Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Related Work
Methodology
Results
Summary
References
All Topics
Computer Science
Software Engineering

Reproducibility in the technical debt domain

Profile image of Kristof SzabadosKristof Szabados

2022, Acta Universitatis Sapientiae, Informatica

https://doi.org/10.2478/AUSI-2021-00016
visibility

…

description

26 pages

link

1 file

Abstract

Context: It is crucial to understand how reproducible the measurement results in the scientific publications are, as reproducibility is one of the cornerstones of engineering. Objective: The goal of this study is to investigate the scientific publications presented at the premier technical debt conferences by understanding how reproducible the reported findings are. Method: We conducted a systematic literature review of 135 unique papers published at the "International Workshop on Managing Technical Debt" and the "International Conference on Managing Technical Debt", the premier scientific conference series on technical debt. Results: Only 44 of the investigated 135 papers presented numerical evidence and only 5 papers listed the tools, the availability of the tools, and the version of the tools used. For the rest of the papers additional information would have been needed for the potential reproducibility. One of the published papers even referred to a pornographic site as a source of a toolset for empirical research.

Related papers

Shedding some light on technical debt opinion
Carolyn Seaman

We identified and organized a number of statements about technical debt (TD Folklore list) expressed by practitioners in online websites, blogs and published papers. We chose 14 statements and we evaluated them through two surveys (37 practitioners answered the questionnaires), ranking them by agreement and consensus. The statements most agreed with show that TD is an important factor in software project management and not simply another term for “bad code”. This study will help the research community in identifying folklore that can be translated into research questions to be investigated, thus targeting attempts to provide a scientific basis for TD management.

View PDFchevron_right
Accepted Manuscript Identification and Analysis of the Elements Required to Manage Technical Debt by Means of a Systematic Mapping Study
Juan Garbajosa

Technical debt, a metaphor for the long-term consequences of weak software development, must be managed to keep it under control. The main goal of this article is to identify and analyze the elements required to manage technical debt.

View PDFchevron_right
Prevalence, common causes and effects of technical debt: Results from a family of surveys with the IT industry
Boris Perez

Journal of Systems and Software, 2021

Context: The technical debt (TD) metaphor describes actions made during various stages of software development that lead to a more costly future regarding system maintenance and evolution. According to recent studies, on average 25% of development effort is spent, i.e. wasted, on TD caused issues in software development organizations. However, further research is needed to investigate the relations between various software development activities and TD. Objective: The objective of this study is twofold. First, to get empirical insight on the understanding and the use of the TD concept in the IT industry. Second, to contribute towards precise conceptualization of the TD concept through analysis of causes and effects. Method: In order to address the research objective a family of surveys was designed as a part of an international initiative that congregates researchers from 12 countries-InsighTD. At country level, national teams ran survey replications with industry practitioners from the respective countries. Results: In total 653 valid responses were collected from 6 countries. Regarding the prevalence of the TD concept 22% of practitioners have only theoretical knowledge about it, and 47% have some practical experiences with TD identification or management. Further analysis indicated that senior practitioners who work in larger organizations, larger teams, and on larger systems are more likely to be experienced with TD management. Time pressure or deadline was the single most cited cause of TD. Regarding the effects of TD: delivery delay, low maintainability, and rework were the most cited. Conclusion: InsighTD is the first family of surveys on technical debt in software engineering. It provided a methodological framework that allowed multiple replication teams to conduct research activities and to contribute to a single dataset. Future work will focus on more specific aspects of TD management.

