Неясно, какие меры и методы Технического Долга (ТД) следует использовать в каких обстоятельствах, и за последние несколько лет было предложено множество определений и подходов. Он почти никогда не основывается на существующих подходах, и их обоснованность учитывается в очень небольшом количестве проектов. В результате практикующие находят такие подходы запутанными и поэтому затрудняются их использовать. Цели: чтобы помочь практикам и исследователям понять доступные альтернативы и правильно их применять, в этой статье исследуются способы оценки ТД с использованием автоматизированных инструментов. Методы: систематический обзор был проведен по 1063 статьям из трех крупных электронных библиотек, которые были собраны из наиболее актуальных цифровых баз данных. Остальные 46 работ детально анализируются после применения всех этапов фильтрации. Полученные результаты: найденные статьи редко разрабатывают или проверяют существующие методы, и в основном они предлагают новые подходы к измерению ТД. Выводы: не существует независимых оценок моделей, предлагаемых в этой области, и эта область еще не развита. Отсутствие консолидации очевидно в литературе, поскольку авторы сосредотачиваются на отстаивании новых подходов. Кроме того, почти все подходы автоматизированы лишь до некоторой степени в тех статьях, в которых они предлагаются, и редко поддерживаются. Более того, большинство предлагаемых инструментов представляют собой прототипы, которые не обслуживаются и могут использоваться для поддержки исследований, проанализированных в статье. Из-за этих фактов практикующим специалистам сложно использовать такие методы.
It is unclear what Technical Debt (TD) measures and methods should be used in what circumstances, and many definitions and approaches have been proposed over the past few years. It is almost never based on existing approaches, and their validity is considered in very few projects. As a result, practitioners find such approaches confusing and therefore have difficulty using them. Aims: To help practitioners and researchers understand the available alternatives and apply them correctly, this article explores ways to evaluate TD using automated tools. METHODS: A systematic review was conducted of 1,063 articles from three major digital libraries that were collected from the most relevant digital databases. The remaining 46 papers were analyzed in detail after applying all filtering steps. The results: the articles found rarely develop or test existing methods, and they mostly propose new approaches to measuring TD. Conclusions: There are no independent evaluations of the models proposed in this area, and the field is still underdeveloped. The lack of consolidation is evident in the literature as authors focus on advocating new approaches. Furthermore, almost all approaches are automated only to some degree in the articles in which they are proposed and are rarely supported. Moreover, most of the proposed tools are prototypes that are not maintained and can be used to support the research analyzed in the article. Because of these facts, it is difficult for practitioners to use such methods.
1. Barry Boehm, Paul Grünbacher, and Robert O. Briggs. Developing Groupware for Requirements Negotiation: Lessons Learned. IEEE Computer Society Press. 2001. Vol. 18. No. 3. P. 46–55.
2. Coman I. D., Sillitti A., Succi G. Investigating the usefulness of pair-programming in a mature agile team. 9th International Conference on extreme Programming and Agile Processes in Software Engineering (XP2008). 2008. P. 127–136.
3. Corral L., Sillitti A., Succi G. Software development processes for mobile systems: Is agile really taking over the business? 1st International Workshop on Mobile-Enabled Systems (MOBS 2013) at ICSE. 2013. P. 19–24.
4. Cunningham W. The wydahs portfolio management system, addendum to the proceedings on object-oriented programming systems, languages, and applications (addendum). OOPSLA. 1992.
5. Tom E., Aurum A., Vidgen R. An exploration of technical debt. Journal of Systems and Software. 2013. Vol. 86. No. 3. P. 1498–1516.
6. Lenarduzzi V., Sillitti A., Taibi D. Analyzing forty years of software maintenance models. 39th International Conference on Software Engineering. 2017. P. 146–148.
7. Falessi D., Shaw M., Shull F., Mullen K., Stein M.K. Practical considerations, challenges, and requirements of tool-support for managing technical debt. 2013 4th International Workshop on Managing Technical Debt. 2013. P. 16–19.
8. Chatzigeorgiou A., Ampatzoglou A., Ampatzoglou A., Amanatidis T. Estimating the breaking point for technical debt. 7th International Workshop on Managing Technical Debt (MTD). 2015. P. 53–56.
9. Ciolkowski M., Guzman L., Trendowicz A., Salfner F. Lessons learned from the prodebt research project on planning technical debt strategically. PROFES. 2017. P. 523–534.
10. Khomyakov I., Makhmutov Z., Mirgalimova R., Sillitti A. Automated measurement of technical debt: A systematic literature review. 21st International Conference on Enterprise Information Systems. 2019.
11. Kitchenham B., Charters S. Guidelines for performing systematic literature reviews in software engineering (version 2.3). Technical report, Keele University and University of Durham. 2007.
12. Letouzey.J. The SQALE method for evaluating technical debt. In Managing Technical Debt (MTD). 2012 Third International Workshop on Managing Technical Debt (MTD). 2012. P. 31–36.
13. Letouzey. J., Ilkiewicz M. Managing technical debt with the SQALE method. IEEE software. 2012. P. 44–51.
14. Curtis B., Sappidi J., Szynkarski A. Estimating the size, cost, and types of technical debt. Proceedings of the Third International Workshop on Managing Technical Debt. 2012. P. 49–53.
