Identification and Management of Technical Debt

A Systematic Mapping Study Update

Authors

  • María Isabel Murillo University of Costa Rica https://orcid.org/0000-0002-8729-3867
  • Gustavo López University of Costa Rica
  • Rodrigo Spínola Salvador University
  • Julio Guzmán University of Costa Rica
  • Nicolli Rios Federal University of Rio de Janeiro
  • Alexia Pacheco University of Costa Rica

DOI:

https://doi.org/10.5753/jserd.2023.2671

Keywords:

Technical Debt Management, Technical Debt Identification, Software Development Process

Abstract

Technical debt is a concept used to describe the lack of good practices during software development, leading to several problems and costs. Identification and management strategies can help reduce these difficulties. In a previous study, Alves et al. (2016) analyzed the research landscape of such strategies from 2010 to 2014. This paper replicates and updates their study to explore the evolution of technical debt identification and management research landscape over a decade, including literature from 2010 until 2022. We analyzed 117 papers from the ACM Digital Library, IEEE Xplore, Science Direct, and Springer Link. Newly suggested strategies include automatically identifying admitted debt in comments, commits, and source code. Between 2015 and 2022, more empirical evaluations have been performed, and the general research focus has changed to a more holistic approach. Therefore, the research area evolved and reached a new level of maturity compared to previous results from Alves et al. (2016). Not only are code aspects considered for technical debt, but other aspects have also been investigated (e.g., models for the development process).

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References

Abad, Z. S. H., & Ruhe, G. (2015). Using real options to manage Technical Debt in Requirements Engineering. 2015 IEEE 23rd International Requirements Engineering Conference, RE 2015 - Proceedings, 230–235. https://doi.org/10.1109/RE.2015.7320428

Akbarinasaji, S., & Bener, A. (2016). Adjusting the Balance Sheet by Appending Technical Debt. Proceedings - 2016 IEEE 8th International Workshop on Managing Technical Debt, MTD 2016, 36–39. https://doi.org/10.1109/MTD.2016.14

Akbarinasaji, S., Bener, A. B., & Erdem, A. (2016). Measuring the principal of defect debt. Proceedings - 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2016, 1–7. https://doi.org/10.1145/2896995.2896999

Albarak, M., Bahsoon, R., Ozkaya, I., & Nord, R. L. (2020). Managing Technical Debt in Database Normalization. IEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2020.3001339

Aldaeej, A., & Seaman, C. (2018). From lasagna to spaghetti, a decision model to manage defect debt. Proceedings - International Conference on Software Engineering, 67–71. https://doi.org/10.1145/3194164.3194177

Alfayez, R., Alwehaibi, W., Winn, R., Venson, E., & Boehm, B. (2020). A systematic literature review of technical debt prioritization. Proceedings - 2020 IEEE/ACM International Conference on Technical Debt, TechDebt 2020, 10, 1–10. https://doi.org/10.1145/3387906.3388630

Alfayez, R., & Boehm, B. (2019). Technical Debt Prioritization: A Search-Based Approach. Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019, 434–445. https://doi.org/10.1109/QRS.2019.00060

Alves, N. S. R., Mendes, T. S., de Mendonça, M. G., Spinola, R. O., Shull, F., & Seaman, C. (2016a). Identification and management of technical debt: A systematic mapping study. Information and Software Technology, 70, 100–121. https://doi.org/10.1016/J.INFSOF.2015.10.008

Alves, N. S. R., Mendes, T. S., de Mendonça, M. G., Spinola, R. O., Shull, F., & Seaman, C. (2016b). Identification and management of technical debt: A systematic mapping study. Information and Software Technology, 70, 100–121. https://doi.org/10.1016/J.INFSOF.2015.10.008

Ampatzoglou, A., Ampatzoglou, A., Avgeriou, P., & Chatzigeorgiou, A. (2015). A Financial Approach for Managing Interest in Technical Debt. Lecture Notes in Business Information Processing, 257, 117–133. https://doi.org/10.1007/978-3-319-40512-4_7

Ampatzoglou, A., Ampatzoglou, A., Chatzigeorgiou, A., & Avgeriou, P. (2015). The financial aspect of managing technical debt: A systematic literature review. Information and Software Technology, 64, 52–73. https://doi.org/10.1016/J.INFSOF.2015.04.001

