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Design debt prioritization: a design best practice-based approach

Published: 27 May 2018 Publication History

Abstract

Technical debt (TD) in a software system is a metaphor that tries to illustrate the remediation effort of the already introduced quality deficit and the impact thereof to the business value of the system. To address TD, various management activities are proposed, each addressing a particular purpose. Whereas the activities of debt identification and measurement are broadly considered in literature, the activities of debt prioritization and communication lack appropriate approaches with an economic perspective. This work proposes a TD prioritization approach. Therefore, it narrows down the focus of TD to design debt and relies on the quantification of design best practices. Further, the non-conformance of these practices is assessed by applying a benchmarking technique. As a result, the gained information is transferred into a portfolio-matrix to support the prioritization and communication of design remediation actions. The applicability and suitability of the approach are demonstrated by using the source code of the open source project GeoGebra.

References

[1]
Nicolli S. R. Alves, Thiago S. Mendes, Manoel G. de Mendonça, Rodrigo O. Spínola, Forrest Shull, and Carolyn Seaman. 2016. Identification and management of technical debt: A systematic mapping study. Information and Software Technology 70, Supplement C (2016), 100--121.
[2]
Areti Ampatzoglou, Apostolos Ampatzoglou, Alexander Chatzigeorgiou, Paris Avgeriou, Pekka Abrahamsson, Antonio Martini, Uwe Zdun, and Kari Systa. 2016. The Perception of Technical Debt in the Embedded Systems Domain: An Industrial Case Study. In 8th IEEE International Workshop on Managing Technical Debt (MTD). 9--16.
[3]
Johannes Bräuer. 2017. Measuring and Assessing Object-oriented Design Principles. Dissertation. Johannes Kepler University, Linz, Austria.
[4]
Johannes Bräuer, Reinhold Plösch, Matthias Saft, and Christian Körner. 2017. A Survey on the Importance of Object-oriented Design Best Practices. In Proceedings of the Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2017. Vienna, Austria, 27--34.
[5]
Johannes Bräuer, Reinhold Plösch, Matthias Saft, and Christian Körner. 2018. Measuring Object-Oriented Design Principles: The Results of Focus Group-Based Research. Journal of Systems and Software (2018).
[6]
Johannes Bräuer, Matthias Saft, Reinhold Plösch, and Christian Körner. 2017. Improving Object-oriented Design Quality: A Portfolio- and Measurement-based Approach. In Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement (IWSM Mensura '17). ACM, New York, NY, USA, 244--254.
[7]
Peter Coad and Edward Yourdon. 1991. Object-Oriented Design. Prentice Hall, London, UK.
[8]
Ward Cunningham. 1992. The WyCash Portfolio Management System. In Addendum to the Proceedings on Object-oriented Programming Systems, Languages, and Applications (Addendum) (OOPSLA '92). ACM, New York, NY, USA, 29--30.
[9]
Bill Curtis, Jay Sappidi, and Alexandra Szynkarski. 2012. Estimating the Size, Cost, and Types of Technical Debt. In Proceedings of the Third International Workshop on Managing Technical Debt (MTD '12). IEEE Press, Piscataway, NJ, USA, 49--53.
[10]
Francesca Arcelli Fontana, Vincenzo Ferme, and Stefano Spinelli. 2012. Investigating the impact of code smells debt on quality code evaluation. In Proceedings of the Third International Workshop on Managing Technical Debt. IEEE, 15--22.
[11]
Object Management Group. 2017. About the Automated Technical Debt Measure Specification Version 1.0 beta 2. (2017). http://www.omg.org/spec/ATDM/
[12]
Yuepu Guo and Carolyn Seaman. 2011. A Portfolio Approach to Technical Debt Management. In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11). ACM, New York, NY, USA, 31--34.
[13]
Jeanette Heidenberg and Ivan Porres. 2010. Metrics Functions for Kanban Guards. In 17th IEEE International Conference and Workshops on Engineering of Computer Based Systems. 306--310.
[14]
Clemente Izurieta, Isaac Griffith, Derek Reimanis, and Rachael Luhr. 2013. On the Uncertainly of Technical Debt Measurements. In Proceedings of the International Conference on Information Science and Applications (ICISA). 1--4.
[15]
Jean-Louis Letouzey. 2012. The SQALE method for evaluating Technical Debt. In Proceedings of the Third International Workshop on Managing Technical Debt. 31--36.
[16]
Jean-Louis Letouzey and Michel Ilkiewicz. 2012. Managing Technical Debt with the SQALE Method. IEEE Software 29, 6 (2012), 44--51.
[17]
Zengyang Li, Paris Avgeriou, and Peng Liang. 2015. A systematic mapping study on technical debt and its management. Journal of Systems and Software 101, Supplement C (2015), 193--220.
[18]
Radu Marinescu. 2004. Detection strategies: metrics-based rules for detecting design flaws. In 20th IEEE International Conference on Software Maintenance. IEEE, 350--359.
[19]
Radu Marinescu. 2012. Assessing technical debt by identifying design flaws in software systems. IBM Journal of Research and Development 56, 5 (2012), 9:1--9:13.
[20]
Alois Mayr, Reinhold Plösch, and Christian Körner. 2014. A Benchmarking-Based Model for Technical Debt Calculation. In Proceedings of the 14th International Conference on Quality Software (QSIC 2014). IEEE, Dallas, Texas, 305--314.
[21]
Ariadi Nugroho, Joost Visser, and Tobias Kuipers. 2011. An Empirical Model of Technical Debt and Interest. In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11). ACM, New York, NY, USA, 1--8.
[22]
Reinhold Plösch, Johannes Bräuer, Christian Körner, and Matthias Saft. 2016. Measuring, Assessing and Improving Software Quality based on Object-Oriented Design Principles. Open Computer Science 6, 1 (2016).
[23]
Reinhold Plösch, Johannes Bräuer, Christian Körner, and Matthias Saft. 2016. MUSE - Framework for Measuring Object-Oriented Design. Journal of Object Technology 15, 4 (2016), 2:1--29.
[24]
Arthur J. Riel. 1996. Object-Oriented Design Heuristics (1st ed.). Addi son-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
[25]
J. Sappidi, Bill Curtis, and Alexandra Szynkarski. 2011. The CRASH Report -2011/12 Summary of Key Findings. Technical Report.
[26]
Carolyn Seaman and Yuepu Guo. 2011. Measuring and Monitoring Technical Debt. Advances in Computers, Vol. 82. Elsevier, 25 -- 46.
[27]
Richard Mark Soley and Bill Curtis. 2013. The Consortium for IT Software Quality (CISQ). In Software Quality. Increasing Value in Software and Systems Development, Dietmar Winkler, Stefan Biffl, and Johannes Bergsmann (Eds.). Number 133 in Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 3--9.
[28]
Jesse Yli-Huumo, Andrey Maglyas, and Kari Smolander. 2016. How do software development teams manage technical debt? - An empirical study. Journal of Systems and Software 120, Supplement C (2016), 195--218.
[29]
Nico Zazworka, Carolyn Seaman, and Forrest Shull. 2011. Prioritizing Design Debt Investment Opportunities. In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11). ACM, New York, NY, USA, 39--42.
[30]
Nico Zazworka, Michele A. Shaw, Forrest Shull, and Carolyn Seaman. 2011. Investigating the Impact of Design Debt on Software Quality. In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11). ACM, New York, NY, USA, 17--23.
[31]
Nico Zazworka, Rodrigo O. Spínola, Antonio Vetro', Forrest Shull, and Carolyn Seaman. 2013. A Case Study on Effectively Identifying Technical Debt. In Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering (EASE '13). ACM, New York, NY, USA, 42--47.
[32]
Nico Zazworka, Antonio Vetro', Clemente Izurieta, Sunny Wong, Yuanfang Cai, Carolyn Seaman, and Forrest Shull. 2014. Comparing four approaches for technical debt identification. Software Quality Journal 22, 3 (2014), 403--426.
[33]
Olaf Zimmermann. 2017. Microservices tenets. Computer Science - Research and Development 32, 3--4 (2017), 301--310.

