Motivation

Technical Debt (TD) has grown to be one of the most important metaphors in software maintenance to express development shortcuts, taken for expediency, but causing the degradation of internal software quality. The metaphor has been proven successful in closing the communication gap between technical and non-technical stakeholders in software development teams. Over the past years, the Software Engineering research community has made great progress in theories, practices and tools to manage Technical Debt. Consequently, researchers have also identified other forms of debt that impact software development practices, such as Process Debt (i.e., down-prioritization of process improvement over other activities) and Social Debt (i.e., sub-optimal ways to structure organizations and systems). 

SEaDeM aims at addressing and discussing experiences and challenges related to the application of Technical, Process and Social Debt in practice, from its identification and quantification to the support of decision making with respect to its prioritization and repayment. Most of the work and associated tools focus on Technical Debt and pertains to the source code level, while evidence has shown that critical issues arise from the presence of Technical Debt at the architecture level. We also lack solid evidence on what granularity of information is needed by the stakeholders on their Technical Debt, what can be provided by automatic tools and what needs to be managed manually. Finally, we need more insight on new forms of debt such as Process and Social Debt to further support the shortcomings of suboptimal software development decisions.

We invite researchers and practitioners to contribute to the Technical Track on the practical and theoretical aspects of managing Technical Debt. We especially welcome empirical studies and industrial experiences.

Topics

The topics of interest include, but are not limited to:

  • Case-studies on (un)successful debt management
  • Case-studies on (un)successful remediation of Technical, Process and Social Debt
  • Estimation of Principal and Interest of specific kinds of debt
  • Frameworks for the estimation of Principal and Interest
  • Stakeholders concerns on Technical, Process and Social Debt
  • Architecture viewpoints on Technical Debt
  • Measurement frameworks to study the components of Technical, Process and Social Debt
  • Empirical evidence on Technical, Process and Social Debt management tools and their accuracy
  • Decision frameworks for prioritizing debt items among themselves and against features
  • Methods and tools for monitoring Technical, Process and Social Debt
  • Approaches for managing Technical, Process and Social Debt in software companies
  • Architectural Technical Debt
  • Less studied kinds of Technical Debt: e.g., Requirement, Test, Documentation, Infrastructure Debt
  • Technical Debt in Model-Driven Engineering
  • Relationships between Technical, Process and Social Debt in software development and other interacting disciplines (e.g. electrical-, mechanical engineering, etc.)
  • Comparison and relationships between Technical, Process and Social Debt and other topics (e.g. DevOps, Machine Learning, etc.)
  • Replication studies on Technical Debt

Track/Session Organizers

Program Committee

  • Apostolos Ampatzoglou, University of Macedonia
  • Juan Garbajosa, Universidad Politécnica De Madrid
  • Carolyn Seaman, UMBC
  • Terese Besker, RISE Research Institutes of Sweden AB, Gothenburg, SWEDEN
  • Rafael Capilla, Universidad Rey Juan Carlos, Madrid
  • Kari Systä, Tampere University of Technology
  • Alexander Chatzigeorgiou, Dept. of Applied Informatics, University of Macedonia
  • Heiko Koziolek, ABB Corporate Research
  • Ville Leppänen, University of Turku, Department of Future Technologies
  • Uwe Zdun, University of Vienna
  • Alfredo Goldman, University of São Paulo
  • Francesca Arcelli Fontana, University of Milano – Bicocca
  • Rami Bahsoon, School of Computer Sc, University of Birmingham
  • Paris Avgeriou, University of Groningen
  • Clemente Izurieta, Montana State University
  • Zadia Codabux, University of Saskatchewan
  • Christoph Treude, The University of Melbourne
  • Rodrigo Rebouças de Almeida, Federal University of Paraíba
  • Daniela S. Cruzes, NTNU
  • Monica Iovan, Visma
  • Jan Bosch, Chalmers University of Technology
  • Hideaki Hata, Shinshu University
  • Dag Sjoberg, Department of Informatics, University of Oslo
  • Gemma Catolino, Tilburg University – Jheronimus Academy of Data Science
  • Marthe Berntzen, University of Oslo
  • Viktoria Stray, University of Oslo
  • Elvira-Maria Arvanitou, University of Macedonia