View PDFchevron_right
Technical Debt Principal Assessment Through Structural Metrics
IOANNIS STAMELOS

2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2017

One of the first steps towards the effective Technical Debt (TD) management is the quantification and continuous monitoring of the TD principal. In the current state-ofresearch and practice the most common ways to assess TD principal are the use of: (a) structural proxies—i.e., most commonly through quality metrics; and (b) monetized proxies—i.e., most commonly through the use of the SQALE (Software Quality Assessment based on Lifecycle Expectations) method. Although both approaches have merit, they seem to rely on different viewpoints of TD and their levels of agreement have not been evaluated so far. Therefore, in this paper, we empirically explore this relation by analyzing data obtained from 20 open source software projects and build a regression model that establishes a relationship between them. The results of the study suggest that a model of seven structural metrics, quantifying different aspects of quality (i.e., coupling, cohesion, complexity, size, and inheritance) can ac...

View PDFchevron_right
Technical Debt Tools: A Systematic Mapping Study
Guilherme Freitas

Proceedings of the 23rd International Conference on Enterprise Information Systems

Context: The concept of technical debt is a metaphor that contextualizes problems faced during software evolution that reflect technical compromises in tasks that are not carried out adequately during their development-they can yield short-term benefit to the project in terms of increased productivity and lower cost, but that may have to be paid off with interest later. Objective: This work investigates the current state of the art of technical debt tools by identifying which activities, functionalities and kind of technical debt are handled by existing tools that support the technical debt management in software projects. Method: A systematic mapping study is performed to identify and analyze available tools for managing technical debt based on a set of five research questions. Results: The work contributes with (i) a systematic mappping of current research on the field, (ii) a highlight of the most referenced tools, their main characteristics, their supported technical debt types and activities, and (iii) a discussion of emerging findings and implications for future research. Conclusions: Our study identified 50 TD tools where 42 of them are new tools, and 8 tools extend an existing one. Most of the tools address technical debt related to code, design, and/or architecture artifacts. Besides, the different TD management activities received different levels of attention. For example, TD identification is supported by 80% of the tools, whereas 30% of them handle the TD documentation activity. Tools that deal with TD identification and measurement activities are still predominant. However, we observed that recent tools focusing on TD prevention, replacement, and prioritization activities represent emergent research trends.

View PDFchevron_right
Estimation and Prediction of technical debt: a proposal
Alvine Belle

2019

Technical debt is a metaphor used to convey the idea that doing things in a "quick and dirty" way when designing and constructing a software leads to a situation where one incurs more and more deferred future expenses. Similarly to financial debt, technical debt requires payment of interest in the form of the additional development effort that could have been avoided if the quick and dirty design choices have not been made. Technical debt applies to all the aspects of software development, spanning from initial requirements analysis to deployment, and software evolution. Technical debt is becoming very popular from scientific and industrial perspectives. In particular, there is an increase in the number of related papers over the years. There is also an increase in the number of related tools and of their adoption in the industry, especially since technical debt is very pricey and therefore needs to be managed. However, techniques to estimate technical debt are inadequate, insufficient since they mostly focus on requirements, code, and test, disregarding key artifacts such as the software architecture and the technologies used by the software at hand. Besides, despite its high relevance, technical debt prediction is one of the least explored aspects of technical debt. To address these shortcomings, it is mandatory that I undertake research to: 1) improve existing techniques to properly estimate technical debt; 2) to determine the extent to which the use of prediction techniques to foresee and therefore avoid technical debt could help companies save money and avoid a potential bankruptcy in the subsequent years. The proposed research can have an important economic impact by helping companies save several millions. It can have a major scientific impact by leading to key findings that will be disseminated through patents, well-established scientific journals and conferences.

View PDFchevron_right
A Framework for Managing Interest in Technical Debt: An Industrial Validation
Alexandros Michailidis, Andre Feldman

Technical debt management entails the quantification of principal and interest. In our previous work we had introduced a framework for calculating the Technical Debt Breaking Point (TD-BP), which is a point in time where the accumulated interest becomes larger than the principal; thus the debt of the company is no longer sustainable after this point in time. In this paper, we instantiate this framework and validate its ability to assess the breaking point of source code modules in an industrial setting. The results of the validation suggest that the calculated TD-BP is strongly correlated to experts' opinion on the sustainability of modules, and that it can accurately rank components, based on their maintenance difficulty. CCS CONCEPTS • Software and its engineering → Software creation and management; Designing software; Software design engineering • Social and professional topics → Professional topics; Management of computing and information systems; Quality assurance KEYWORDS