Ampatzoglou, A., Michailidis, A., Sarikyriakidis, C., Ampatzoglou, A., Chatzigeorgiou, A., Avgeriou, P., Ampatzoglou, A., Michailidis, A., Sarikyriakidis, C., Chatzigeorigiou, A., & Avgeriou, P. (2018). A Framework for Managing Interest in Technical Debt: An Industrial Validation. Proceedings of the 2018 International Conference on Technical Debt, 10. https://doi.org/10.1145/3194164

Anderson, P., Kot, L., Gilmore, N., & Vitek, D. (2019). SARIF-enabled tooling to encourage gradual technical debt reduction. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 71–72. https://doi.org/10.1109/TECHDEBT.2019.00024

Aversano, L., Bernardi, M. L., Cimitile, M., & Iammarino, M. (2021). Technical Debt predictive model through Temporal Convolutional Network. Proceedings of the International Joint Conference on Neural Networks, 2021-July. https://doi.org/10.1109/IJCNN52387.2021.9534423

Baldassarre, M. T., Lenarduzzi, V., Romano, S., & Saarimäki, N. (2020). On the diffuseness of technical debt items and accuracy of remediation time when using SonarQube. Information and Software Technology, 128, 106377. https://doi.org/10.1016/J.INFSOF.2020.106377

Besker, T., Martini, A., & Bosch, J. (2019). Technical debt triage in backlog management. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 13–22. https://doi.org/10.1109/TECHDEBT.2019.00010

Besker, T., Martini, A., & Bosch, J. (2022). The use of incentives to promote technical debt management. Information and Software Technology, 142, 106740. https://doi.org/10.1016/J.INFSOF.2021.106740

Borup, N. B., Christiansen, A. L. J., Tovgaard, S. H., & Persson, J. S. (2021). Deliberative Technical Debt Management: An Action Research Study. Lecture Notes in Business Information Processing, 434 LNBIP, 50–65. https://doi.org/10.1007/978-3-030-91983-2_5/TABLES/3

Chatzigeorgiou, A., Ampatzoglou, A., Ampatzoglou, A., & Amanatidis, T. (2015). Estimating the breaking point for technical debt. 2015 IEEE 7th International Workshop on Managing Technical Debt, MTD 2015 - Proceedings, 53–56. https://doi.org/10.1109/MTD.2015.7332625

Chicote, M. (2017). Startups and Technical Debt: Managing Technical Debt with Visual Thinking. Proceedings - 2017 IEEE/ACM 1st International Workshop on Software Engineering for Startups, SoftStart 2017, 10–11. https://doi.org/10.1109/SOFTSTART.2017.6

Ciancarini, P., & Russo, D. (2020). The Strategic Technical Debt Management Model: An Empirical Proposal. IFIP Advances in Information and Communication Technology, 582 IFIP, 131–140. https://doi.org/10.1007/978-3-030-47240-5_13

Codabux, Z., & Williams, B. J. (2016). Technical debt prioritization using predictive analytics. Proceedings - International Conference on Software Engineering, 704–706. https://doi.org/10.1145/2889160.2892643

Consortium for Information & Software Quality. (2022). Cost of Poor Software Quality in the U.S.: A 2022 Report - CISQ. [link]

Crespo, Y., Gonzalez-Escribano, A., & Piattini, M. (2021). Carrot and Stick approaches revisited when managing Technical Debt in an educational context. Proceedings - 2021 IEEE/ACM International Conference on Technical Debt, TechDebt 2021, 99–108. https://doi.org/10.1109/TECHDEBT52882.2021.00020

da Maldonado, E. S., Abdalkareem, R., Shihab, E., & Serebrenik, A. (2017). An empirical study on the removal of Self-Admitted Technical Debt. Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017, 238–248. https://doi.org/10.1109/ICSME.2017.8

de Leon-Sigg, M., Vazquez-Reyes, S., & Rodriguez-Avila, D. (2020). Towards the use of a framework to make technical debt visible. Proceedings - 2020 8th Edition of the International Conference in Software Engineering Research and Innovation, CONISOFT 2020, 86–92. https://doi.org/10.1109/CONISOFT50191.2020.00022

de Lima, B. S., Garcia, R. E., & Eler, D. M. (2022). Toward prioritization of self-admitted technical debt: an approach to support decision to payment. Software Quality Journal, 1–27. https://doi.org/10.1007/S11219-021-09578-7/FIGURES/10

Detofeno, T., Malucelli, A., & Reinehr, S. (2021). Technical Debt Guild: When experience and engagement improve Technical Debt Management. XX Brazilian Symposium on Software Quality. https://doi.org/10.1145/3493244

di Biase, M., Rastogi, A., Bruntink, M., & van Deursen, A. (2019). The delta maintainability model: Measuring maintainability of fine-grained code changes. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 113–122. https://doi.org/10.1109/TECHDEBT.2019.00030