15. Nugroho A., Visser J., Kuipers T. An empirical model of technical debt and interest. Proceedings of the 2nd Workshop on Managing Technical Debt (ACM). 2011. P. 1–8.
16. Mayr A., Pl ̈osch R., K ̈orner С. A benchmarking-based model for technical debt calculation. In Quality Software (QSIC). 2014. P. 305–314.
17. Ploesch R., Gruber H, Pomberger G., Saft M., Schiffer S. Tool support for expert -centered code assessments. 1st International Conference on Software Testing, Verification, and Validation. 2008. P. 258–267.
18. Skourletopoulos G., Mavromoustakis C.X., Mastorakis G., Rodrigues J.J., Chatzimisios P., Batalla J.M. A fluctuation-based modelling approach to quantification of the technical debt on mobile cloud-based service level IEEE Globecom Workshops (GC Wkshps). 2015. P. 1–6.
19. Kamei Y., Maldonado E.D., Shihab E., Ubayashi N. Using analytics to quantify interest of self-admitted technical debt. QuASoQ/TDA@ APSEC. 2016. P. 68–71.
20. Marinescu R. Assessing technical debt by identifying design flaws in software systems. IBM Journal of Research and Development. 2012. Vol. 56. P. 9.
21. Singh V., Snipes W., Kraft N. A framework for estimating interest on technical debt by monitoring developer activity related to code comprehension. Sixth International Workshop on Managing Technical Debt (MTD). 2014. P. 27–30.
22. Li Z., Liang P., Avgeriou P., Guelfi N., Ampatzoglou A. An empirical investigation of modularity metrics for indicating architectural technical debt. Proceedings of the 10th international ACM Sigsoft conference on Quality of software architectures QoSA. 2014. P. 119–128.
23. Maldonado E., Shihab E. Detecting and quantifying different types of self-admitted technical debt. IEEE 7th International Workshop on Managing Technical Debt (MTD). 2015. P. 9–15.
24. Almeida R.R., Treude C., Kulesza. U. A business-driven technical debt prioritization framework. 35th International Conference on Software Maintenance and Evolution (ICSME’19). 2019. P. 181–185.
25. Mendes T.S., Gomes F.G.S., Goncalves D., Mendonca M.G., Novais R.L., Spinola. R.O. VisminerTD: a tool for automatic identification and interactive monitoring of the evolution of technical debt items. Journal of the Brazilian Computer Society. 2018. Vol. 25. P. 1–28.
26. Parthiban D.G. Examination of tools for managing different dimensions of technical debt. CoRR, abs/1904.11062. 2019. 20 р.
27. Arvedahl S. Introducing debt grep, a tool for fighting technical debt in base station software. International Conference on Technical Debt. 2018. P. 51–52.
28. Biaggi A., Fontana A., Roveda R. An architectural smells detection tool for c and c++ projects. 44th Euro micro-Conference on Software Engineering and Advanced Applications (SEAA). 2018. P. 417–420.
29. Lenarduzzi V., Martini A., Taibi D., Tamburri D.A. Towards surgically-precise technical debt estimation: Early results and research roadmap. In Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE2019. 2019. P. 37–42.
30. Bougouffa S., Dong Q.H., Diehm S., Gemein F., Vogel-Heuser B. Technical debt indication in plc code for automated production systems: Introducing a domain specific static code analysis tool. IFAC-PapersOnLine. 2018. Vol. 51 P. 70–75.
31. Soudris D., Papadopoulos L., Kessler C.W., Kehagias D.D., Papadopoulos A.I., Seferlis P., Chatzigeorgiou A., Ampatzoglou A., Thibault S., Namyst R., Pleiter D., Gaydadjiev G., Becker T., Haefele M. Exa2pro programming environment: Architecture and applications Proceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation. 2018. P. 202–209.
32. Griffith I., Reimanis D., Izurieta C., Codabux Z., Deo A., Williams B. The correspondence between software quality models and technical debt estimation approaches. Sixth International Workshop on Managing Technical Debt (MTD). 2014. P. 19–26.
33. Izurieta C., Griffith I., Reimanis D., Luhr R. On the uncertainty of technical debt measurements. Information Science and Applications (ICISA). 2013. P. 1–4.
34. Lenarduzzi V., Saarim ̈aki N., Taibi D. The technical debt dataset. International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE’19. 2019. P. 2–11.
35. Zazworka N., Vetro’ A., Izurieta C., Wong S., Cai Y., Seaman C., Shull F. Comparing four approaches for technical debt identification. Software Quality Journal.2014. Vol. 22. P. 403–426.
36. Sierra G., Shihab E., Kamei Y. A survey of self-admitted technical debt. Journal of Systems and Software. 2019. Vol. 152. P. 70–82.
37. Parodi E., Matalonga S., Macchi D., ınSolari M. Comparing technical debt in student exercises using test driven development, test last and ad hoc programming. Computing Conference (CLEI). 2016. P. 1–10.
38. Luhr L.R. The application of technical debt mitigation techniques to a multidisciplinary software project. PhD thesis, Montana State University-Bozeman, College of Engineering. 2015. 98 р.
39. Monteith J., McGregor J. Exploring software supply chains from a technical debt perspective. 4th International Workshop on Managing Technical Debt. 2013. P. 32–38.
40. Britsman E., ̈Tanriverdi O. Identifying technical debt impact on maintenance effort-an industrial case study. 2015. 70 р.