CoChat
Sign Up
Cited by 116Software Engineering Techniques and PracticesSoftware Engineering ResearchAdvanced Software Engineering Methodologies

Analyzing the concept of technical debt in the context of agile software development: A systematic literature review

Open access
Behutiye, Woubshet Nema, Rodríguez, Pilar, Oivo, Markku, Tosun, Ayşe·2017·Information and Software Technology
DOI 10.1016/j.infsof.2016.10.004
Open this paper in CoChat — free accountOpen PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.infsof.2016.10.004

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A possible reason participants are skeptical about stability metrics showing trade-off is that the metrics will show this trade-off not in short but in long term; if a team does not constantly refactor, in the short term they will be faster and not lose stability, but in the mid to long term technical debt will lead to lower pace and lower stability.

    “A possible reason is that the metrics will show this trade-off not in short but in long term. If a team decides to not constantly refactor the code base, in the short term they will be faster and not lose stability. However, in the mid to long term the technical dept will lead to lower pace and to lower stability [16].”

    From Measuring Software Delivery Performance Using the Four Key Metrics of DevOps (2021) · cites this paper as [16]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 37

  1. Schmid, K. (2013). A formal approach to technical debt decision making. In Proceedings of the 9th international ACM Sigsoft conference on Quality of software architectures (pp. 153-162). ACM.
  2. Bellomo, S., Nord, R. L., & Ozkaya, I. (2013). A study of enabling factors for rapid fielding combined practices to balance speed and stability. pp. 982-991.
  3. Stochel, M. G., Wawrowski, M. R., & Waskiel, J. J. (2012). Adaptive agile performance modeling and testing. Computer Software and Applications Conference Workshops (COMPSACW), 2012 IEEE 36th Annual, pp. 446-451.
  4. Torkar, R., Minoves, P., & Garrigós, J. (2011). Adopting Free/Libre/Open source software practices, techniques and methods for industrial use*. Journal of the Association for Information Systems, 12(1), 88-122.
  5. Lane, J. A., Koolmanojwong, S., & Boehm, B. (2013). Affordable systems: Balancing the capability, schedule, flexibility, and technical debt tradespace. 2. pp. 1391-1405.
  6. Stolberg, S. (2009). Enabling agile testing through continuous integration. Agile Conference, 2009. AGILE'09. 369-374
  7. Abrahamsson, P., Babar, M. A., & Kruchten, P. (2010). Agility and architecture: Can they coexist? IEEE Software, 27(2), 16-22.
  8. Brown, N., Nord, R. L., Ozkaya, I., & Pais, M. (2011). Analysis and management of architectural dependencies in iterative release planning. Software Architecture (WICSA), 2011 9th Working IEEE/IFIP Conference on, 103-112.
  9. Holvitie, J., & Leppänen, V. (2013). DebtFlag: Technical debt management with a development environment integrated tool. pp. 20-27.
  10. Oualid Ktata and Ghislain Lévesque. 2010. Designing and implementing a measurement program for Scrum teams: what do agile developers really need and want? In Proceedings of the Third C* Conference on Computer Science and Software Engineering (C3S2E
  11. Bavani, R. (2012). Distributed agile, agile testing, and technical debt. IEEE Software, 29(6), 28-33.
  12. Davis, N. (2013). Driving quality improvement and reducing technical debt with the definition of done. Agile Conference (AGILE), 2013, pp. 164-168.
  13. Koolmanojwong, S., & Lane, J. A. (2013). Enablers and inhibitors of expediting systems engineering. 16. pp. 483-491.
  14. Frank, A., & Hartel, C. (2009). Feature teams collaboratively building products from READY to DONE. Agile Conference, 2009. AGILE '09. 320-325
  15. Birkeland, J. O. (2010). From a timebox tangle to a more flexible flow. In Agile Processes in Software Engineering and Extreme Programming (pp. 325-334). Springer Berlin Heidelberg.
  16. Israel Gat and John D. Heintz. 2011. 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). ACM, New York, NY, USA, 24-26.
  17. Ferna ńdez-Sa ńchez, C., Di áz, J., Pe ŕez, J., & Garbajosa, J. (2014). Guiding flexibility investment in agile architecting. System Sciences (HICSS), 2014 47th Hawaii International Conference on, pp. 4807-4816.
