We, the technical debt research community, agree that a common conceptual model of technical debt that we collectively improve and validate would increase the pace of technical debt research. Therefore, as organizers we felt it is important to tease this apart together during the workshop. Early conceptual models offered by Martin Fowler (the debt quadrants) and Steve McConnell (intentional versus unintentional debt) provided useful starting points, but do not suffice to guide answering the hard questions for eliciting, quantifying, and reducing debt and transitioning to developers validated, easy to adopt practices.
Different technical debt enthusiasts refer to this semantic model in different ways: “technical debt framework”, “technical debt landscape”, “conceptual model”, “empirical model”, “financial model” ,“quality model”, “measurement model”. The concepts discussed in these models are not consistent either. Is design debt the same as architectural debt? If defects are not technical debt, what are postposed defects? Does principal of debt map to all code quality violations? Does principal change? What are the attributes of interest?
The underlying goal of all these models are common, to guide defining technical debt concepts and creating methods to control the inputs and outputs for managing it. Several blog posts here already refer to the conceptual model. In addition, there are several papers already published that can help shape a strawman conceptual model of technical debt. We compiled a reading list to help us all prepare for our sessions during the workshop when we discuss the conceptual model.
We believe that a baseline model will help the technical debt community make collective progress rather than coming up with yet another model variation. The reading list is meant to be representative rather than all-inclusive. If we have skipped a fundamental work that should be included comment and we will add it.
All the papers referred to are here: Ipek TD papers (in a zip file).
Systematic literature reviews and technical debt landscape
Chen Yang, Peng Liang, Paris Avgeriou: A systematic mapping study on the combination of software architecture and agile development. Journal of Systems and Software 111: 157-184 (2016)
Areti Ampatzoglou, Apostolos Ampatzoglou, Alexander Chatzigeorgiou, Paris Avgeriou: The financial aspect of managing technical debt: A systematic literature review. Information & Software Technology 64: 52-73 (2015)
Zengyang Li, Paris Avgeriou, Peng Liang: A systematic mapping study on technical debt and its management. Journal of Systems and Software 101: 193-220 (2015)
Edith Tom, AybüKe Aurum, and Richard Vidgen. 2013. An exploration of technical debt. J. Syst. Softw. 86, 6 (June 2013), 1498-1516.
Nicolli S. R. Alves, Thiago Souto Mendes, Manoel Gomes de Mendonça Neto, Rodrigo O. Spínola, Forrest Shull, Carolyn B. Seaman: Identification and management of technical debt: A systematic mapping study. Information & Software Technology 70: 100-121 (2016)
Clemente Izurieta, Antonio Vetro, Nico Zazworka, Yuanfang Cai, Carolyn B. Seaman, Forrest Shull: Organizing the technical debt landscape. MTD@ICSE 2012: 23-26
Philippe Kruchten, Robert L. Nord, Ipek Ozkaya: Technical Debt: From Metaphor to Theory and Practice. IEEE Software 29(6): 18-21 (2012)
Comparative studies on debt identification:
Nico Zazworka, Antonio Vetro, Clemente Izurieta, Sunny Wong, Yuanfang Cai, Carolyn B. Seaman, Forrest Shull: Comparing four approaches for technical debt identification. Software Quality Journal 22(3): 403-426 (2014)
Griffith I., Reimanis D., Izurieta C., Codabux Z., Deo A., Williams B., “The Correspondence between Software Quality Models and Technical Debt Estimation Approaches,” IEEE ACM MTD 2014 6th International Workshop on Managing Technical Debt. In association with the 30th International Conference on Software Maintenance and Evolution, ICSME, Victoria, British Columbia, Canada, September 30, 2014.
Griffith I., Izurieta C., Taffahi H., Claudio D., “A Simulation Study of Practical Methods for Technical Debt Management in Agile Software Development,” Winter Simulation Conference WSC 2014, Savannah, GA, December 7-10, 2014.
Antonio Martini, Lars Pareto, Jan Bosch:
A multiple case study on the inter-group interaction speed in large, embedded software companies employing agile. Journal of Software: Evolution and Process 28(1): 4-26 (2016)
Ariadi Nugroho, Joost Visser, and Tobias Kuipers. 2011. An empirical model of technical debt and interest. In Proceedings of the 2nd Workshop on Managing Technical Debt (MTD ’11). ACM, New York, NY, USA, 1-8.