View PDFchevron_right
Monitoring Technical Debt in an Industrial Setting
IOANNIS STAMELOS

Proceedings of the Evaluation and Assessment on Software Engineering

Context: Technical Debt (TD) quantification has been studied in the literature and is supported by various tools; however, there is no common ground on what information shall be presented to stakeholders. Similarly to other quality monitoring processes, it is desirable to provide several views of quality through a dashboard, in which metrics concerning the phenomenon of interest are displayed. Objective: The aim of this study is to investigate the indicators that shall be presented in such a dashboard, so as to: (a) be meaningful for industrial stakeholders, (b) present all necessary information, and (c) be simple enough so that stakeholders can use them. Method: We explore TD Management (TDM) activities (i.e., measurement, prioritization, repayment) and choose the main concepts that need to be visualized, based on existing literature and toolsupport. Next, we perform a survey with 60 software engineers (i.e., architects, developers, etc.) working for 11 software development companies located in 9 countries, to understand their needs for TDM. Results / Conclusions: The results of the study suggest that different stakeholders need a different view of the quality dashboard, but also some commonalities can be identified. For example, on the one hand, managers are mostly interested in financial concepts, whereas on the other hand developers are more interested in the nature of the problems that exist in the code. The outcomes of this study can be useful to both researchers and practitioners, in the sense that the former can focus their efforts on aspects that are meaningful to industry, whereas the latter to develop meaningful dashboards, with multiple views.

View PDFchevron_right
Evolution of Technical Debt: An Exploratory Study
Md Abdullah Al Mamun

2019

Context: Technical debt is known to impact maintainability of software. As source code files grow in size, maintainability becomes more challenging. Therefore, it is expected that the density of te ...

View PDFchevron_right
Technical Debt: Identify, Measure and Monitor
Nikhil Oswal

ArXiv, 2019

Technical Debt is a term begat by Ward Cunningham to signify the measure of adjust required to put a software into that state which it ought to have had from the earliest starting point. Often organizations need to support continuous and fast delivery of customer value both in short and a long-term perspective and later have to compromise with the quality and productivity of the software. So, a simple solution could be to repay the debts as and when they are encountered to avoid maintainability cost and subsequent delays. Therefore, it has become inevitable to identify and come up with techniques so as to know when, what and how TD items to repay. This study aims to explore how to identify, measure and monitor technical debt using SonarQube and PMD.

View PDFchevron_right

References (72)

