Antonio Martini
ABSTRACT
The interest of Technical Debt is difficult to assess. The negative effects (severity) of Technical Debt might depend on the context of the organization and the estimations might be subjective. There is a need for assessing Technical Debt interest in a more systematic way.
Based on the results of previous research, we have developed and used a lightweight tool, AnaConDebt, to assess the severity of the interest of 9 Technical Debt items with the stakeholders in 3 Agile teams. The systematic and semi-automatic assessment of seven factors and their growth has been compared to the stakeholders' intuitive estimations.
The results show that the outcome of the tool is very close to the estimation given by the stakeholders. The implications are that, if further data support the hypothesis, the severity of the interest can be systematically assessed by the stakeholders by estimating only seven factors in a cost-effective manner with acceptable results.
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Digital Library
- P. Avgeriou, P Kruchten, I. Ozkaya, and C. Seaman. Managing Technical Debt in Software Engineering (Dagstuhl Seminar 16162).Google Scholar
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- Yuepu Guo and Carolyn Seaman. 2011. A Portfolio Approach to Technical Debt Management. In Proceedings of the 2Nd Workshop on Managing Technical Debt. ACM, New York, NY, USA, 31--34. Google ScholarDigital Library
- Yuepu Guo, Carolyn Seaman, Rebeka Gomes, Antonio Cavalcanti, Graziela Tonin, Fabio QB Da Silva, AndrÃl' Luis M. Santos, and Clauirton Siebra. 2011. Tracking technical debt: An exploratory case study. In Software Maintenance (ICSM), 2011 27th IEEE International Conference on. 528--531.Google ScholarDigital Library
- Philippe Kruchten, R.L. Nord, and I. Ozkaya. 2012. Technical Debt: From Metaphor to Theory and Practice. IEEE Software 29, 6 (2012), 18--21.Google ScholarDigital Library
- A. Martini, T. Besker, and J. Bosch. 2016. The Introduction of Technical Debt Tracking in Large Companies. In accepted at APSEC 2016. Hamilton, New Zealand. Google ScholarCross Ref
- Antonio Martini and Jan Bosch. 2016. An Empirically Developed Method to Aid Decisions on Architectural Technical Debt Refactoring: AnaConDebt. In Proceedings of the 38th International Conference on Software Engineering Companion (ICSE '16). ACM, New York, NY, USA, 31--40. Google ScholarDigital Library
- Antonio Martini and Jan Bosch. 2016. A Multiple Case Study of Continuous Architecting in Large Agile Companies: Current Gaps and the CAFFEA Framework. IEEE, 1--10.Google Scholar
- Klaus Schmid. 2013. A formal approach to technical debt decision making. In Proceedings of the 9th international ACM Sigsoft conference on Quality of software architectures. ACM, 153--162. http://dl.acm.org/citation.cfm?id=2465492 Google ScholarDigital Library
- C. Seaman, Yuepu Guo, Nico Zazworka, F. Shull, C. Izurieta, Yuanfang Cai, and A. Vetro. 2012. Using technical debt data in decision making: Potential decision approaches. In 2012 Third International Workshop on Managing Technical Debt (MTD). 45--48. Google ScholarCross Ref
- 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 (Oct. 2016), 195--218. 05.018 Google ScholarCross Ref
Index Terms
- The magnificent seven: towards a systematic estimation of technical debt interest
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