  18. Antinyan, V., Staron, M., Meding, W., Osterstrom, P., Wikstrom, E., Wranker, J., Hansson, J. (2014). Identifying risky areas of software code in Agile/Lean software development: An industrial experience report. Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE), 2014 Software Evolution Week -IEEE Conference on, 154-163.
  19. Nord, R. L., Ozkaya, I., Kruchten, P., & Gonzalez-Rojas, M. (2012). In search of a metric for managing architectural technical debt. pp. 91-100.
  20. Codabux, Z., & Williams, B. (2013). Managing technical debt: An industrial case study. Managing Technical Debt (MTD), 2013 4th International Workshop on, pp. 8-15.
  21. Cavaleri, S., Firestone, J., & Reed, F. (2012). Managing project problem-solving patterns. International Journal of Managing Projects in Business, 5(1), 125-145.
  22. Brown, N., Cai, Y., Guo, Y., Kazman, R., Kim, M., Kruchten, P., et al. (2010). Managing technical debt in software-reliant systems. Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, pp. 47-52.
  23. Schwanke, R., Lu Xiao, & Yuanfang Cai. (2013). Measuring architecture quality by structure plus history analysis. Software Engineering (ICSE), 2013 35th International Conference on, 891-900.
  24. Sneed, H. M., & Verhoef, C. (2013). Migrating to service-oriented systems (why and how to avoid developing customized software applications from scratch). pp. 91-96.
  25. Krishna, V., & Basu, A. (2012). Minimizing technical debt: Developer's viewpoint. Software Engineering and Mobile Application Modelling and Development (ICSEMA 2012), International Conference on, pp. 1-5.
  26. Elssamadisy, A., & Schalliol, G. (2002). Recognizing and responding to "bad smells" in extreme programming. Software Engineering, 2002. ICSE 2002. Proceedings of the 24rd International Conference on, 617-622.
  27. Greening, D. R. (2013). Release duration and enterprise agility. System Sciences (HICSS), 2013 46th Hawaii International Conference on, pp. 4835-4841.
  28. Kaiser, M., & Royse, G. (2011). Selling the investment to pay down technical debt: The code christmas tree. Agile Conference (AGILE), 2011, pp. 175-180.
  29. David Garlan. 2014. Software architecture: a travelogue. In Proceedings of the on Future of Software Engineering (FOSE 2014). ACM, New York, NY, USA, 29-39.
  30. Holvitie, J., Leppanen, V., & Hyrynsalmi, S. (2014). Technical debt and the effect of agile software development practices on it -an industry practitioner survey. Managing Technical Debt (MTD), 2014 Sixth International Workshop on, 35-42.
  31. Wiklund, K., Eldh, S., Sundmark, D., & Lundqvist, K. (2012). Technical debt in test automation. pp. 887-892.
  32. Palmer, K. D. (2014). The essential nature of product traceability and its relation to agile approaches., 28. pp. 44-53.
  33. Letouzey, J. -. (2012). The SQALE method for evaluating technical debt. pp. 31-36.
  34. Power, K. (2013). Understanding the impact of technical debt on the capacity and velocity of teams and organizations: Viewing team and organization capacity as a portfolio of real options. pp. 28-31.
  35. Dos Santos, Paulo Sérgio Medeiros, Varella, A., Dantas, C. R., & Borges, D. B. (2013). Visualizing and managing technical debt in agile development: An experience report Springer.
  36. S.V. Shrivastava, others, Distributed agile software development: A review, arXiv Prepr. arXiv1006.1955. (2010).
  37. Abrahamsson, P. (Ed.). (2008). Agile Processes in Software Engineering and Extreme Programming: 9Th International Conference, XP 2008, Limerick, Ireland, June 10- 14, 2008: Proceedings (No. 9). Springer Science & Business Media

Cited by

Sorted by citations

Related

Authors
Behutiye, W. N., Rodríguez, P., Oivo, M., & Tosun, A.
Affiliations
  • University of Oulu (FI)
  • Istanbul Technical University (TR)
Corresponding author
Behutiye, Woubshet Nema
ORCID
Venue
Information and Software Technology
Publisher
Elsevier BV
Published
2017-02
Volume
82
Pages
139-158
ISSN
0950-5849 (ISSN-L)
DOI
10.1016/j.infsof.2016.10.004
OpenAlex
W2541116876
Cited by
116
References
34
Citations by year
2026: 11 · 2025: 10 · 2024: 14 · 2023: 13 · 2022: 21 · 2021: 20 · 2020: 8 · 2019: 9 · 2018: 3 · 2017: 7
FWCI
2.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Information and Software Technology (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
Funders
Tekes
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
Keywords
technical debt, agile software development, technical debt management, refactoring, software architecture, software maintenance costs, system degradation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review