Fairley, R. E., & Willshire, M. J. (2017). Better Now Than Later: Managing Technical Debt in Systems Development. Computer, 50(5), 80–87. https://doi.org/10.1109/MC.2017.124

Falessi, D., & Reichel, A. (2015). Towards an open-source tool for measuring and visualizing the interest of technical debt. 2015 IEEE 7th International Workshop on Managing Technical Debt, MTD 2015 - Proceedings, 1–8. https://doi.org/10.1109/MTD.2015.7332618

Fernández-Sánchez, C., Garbajosa, J., Yagüe, A., & Perez, J. (2017). Identification and analysis of the elements required to manage technical debt by means of a systematic mapping study. Journal of Systems and Software, 124, 22–38. https://doi.org/10.1016/J.JSS.2016.10.018

Fernandez-Sanchez, C., Humanes, H., Garbajosa, J., & Diaz, J. (2017). An open tool for assisting in technical debt management. Proceedings - 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017, 400–403. https://doi.org/10.1109/SEAA.2017.60

Fontana, F. A., Roveda, R., & Zanoni, M. (2016). Tool support for evaluating architectural debt of an existing system: An experience report. Proceedings of the ACM Symposium on Applied Computing, 04-08-April-2016, 1347–1349. https://doi.org/10.1145/2851613.2851963

Freire, S., Rios, N., Mendonça, M., Falessi, D., Seaman, C., Izurieta, C., & Spínola, R. O. (2020). Actions and impediments for technical debt prevention: Results from a global family of industrial surveys. Proceedings of the ACM Symposium on Applied Computing, 1548–1555. https://doi.org/10.1145/3341105.3373912

Griffith, I., Taffahi, H., Izurieta, C., & Claudio, D. (2014). A SIMULATION STUDY OF PRACTICAL METHODS FOR TECHNICAL DEBT MANAGEMENT IN AGILE SOFTWARE DEVELOPMENT. Proceedings of the Winter Simulation Conference 2014. https://doi.org/10.1109/WSC.2014.7019961

Guo, Y., & Seaman, C. (2011). A Portfolio Approach to Technical Debt Management.

Guo, Y., Spínola, R. O., & Seaman, C. (2014). Exploring the costs of technical debt management – a case study. Empirical Software Engineering 2014 21:1, 21(1), 159–182. https://doi.org/10.1007/S10664-014-9351-7

Haas, R., Niedermayr, R., & Juergens, E. (2019). Team-scale: Tackle technical debt and control the quality of your software. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 55–56. https://doi.org/10.1109/TECHDEBT.2019.00016

Holvitie, J., Licorish, S. A., & Leppanen, V. (2016). Modelling Propagation of Technical Debt. Proceedings - 42nd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2016, 54–58. https://doi.org/10.1109/SEAA.2016.53

Iovino, L., Di, A., Davide, S., Ruscio, D., Pierantonio, A., Salle, A. di, Ruscio, D. di, & Pieran, A. (2020). Metamodel deprecation to manage technical debt in model co-evolution. Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings, 306–315. https://doi.org/10.1145/3417990.3419625

Izurieta, C., Ozkaya, I., Seaman, C., Kruchten, P., Nord, R., Snipes, W., & Avgeriou, P. (2016). Perspectives on managing technical debt : A transition point and roadmap from Dagstuhl. CEUR Workshop Proceedings, 1771, 84–87.

Izurieta, C., Rice, D., Kimball, K., & Valentien, T. (2018). A Position Study to Investigate Technical Debt Associated with Security Weaknesses. Proceedings of the 2018 International Conference on Technical Debt. https://doi.org/10.1145/3194164

Izurieta, C., Rojas, G., & Griffith, I. (2015). Preemptive Management of Model Driven Technical Debt for Improving Software Quality. Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures. https://doi.org/10.1145/2737182

Izurieta, C., Vetrò, A., Zazworka, N., Cai, Y., Seaman, C., & Shull, F. (2012). Organizing the technical debt landscape. 2012 3rd International Workshop on Managing Technical Debt, MTD 2012 - Proceedings, 23–26. https://doi.org/10.1109/MTD.2012.6225995

Kontsevoi, B., Soroka, E., & Terekhov, S. (2019). TETRA, as a set of techniques and tools for calculating technical debt principal and interest. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 64–65. https://doi.org/10.1109/TECHDEBT.2019.00021