  1. S. Ahmadjee, C. Mera-Gómez, R. Bahsoon, Assessing Smart Contracts Security Technical Debts, 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, pp. 6-15. ⇒ 350
  2. A. Aldaeej, C. Seaman, From Lasagna to Spaghetti: A Decision Model to Man- age Defect Debt, 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, pp. 67-71. ⇒ 348
  3. R. Alfayez, W. Alwehaibi, R. Winn, E. Venson, B. Boehm, A systematic liter- ature review of technical debt prioritization, In Proceedings of the 3rd Interna- tional Conference on Technical Debt (TechDebt '20), 2020, pp. 1-10. ⇒ 341
  4. R. R. Alfayez, P. Behnamghader, K. Srisopha, B. Boehm, An Exploratory Study on the Influence of Developers in Technical Debt, 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, pp. 1-10. ⇒ 348
  5. S. Alhusain, Predicting Relative Thresholds for Object Oriented Metrics, 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, pp. 55-63. ⇒ 351
  6. T. Amanatidis, N. Mittas, A. Chatzigeorgiou, A. Ampatzoglou, L. Angelis, The Developer's Dilemma: Factors Affecting the Decision to Repay Code Debt, 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, pp. 62-66. ⇒ 348
  7. A. Ampatzoglou, A. Ampatzoglou, A. Chatzigeorgiou, P. Avgeriou, P. Abra- hamsson, A. Martini, U. Zdun, K. Systa, The Perception of Technical Debt in the Embedded Systems Domain: An Industrial Case Study, 2016 IEEE 8th In- ternational Workshop on Managing Technical Debt (MTD), 2016, pp. 9-16. ⇒ 347
  8. A. Ampatzoglou, A. Michailidis, C. Sarikyriakidis, A. Ampatzoglou, A. Chatzi- georgiou, P. Avgeriou, A Framework for Managing Interest in Technical Debt: An Industrial Validation, 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, pp. 115-124. ⇒ 337, 347, 349, 352
  9. M. F. Aniche, G. A. Oliva, M. A. Gerosa, Are the Methods in Your Data Access Objects (DAOs) in the Right Place? A Preliminary Study, Sixth International Workshop on Managing Technical Debt (MTD '14), 2014, pp. 47-50. ⇒ 345
  10. P. C. Avgeriou, D. Taibi, A. Ampatzoglou, A. F. Fontana, T. Besker, A. Chatzi- georgiou, V. Lenarduzzi, A. Martini, A. Moschou, I. Pigazzini, N. Saarimäki, D. Sas, S. Toledo, A. Tsintzira. An Overview and Comparison of Technical Debt Measurement Tools, in IEEE Software, vol. 38, no. 3, pp. 61-71, May-June 2021. ⇒ 336, 338
  11. B. Barta, G. Manz, I. Siket, R. Ferenc, Challenges of SonarQube Plug-In Main- tenance, IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), 2019, pp. 574-578. ⇒ 336, 338
  12. T. Besker, A. Martini, J. Bosch, Technical debt cripples software developer pro- ductivity: a longitudinal study on developers' daily software development work, In Proceedings of the 2018 IEEE/ACM International Conference on Technical Debt (TechDebt '18), 2018, pp. 105-114. ⇒ 341
  13. J. Bohnet, J. Döllner, Monitoring code quality and development activity by software maps, In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11), ACM, 2011, New York, NY, USA, 9-16. ⇒ 341
  14. N. Brown, Y.g Cai, Y. Guo, R. Kazman, M. Kim, P. Kruchten, E. Lim, A. MacCormack, R. L. Nord, I. Ozkaya, R. Sangwan, C. Seaman, K. Sullivan, N. Zazworka, Managing technical debt in software-reliant systems, In Proceedings of the FSE/SDP workshop on Future of software engineering research (FoSER '10), 2010, pp. 47-52. ⇒ 337, 338, 341, 342, 343
  15. R. Capilla, T. Mikkonen, C. Carrillo, F. A. Fontana, I. Pigazzini, V. Lenarduzzi, Impact of Opportunistic Reuse Practices to Technical Debt, 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, pp. 16-25. ⇒351
  16. A. Chatzigeorgiou, A. Ampatzoglou, A. Ampatzoglou, T. Amanatidis, Estimat- ing the breaking point for technical debt, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, pp. 53-56. ⇒ 346
