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Lessons learned from the ProDebt research project on planning technical debt strategically

: Ciolkowski, Marcus; Guzman, Liliana; Trendowicz, Adam; Salfner, Felix


Felderer, Michael (Ed.):
Product-focused software process improvement. 18th International Conference, PROFES 2017. Proceedings : Innsbruck, Austria, November 29 - December 1, 2017
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10611)
ISBN: 978-3-319-69925-7 (Print)
ISBN: 978-3-319-69926-4 (Online)
ISBN: 3-319-69925-3
International Conference on Product-Focused Software Process Improvement (PROFES) <18, 2017, Innsbruck>
International Workshop on Managing Quality in Agile and Rapid Software Development Processes (QuASD) <1, 2017, Innsbruck>
Conference Paper
Fraunhofer IESE ()
agile software development; quality management; ProDebt

Due to cost and time constraints, software quality is often neglected in the evolution and adaptation of software. Thus, maintainability suffers, maintenance costs rise, and the development takes longer. These effects are referred to as "technical debt". The challenge for project managers is to find a balance when using the given budget and schedule, either by reducing technical debt or by adding technical features. This balance is needed to keep time to market for current product releases short and future maintenance costs at an acceptable level.
Method: The project ProDebt aimed at developing an innovative methodology and a software tool to support the strategic planning of technical debt in the context of agile software development. In this project, we created quality models and collected corresponding measurement data for two case studies in two different companies. Altogether, the two case studies contributed 5-6 years of data, from the end of 2011, resp. mid-2012, until today. Using measurement and effort data, we trained a machine-learning model to predict productivity based on measurement data-representing the technical debt of a file at a given point in time.
Result: We developed a prototype and a prediction model for forecasting potential savings based on proposed refactorings of key drivers of technical debt identified by the model. In this paper, we present the approach and the experiences made during model development.