Kosti, M. V., Ampatzoglou, A., Chatzigeorgiou, A., Pal-las, G., Stamelos, I., & Angelis, L. (2017). Technical debt principal assessment through structural metrics. Proceedings - 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017, 329–333. https://doi.org/10.1109/SEAA.2017.59

Kouros, P., Chaikalis, T., Arvanitou, E. M., Chatzigeor-giou, A., Ampatzoglou, A., & Amanatidis, T. (2019). JCaliper: Search-based technical debt management. Proceedings of the ACM Symposium on Applied Computing, Part F147772, 1721–1730. https://doi.org/10.1145/3297280.3297448

Lahti, J. R., Tuovinen, A. P., & Mikkonen, T. (2021). Experiences on Managing Technical Debt with Code Smells and AntiPatterns. Proceedings - 2021 IEEE/ACM International Conference on Technical Debt, TechDebt 2021, 36–44. https://doi.org/10.1109/TECHDEBT52882.2021.00013

Lenarduzzi, V., Besker, T., Taibi, D., Martini, A., & Arcelli Fontana, F. (2021). A systematic literature review on Technical Debt prioritization: Strategies, processes, factors, and tools. Journal of Systems and Software, 171, 110827. https://doi.org/10.1016/J.JSS.2020.110827

Lenarduzzi, V., Martini, A., Taibi, D., & Tamburri, D. A. (2019). Towards surgically-precise technical debt estimation: Early results and research roadmap. MaLTeSQuE 2019 - Proceedings of the 3rd ACM SIG-SOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation, Co-Located with ESEC/FSE 2019, 37–42. https://doi.org/10.1145/3340482.3342747

Li, Z., Avgeriou, P., & Liang, P. (2015). A systematic mapping study on technical debt and its management. Journal of Systems and Software, 101, 193–220. https://doi.org/10.1016/J.JSS.2014.12.027

Macit, Y., Giray, G., & Tüzün, E. (2020). Methods for Identifying Architectural Debt: A Systematic Mapping Study. 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings. https://doi.org/10.1109/UYMS50627.2020.9247070

Malakuti, S., & Heuschkel, J. (2021). The Need for Holistic Technical Debt Management across the Value Stream: Lessons Learnt and Open Challenges. Proceedings - 2021 IEEE/ACM International Conference on Technical Debt, TechDebt 2021, 109–113. https://doi.org/10.1109/TECHDEBT52882.2021.00021

Martini, A. (2018). AnaConDebt: A Tool to Assess and Track Technical Debt. Proceedings of the 2018 International Conference on Technical Debt. https://doi.org/10.1145/3194164

Martini, A., Besker, T., & Bosch, J. (2016). The introduction of technical debt tracking in large companies. Proceedings - Asia-Pacific Software Engineering Conference, APSEC, 0, 161–168. https://doi.org/10.1109/APSEC.2016.032

Martini, A., & Bosch, J. (2016). An empirically developed method to aid decisions on architectural technical debt refactoring: AnaConDebt. Proceedings - International Conference on Software Engineering, 31–40. https://doi.org/10.1145/2889160.2889224

Martini, A., & Bosch, J. (2017a). The Magnificent Seven: Towards a Systematic Estimation of Technical Debt Interest. Proceedings of the XP2017 Scientific Workshops. https://doi.org/10.1145/3120459

Martini, A., & Bosch, J. (2017b). Revealing social debt with the CAFFEA framework: An antidote to architectural debt. Proceedings - 2017 IEEE International Conference on Software Architecture Workshops, ICSAW 2017: Side Track Proceedings, 179–181. https://doi.org/10.1109/ICSAW.2017.42

Mendes, E., Wohlin, C., Felizardo, K., & Kalinowski, M. (2020). When to update systematic literature reviews in software engineering. Journal of Systems and Software, 167, 110607. https://doi.org/10.1016/J.JSS.2020.110607

Mera-Gómez, C., Bahsoon, R., & Buyya, R. (2016). Elasticity Debt: A Debt-Aware Approach to Reason About Elasticity Decisions in the Cloud. Proceedings of the 9th International Conference on Utility and Cloud Computing. https://doi.org/10.1145/2996890

Mohan, M., Greer, D., & McMullan, P. (2016). Technical debt reduction using search based automated refactoring. Journal of Systems and Software, 120, 183–194. https://doi.org/10.1016/J.JSS.2016.05.019