  17. Carnegie Mellon University, Software Engineering Institute, Managing Technical Debt Workshops, previous editions, 2010, https://resources.sei.cmu.edu/news- events/events/techdebt/past.cfm, last visited: 2021.10.05. ⇒ 342, 345
  18. Y. Crespo, A. Gonzalez-Escribano, M. Piattini, Carrot and Stick approaches revisited when managing Technical Debt in an educational context, 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, pp. 99-108. ⇒ 351
  19. D. Cruz, A. Santana, E. Figueiredo, Detecting bad smells with machine learning algorithms: an empirical study, In Proceedings of the 3rd International Confer- ence on Technical Debt (TechDebt '20), 2020, ACM, New York, NY, USA, 31-40. ⇒ 350
  20. W. Cunningham, The WyCash Portfolio Management System, In Addendum to the proceedings on Object-oriented programming systems, languages, and appli- cations (Addendum) (OOPSLA '92). ACM, NY, USA, 1992, 29-30. ⇒ 337
  21. B. Curtis, J. Sappidi, A. Szynkarski, Estimating the size, cost, and types of Technical Debt, Third International Workshop on Managing Technical Debt (MTD'12), 2012, pp. 49-53. ⇒ 344
  22. R. R. de Almeida, R. do Nascimento Ribeiro, C. Treude, U. Kulesza, Business-Driven Technical Debt Prioritization: An Industrial Case Study, 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, pp. 74-83. ⇒ 351
  23. M. A. de Freitas Farias, M. G. de Mendonça Neto, A. B. d. Silva, R. O. Spínola, A Contextualized Vocabulary Model for identifying technical debt on code com- ments, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, pp. 25-32. ⇒ 346
  24. J. de Groot, A. Nugroho, T. Bäck, J. Visser, What is the value of your software?, Third International Workshop on Managing Technical Debt (MTD'12), 2012, pp. 37-44. ⇒ 344
  25. M. di Biase, A. Rastogi, M. Bruntink, A. van Deursen, The Delta Maintain- ability Model: Measuring Maintainability of Fine-Grained Code Changes, 2019 IEEE/ACM International Conference on Technical Debt (TechDebt), 2019, pp. 113-122. ⇒ 349
  26. D. Falessi, R. Kazman, Worst Smells and Their Worst Reasons, 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, pp. 45-54. ⇒351
  27. D. Falessi, P. Kruchten, R. L. Nord, I. Ozkaya, Technical debt at the crossroads of research and practice: report on the fifth international workshop on managing technical debt, 2014, SIGSOFT Softw. Eng. Notes 39, 2, 31-33. ⇒ 343, 345
  28. D. Falessi, A. Reichel, Towards an open-source tool for measuring and visual- izing the interest of technical debt, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, pp. 1-8. ⇒ 346
  29. D. Falessi, A. Voegele, Validating and prioritizing quality rules for managing technical debt: An industrial case study, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, pp. 41-48. ⇒ 347
  30. F. A. Fontana, V. Ferme, S. Spinelli, Investigating the impact of code smells debt on quality code evaluation, Third International Workshop on Managing Technical Debt (MTD'12), 2012, pp. 15-22. ⇒ 344
  31. F. A. Fontana, V. Ferme, M. Zanoni, R. Roveda, Towards a prioritization of code debt: A code smell Intensity Index, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, pp. 16-24. ⇒ 346
  32. F. A. Fontana, R. Roveda, M. Zanoni, Technical Debt Indexes Provided by Tools: A Preliminary Discussion, 2016 IEEE 8th International Workshop on Managing Technical Debt (MTD), 2016, pp. 28-31. ⇒ 347
  33. I. Gat, J. D. Heintz, From assessment to reduction: how cutter consortium helps rein in millions of dollars in technical debt, In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11), 2011, Waikiki, Honolulu, HI, USA, 24-26. ⇒ 343
  34. R. Gomes, C. Siebra, G. Tonin, A. Cavalcanti, Fabio Q.B. da Silva, Andre L.M. Santos, R. Marques, An extraction method to collect data on defects and effort evolution in a constantly modified system, In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11), 2011, Waikiki, Honolulu, HI, USA, 27-30. ⇒ 343
