P., and Chatzigeorgiou, A., “Establishing a framework for managing interest in technical debt,” 5th International Symposium on Business Modeling and Software Design (BMSD), Milan, Italy, 2015

[3] Ampatzoglou, Ar., Ampatzoglou, Ap., Chatzigeorgiou, A., Avgeriou, P., Abrahamsson, P., Martini, A., Zdun, U., and Systa, K., “The perception of technical debt in the embedded systems domain: an industrial case study” 8th International Workshop on Managing Technical Debt (MTD'16), IEEE, October 2016, Raleigh, NC, USA.

[4] Ampatzoglou, Ar., Michailidis, A., Sarikyriakidis, C., Ampatzoglou, Ap., Chatzigeorgiou, A., and Avgeriou, P., “A framework for managing interest in technical debt: an industrial validation”, 2018 International Conference on Technical Debt (TechDebt 2018), ACM, May 2018, Gothenburg, Sweden. [OpenAIRE]

[5] Arvanitou, E. M., Ampatzoglou, A., Chatzigeorgiou, A., and Avgeriou, P., “A Method for Assessing Class Change Proneness”, 21st International Conference on Evaluation and Assessment in Software Engineering (EASE' 17), ACM, 15-16 June 2017, Sweden

[6] Arvanitou, E. M., Ampatzoglou, A., Tzouvalidis, K., Chatzigeorgiou, A., Avgeriou, P. and Deligiannis I., “Assessing Change Proneness at the Architecture Level: An Empirical Validation”, 1st International Workshop on Emerging Trends in Software Design and Architecture (WETSoDA' 17), Nanjing, China, 4 December 2017.

[7] Chatzigeorgiou, A., Ampatzoglou, Ar., Ampatzoglou, Ap., and Amanatidis, T., “Estimating the Breaking Point for Technical Debt”, 7th International Workshop on Managing Technical Debt (MTD '15), IEEE, Computer Society, 2015. [OpenAIRE]

[8] DeMarco, T., “Controlling Software Projects: Management, Measurement, and Estimates”, Prentice Hall; 1st Edition, 1986.

[9] Kruchten, P., Nord, R. L., and Ozkaya, I., “Technical Debt: From Metaphor to Theory and Practice,” IEEE Software, vol. 29, no. 6, pp. 18-21, Nov. 2012

[10] Letouzey, J.-L., “The SQALE Method for Managing Technical Debt”, 3rd International Workshop on Managing Technical Debt (MTD'12), Zurich, Switzerland, 2012, 5 June 2012

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citations
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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