Nepomuceno, V., & Soares, S. (2019). On the need to update systematic literature reviews. Information and Software Technology, 109, 40–42. https://doi.org/10.1016/J.INFSOF.2019.01.005

Nielsen, M. E., Østergaard Madsen, C., & Lungu, M. F. (2020). Technical Debt Management: A Systematic Literature Review and Research Agenda for Digital Government. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12219 LNCS, 121–137. https://doi.org/10.1007/978-3-030-57599-1_10

Nielsen, M. E., & Skaarup, S. (2021). IT Portfolio management as a framework for managing Technical Debt; IT Portfolio management as a framework for managing Technical Debt. 14th International Conference on Theory and Practice of Electronic Governance. https://doi.org/10.1145/3494193

Oliveira, F., Goldman, A., & Santos, V. (2015). Managing Technical Debt in Software Projects Using Scrum: An Action Research. Proceedings - 2015 Agile Conference, Agile 2015, 50–59. https://doi.org/10.1109/AGILE.2015.7

Pacheco, A., Marín-Raventós, G., & López, G. (2018). Designing a Technical Debt Visualization Tool to Improve Stakeholder Communication in the Decision-Making Process: A Case Study. Lecture Notes in Business Information Processing, 327, 15–26. https://doi.org/10.1007/978-3-319-99040-8_2

Perez, B., Correal, D., & Astudillo, H. (2019). A proposed model-driven approach to manage architectural technical debt life cycle. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 73–77. https://doi.org/10.1109/TECHDEBT.2019.00025

Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008). Systematic Mapping Studies in Software Engineering. 12th International Conference on Evaluation and Assessment in Software Engineering, EASE 2008. https://doi.org/10.14236/EWIC/EASE2008.8

Plösch, R., Bräuer, J., Saft, M., & Körner, C. (2018). Design Debt Prioritization: A Design Best Practice-Based Approach. Proceedings of the 2018 International Conference on Technical Debt, 18. https://doi.org/10.1145/3194164

Ramasubbu, N., & Kemerer, C. F. (2019). Integrating Technical Debt Management and Software Quality Management Processes: A Normative Framework and Field Tests. IEEE Transactions on Software Engineering, 45(3), 285–300. https://doi.org/10.1109/TSE.2017.2774832

Reboucas De Almeida, R. (2019). Business-Driven Technical Debt Prioritization. Proceedings - 2019 IEEE International Conference on Software Maintenance and Evolution, ICSME 2019, 605–609. https://doi.org/10.1109/ICSME.2019.00096

Reboucas De Almeida, R., Kulesza, U., Treude, C., Cavalcanti Feitosa, D., & Lima, A. H. G. (2018). Aligning technical debt prioritization with business objectives: A multiple-case study. Proceedings - 2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018, 655–664. https://doi.org/10.1109/ICSME.2018.00075

Reboucas De Almeida, R., Treude, C., & Kulesza, U. (2019). Tracy: A Business-Driven Technical Debt Prioritization Framework. Proceedings - 2019 IEEE International Conference on Software Maintenance and Evolution, ICSME 2019, 181–185. https://doi.org/10.1109/ICSME.2019.00028

Ribeiro, L. F., Alves, N. S. R., de Mendonca Neto, M. G., & Spinola, R. O. (2017). A strategy based on multiple decision criteria to support technical debt management. Proceedings - 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017, 334–341. https://doi.org/10.1109/SEAA.2017.37

Rindell, K., Bernsmed, K., & Gilje Jaatun, M. (2019). Managing security in software or: How I learned to stop worrying and manage the security technical debt. ACM International Conference Proceeding Series. https://doi.org/10.1145/3339252.3340338

Rios, N., Mendonça Neto, M. G. de, & Spínola, R. O. (2018). A tertiary study on technical debt: Types, management strategies, research trends, and base information for practitioners. Information and Software Technology, 102, 117–145. https://doi.org/10.1016/J.INFSOF.2018.05.010

Rios, N., Spinola, R. O., de Mendonça Neto, M. G., & Seaman, C. (2019). Supporting analysis of technical debt causes and effects with cross-company probabilistic cause-effect diagrams. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 3–12. https://doi.org/10.1109/TECHDEBT.2019.00009

Rios, N., Spínola, R. O., Mendonça, M., & Seaman, C. (2020). The practitioners’ point of view on the concept of technical debt and its causes and consequences: a design for a global family of industrial surveys and its first results from Brazil. Empirical Software Engineering 2020 25:5, 25(5), 3216–3287. https://doi.org/10.1007/S10664-020-09832-9