  35. I. Griffith, D. Reimanis, C. Izurieta, Z. Codabux, A. Deo, B. Williams, The Cor- respondence Between Software Quality Models and Technical Debt Estimation Approaches, Sixth International Workshop on Managing Technical Debt (MTD '14), 2014, pp. 19-26. ⇒ 345
  36. I. Khomyakov, Z. Makhmutov, R. Mirgalimova, A. Sillitti, 2019, Automated Measurement of Technical Debt: A Systematic Literature Review, In Proceed- ings of the 21st International Conference on Enterprise Information Systems - Volume 2(ICEIS), 2019, pages 95-106. ⇒ 338
  37. J. R. Lahti, A. -P. Tuovinen, T. Mikkonen, Experiences on Managing Techni- cal Debt with Code Smells and AntiPatterns, 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, pp. 36-44. ⇒ 351
  38. V. Lenarduzzi, T. Besker, D. Taibi, A. Martini, F. A. Fontana, Techni- cal Debt Prioritization: State of the Art. A Systematic Literature Review, arXiv:1904.12538. ⇒ 338
  39. V. Lenarduzzi, F. Lomio, H. Huttunen, D. Taibi, Are SonarQube Rules Inducing Bugs?, IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER), 2020, pp. 501-511. ⇒ 336, 339
  40. V. Lenarduzzi, V. Mandić, A. Katin, D. Taibi, How long do Junior Develop- ers take to Remove Technical Debt Items?, In Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Mea- surement(ESEM '20), 2020, Article 30, 1-6. ⇒ 336, 338
  41. A. Lerina, L. Nardi, Investigating on the Impact of Software Clones on Technical Debt, 2019 IEEE/ACM International Conference on Technical Debt (TechDebt), 2019, pp. 108-112. ⇒ 349
  42. E. d. S. Maldonado, E. Shihab, Detecting and quantifying different types of self- admitted technical Debt, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, pp. 9-15. ⇒ 346
  43. M. A. Al Mamun, C. Berger, J. Hansson, Explicating, Understanding, and Man- aging Technical Debt from Self-Driving Miniature Car Projects, Sixth Interna- tional Workshop on Managing Technical Debt (MTD '14), 2014, pp. 11-18. ⇒ 346
  44. D. Marcilio, R. Bonifácio, E. Monteiro, E. Canedo, W. Luz, G. Pinto, Are Static Analysis Violations Really Fixed? A Closer Look at Realistic Usage of Sonar- Qube, IEEE/ACM 27th International Conference on Program Comprehension (ICPC), 2019, pp. 209-219. ⇒ 336, 339
  45. J. Y. Monteith, J. D. McGregor, Exploring software supply chains from a tech- nical debt perspective, 4th International Workshop on Managing Technical Debt (MTD '13), 2013, pp. 32-38. ⇒ 344
  46. A. Mori, G. Vale, M. Viggiato, J. Oliveira, E. Figueiredo, E. Cirilo, P. Jamshidi, C. Kastner, Evaluating Domain-Specific Metric Thresholds: An Empirical Study, 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, pp. 41-50. ⇒ 348
  47. A. Nugroho, J. Visser, T. Kuipers, An empirical model of technical debt and interest, In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11), 2011, Waikiki, Honolulu, HI, USA, 1-8. ⇒ 343
  48. E. Parodi, S. Matalonga, D. Macchi, M. Solari, Comparing technical debt in stu- dent exercises using test driven development, test last and ad hoc programming, 2016 XLII Latin American Computing Conference (CLEI), 2016, pp. 1-10. ⇒ 336, 338
  49. I. Pigazzini, F. A. Fontana, V. Lenarduzzi, D. Taibi, Towards microservice smells detection, In Proceedings of the 3rd International Conference on Technical Debt (TechDebt '20), 2020, ACM, New York, NY, USA, 92-97. ⇒ 350
  50. R. Plösch, J. Bräuer, M. Saft and C. Körner, Design Debt Prioritization: A De- sign Best Practice-Based Approach, 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, pp. 95-104. ⇒ 348
  51. W. W. Royce, Managing the development of large software systems, Proceedings of IEEE WESCON, 1970, pp. 1-9. ⇒ 337