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  • (2023)Managing Technical Debt Using Intelligent Techniques - A Systematic Mapping StudyIEEE Transactions on Software Engineering10.1109/TSE.2022.321476449:4(2202-2220)Online publication date: 1-Apr-2023
  • (2023)Technical debt (TD) through the lens of Twitter: A surveyJournal of Software: Evolution and Process10.1002/smr.2547Online publication date: 4-Mar-2023
  • (2023)Code smell prioritization in object‐oriented software systemsJournal of Software: Evolution and Process10.1002/smr.253635:12Online publication date: 29-Jan-2023
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cover image ACM Conferences
TechDebt '18: Proceedings of the 2018 International Conference on Technical Debt
May 2018
157 pages
ISBN:9781450357135
DOI:10.1145/3194164
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 27 May 2018

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Author Tags

  1. debt prioritization
  2. design debt
  3. design quality
  4. technical debt

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ICSE '18
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Overall Acceptance Rate 14 of 31 submissions, 45%

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Cited By

View all
  • (2023)Managing Technical Debt Using Intelligent Techniques - A Systematic Mapping StudyIEEE Transactions on Software Engineering10.1109/TSE.2022.321476449:4(2202-2220)Online publication date: 1-Apr-2023
  • (2023)Technical debt (TD) through the lens of Twitter: A surveyJournal of Software: Evolution and Process10.1002/smr.2547Online publication date: 4-Mar-2023
  • (2023)Code smell prioritization in object‐oriented software systemsJournal of Software: Evolution and Process10.1002/smr.253635:12Online publication date: 29-Jan-2023
  • (2022)Reproducibility in the technical debt domainActa Universitatis Sapientiae, Informatica10.2478/ausi-2021-001613:2(335-360)Online publication date: 2-Feb-2022
  • (2022)Qualitative analysis of the relationship between design smells and software engineering challengesProceedings of the 2022 European Symposium on Software Engineering10.1145/3571697.3571704(48-55)Online publication date: 27-Oct-2022
  • (2022)Technical debt resulting from architectural degradation and code smellsACM SIGAPP Applied Computing Review10.1145/3512753.351275521:4(20-36)Online publication date: 19-Jan-2022
  • (2021)Technical Debt Prioritization: Taxonomy, Methods Results, and Practical Characteristics2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA53835.2021.00034(206-213)Online publication date: Sep-2021
  • (2020)A systematic literature review of technical debt prioritizationProceedings of the 3rd International Conference on Technical Debt10.1145/3387906.3388630(1-10)Online publication date: 28-Jun-2020

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