Shapochka, A., & Omelayenko, B. (2016). Practical Technical Debt Discovery by Matching Patterns in Assessment Graph. Proceedings - 2016 IEEE 8th Inter-national Workshop on Managing Technical Debt, MTD 2016, 32–35. https://doi.org/10.1109/MTD.2016.7

Sharma, T. (2019). How deep is the mud: Fathoming architecture technical debt using designite. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 59–60. https://doi.org/10.1109/TECHDEBT.2019.00018

Snipes, W., & Ramaswamy, S. (2018). A Proposed Sizing Model for Managing 3rd Party Code Technical Debt. Proceedings of the 2018 International Conference on Technical Debt, 18. https://doi.org/10.1145/3194164

Stochel, M. G., Cholda, P., & Wawrowski, M. R. (2020). Continuous Debt Valuation Approach (CoDVA) for Technical Debt Prioritization. Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020, 362–366. https://doi.org/10.1109/SEAA51224.2020.00066

Tom, E., Aurum, A., & Vidgen, R. (2013). An exploration of technical debt. Journal of Systems and Software, 86(6), 1498–1516. https://doi.org/10.1016/J.JSS.2012.12.052

Tornhill, A. (2018). Assessing technical debt in automated tests with codescene. Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018, 122–125. https://doi.org/10.1109/ICSTW.2018.00039

Tornhill, A., & Ab, E. (n.d.). Prioritize Technical Debt in Large-Scale Systems using CodeScene. Proceedings of the 2018 International Conference on Technical Debt, 18. https://doi.org/10.1145/3194164

Trumler, W., & Paulisch, F. (2016). How “Specification by Example” and Test-Driven Development Help to Avoid Technical Debt. Proceedings - 2016 IEEE 8th International Workshop on Managing Technical Debt, MTD 2016, 1–8. https://doi.org/10.1109/MTD.2016.10

Vidal, S., Vazquez, H., Diaz-Pace, J. A., Marcos, C., Garcia, A., & Oizumi, W. (2016). JSpIRIT: A flexible tool for the analysis of code smells. Proceedings - International Conference of the Chilean Computer Science Society, SCCC, 2016-February. https://doi.org/10.1109/SCCC.2015.7416572

von Zitzewitz, A. (2019). Mitigating technical and architectural debt with sonargraph. Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019, 66–67. https://doi.org/10.1109/TECHDEBT.2019.00022

Wiese, M., Riebisch, M., & Schwarze, J. (2021). Preventing Technical Debt by Technical Debt Aware Project Management. Proceedings - 2021 IEEE/ACM International Conference on Technical Debt, TechDebt 2021, 84–93. https://doi.org/10.1109/TECHDEBT52882.2021.00018

Wolfart, D., Assunção, W. K. G., & Martinez, J. (2021). Variability Debt: Characterization, Causes and Consequences. SBQS ’21: Proceedings of the XX Brazilian Symposium on Software Quality. https://doi.org/10.1145/3488042.3488048

Xiao, L., Cai, Y., Kazman, R., Mo, R., & Feng, Q. (2016). Identifying and Quantifying Architectural Debt. Proceedings of the 38th International Conference on Software Engineering. https://doi.org/10.1145/2884781

Xiao, T., Wang, D., Mcintosh, S., Hata, H., Kula, R. G., Ishio, T., & Matsumoto, K. (2021). Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems. IEEE Transactions on Software Engineering, 1–1. https://doi.org/10.1109/TSE.2021.3115772

Yli-Huumo, J., Maglyas, A., Smolander, K., Haller, J., & Törnroos, H. (2016). Developing Processes to Increase Technical Debt Visibility and Manageability – An Action Research Study in Industry. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10027 LNCS, 368–378. https://doi.org/10.1007/978-3-319-49094-6_24

Zampetti, F., Serebrenik, A., & di Penta, M. (2020). Automatically Learning Patterns for Self-Admitted Technical Debt Removal. SANER 2020 - Proceedings of the 2020 IEEE 27th International Conference on Software Analysis, Evolution, and Reengineering, 355–366. https://doi.org/10.1109/SANER48275.2020.9054868

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2023-08-03

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Murillo, M. I., López, G., Spínola, R., Guzmán, J., Rios, N., & Pacheco, A. (2023). Identification and Management of Technical Debt: A Systematic Mapping Study Update. Journal of Software Engineering Research and Development, 11(1), 8:1 – 8:20. https://doi.org/10.5753/jserd.2023.2671

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