  52. N. Saarimäki, M. T. Baldassarre, V. Lenarduzzi and S. Romano, On the Ac- curacy of SonarQube Technical Debt Remediation Time, 45th Euromicro Con- ference on Software Engineering and Advanced Applications (SEAA), 2019, pp. 317-324. ⇒ 336, 338
  53. V. Lenarduzzi, N. Saarimäki, D. Taibi, On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem, 2019 IEEE/ACM International Conference on Technical Debt (TechDebt), 2019, pp. 98-107. ⇒ 349
  54. A. Shapochka, B. Omelayenko, Practical Technical Debt Discovery by Match- ing Patterns in Assessment Graph, 2016 IEEE 8th International Workshop on Managing Technical Debt (MTD), 2016, pp. 32-35. ⇒ 347
  55. V. Singh, W. Snipes, N. A. Kraft, A Framework for Estimating Interest on Tech- nical Debt by Monitoring Developer Activity Related to Code Comprehension, Sixth International Workshop on Managing Technical Debt (MTD '14), 2014, pp. 27-30. ⇒ 345
  56. A. Sundelin, J. Gonzalez-Huerta, K. Wnuk, The hidden cost of backward com- patibility: when deprecation turns into technical debt -an experience report, In Proceedings of the 3rd International Conference on Technical Debt (TechDebt '20), 2020, ACM, New York, NY, USA, 67-76. ⇒ 350
  57. K. Szabados and A. Kovács, Technical debt of standardized test software, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, pp. 57-60. ⇒ 346
  58. R. Szalay, Á. Sinkovics, Z. Porkoláb, The Role of Implicit Conversions in Erro- neous Function Argument Swapping in C++, 20th International Working Con- ference on Source Code Analysis and Manipulation (SCAM), 2020, pp. 203-214. ⇒ 336
  59. J. Tan, D. Feitosa, P. Avgeriou, An empirical study on self-fixed technical debt, In Proceedings of the 3rd International Conference on Technical Debt (TechDebt '20), 2020, ACM, New York, NY, USA, 11-20. ⇒ 350
  60. A. Tornhill, Prioritize Technical Debt in Large-Scale Systems Using CodeScene, 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, pp. 59-60. ⇒ 348
  61. P. Wang, J. Yang, L. Tan, R. Kroeger, J. David Morgenthaler, Generating pre- cise dependencies for large software, 4th International Workshop on Managing Technical Debt (MTD '13), 2013, pp. 47-50. ⇒ 345
  62. N. Zazworka, C. Seaman, F. Shull, Prioritizing design debt investment opportu- nities, In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD '11), 2011, pp. 39-42. ⇒ 344
  63. * * * , MTD '11: Proceedings of the 2nd Workshop on Managing Technical Debt, 2011, ACM, New York, NY, USA. ⇒ 337, 338, 341, 343
  64. * * * , MTD '12: Proceedings of the Third International Workshop on Managing Technical Debt, 2012, IEEE Press ⇒ 337, 338, 341, 343, 344
  65. * * * , MTD '13: Proceedings of the 4th International Workshop on Managing Technical Debt, 2013, IEEE Press. ⇒ 337, 338, 341, 343, 344
  66. * * * , MTD '14: Proceedings of the 2014 Sixth International Workshop on Man- aging Technical Debt, 2014, IEEE Computer Society. ⇒ 337, 338, 341, 343, 345
  67. * * * , 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 2015, IEEE Computer Society. ⇒ 337, 338, 341, 343, 346
  68. * * * , 2016 IEEE 8th International Workshop on Managing Technical Debt (MTD), 2016, IEEE Computer Society. ⇒ 337, 338, 341, 343, 347
  69. * * * , 2018 IEEE/ACM International Conference on Technical Debt (TechDebt), 2018, IEEE Computer Society. ⇒ 337, 338, 341, 343, 347
  70. * * * , 2019 IEEE/ACM International Conference on Technical Debt (TechDebt), 2019, IEEE Computer Society. ⇒ 337, 338, 341, 343, 348
  71. * * * , TechDebt '20: Proceedings of the 3rd International Conference on Technical Debt, 2020, ACM, New York, NY, USA. ⇒ 337, 338, 341, 343, 350
  72. * * * , 2021 IEEE/ACM International Conference on Technical Debt (TechDebt), 2021, IEEE Computer Society. ⇒ 337, 338, 341, 343, 350

Related papers

On the Uncertainty of Technical Debt Measurements
Rachael Luhr, Derek Reimanis, Clemente Izurieta, Isaac Griffith

2013 International Conference on Information Science and Applications (ICISA), 2013

Measurements are subject to random and systematic errors, yet almost no study in software engineering makes significant efforts in reporting these errors. Whilst established statistical techniques are well suited for the analysis of random error, such techniques are not valid in the presence of systematic errors. We propose a departure from de-facto methods of reporting results of technical debt measurements for more rigorous techniques drawn from established methods in the physical sciences. This line of inquiry focuses on technical debt calculations; however it can be generalized to quantitative software engineering studies. We pose research questions and seek answers to the identification of systematic errors in metric-based tools, as well as the reporting of such errors when subjected to propagation. Exploratory investigations reveal that the techniques suggested allow for the comparison of uncertainties that come from differing sources. We suggest the study of error propagation of technical debt is a worthwhile subject for further research and techniques seeded from the physical sciences present viable options that can be used in software engineering reporting.

View PDFchevron_right
Investigating technical debt folklore: Shedding some light on technical debt opinion
Nico Zazworka

2013 4th International Workshop on Managing Technical Debt (MTD), 2013

We identified and organized a number of statements about technical debt (TD Folklore list) expressed by practitioners in online websites, blogs and published papers. We chose 14 statements and we evaluated them through two surveys (37 practitioners answered the questionnaires), ranking them by agreement and consensus. The statements most agreed with show that TD is an important factor in software project management and not simply another term for "bad code". This study will help the research community in identifying folklore that can be translated into research questions to be investigated, thus targeting attempts to provide a scientific basis for TD management.

View PDFchevron_right
An Overview and Comparison of Technical Debt Measurement Tools
Saulo Soares de Toledo

IEEE Software, 2021

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

View PDFchevron_right
Investigate,Identify and Estimate the Technical Debt: A Systematic Mapping Study
Hany Ammar

2018

Context: Technical Debt (TD) is a metaphor that refers to short-term solutions in software development that may affect the cost to the software development life cycle. Objective: To explore and understand TDrelated to the software industry as well as an overview on the current state of TD research. Forty-three TD empirical studies were collected for classification and analyzation. Goals: Classify TD types, find the indicators used to detect TD, find the estimators used to quantify the TD, evaluate how researchers investigate TD. Method: By performing a systematic mapping study to identify and analyze the TD empirical studies which published between 2014 and 2017. Results: We present the most common indicators and evaluators to identify and evaluate the TD, and we gathered thirteen types of TD. We showed some ways to investigate the TD, and used tools in the selected studies. Conclusion: The outcome of our systematic mapping study can help researchers to identify interestand future i...

View PDFchevron_right
The Gap between the Admitted and the Measured Technical Debt: An Empirical Study
Tilen hliš

Applied Sciences

Technical debt is a well understood and used concept in IT development. The metaphor, rooted in the financial world, captures the amount of work that development teams owe to a product. Every time developers take a shortcut within development, the technical debt accumulates. Technical debt identification can be accomplished via manual reporting on the technical debt items, which is called self-admitted technical debt. Several specialised methods and tools have also emerged that promise to measure the technical debt. Based on experience in the community, the impression emerged that the measured technical debt is of a significantly different amount than the self-admitted debt. In this context, we decided to perform empirical research on the possible gap between the two. We investigated 14 production-grade software products while determining the amount of accumulated technical debt via (a) a self-admitting procedure and (b) measuring the debt. The outcomes show clearly the significant ...

View PDFchevron_right

Related topics

  • Computer Science
  • Measurement and Evaluation
  • Software Quality
  • Technical Debt
  • Reproducibility of Results
  • Managing Technical Debt
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved