SEAA 2021

Call for Papers

Special Session @ 47th EUROMICRO SEAA Conference in Palermo / Italy

Software Engineering and Technical Debt in (SEaTeD)

Part of SPPI – Software Process and Product Improvement Track

September 1-3, 2021

https://dsd-seaa2021.unipv.it/seaa/index.html

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.

SEaTeD aims at addressing and discussing experiences and challenges related to the application of Technical Debt in practice, from its identification and quantification to the support of decision making with respect to its repayment. Most of the work and associated tools focus on the source code level, while evidence has shown that critical issues arise from the presence of Technical Debt at the architecture level. Finally, we 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.

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

The track is an integral part of the 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2021.

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

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

Special Session Organizers:
Paris Avgeriou, paris@cs.rug.nl, University of Groningen, The Netherlands
Alexander Chatzigeorgiou, achat@uom.gr, University of Macedonia, Greece

Program Committee:
Apostolos Ampatzoglou, University of Macedonia, Greece
Francesca Arcelli Fontana, University of Milano – Bicocca, Italy
Elvira-Maria Arvanitou, University of Macedonia, Macedonia
Rami Bahsoon, University of Birmingham, United Kingdom
Ayse Bener, Ryerson University, Canada
Terese Besker, Chalmers University of Technology, Sweden
Jan Bosch, Chalmers University of Technology, Sweden
Frank Buschmann, Siemens AG, Germany
Gemma Catolino, Jheronimus Academy of Data Science, Netherlands
Zadia Codabux, University of Saskatchewan, Canada
Daniel Feitosa, University of Groningen, The Netherlands
Davide Falessi, University of Rome "Tor Vergata", Italy
Juan Garbajosa, Universidad Politecnica de Madrid, Spain
Alfredo Goldman, University of São Paulo, Brazil
Javier Gonzalez-Huerta, Blekinge Institue of Technology, Sweden
Johannes Holvitie, University of Turku, Finland
Clemente Izurieta, Montana State University, USA
Heiko Koziolek, ABB Corporate Research, Sweden
Philippe Kruchten, The University of British Columbia, Canada
Valentina Lenarduzzi, Tampere University of Technology, Finland
Ville Leppänen, University of Turku, Finland
Jean-Louis Letouzey, Inspearit, The Netherlands
Klaus Schmid, University of Hildesheim, Germany
Carolyn Seaman, University of Maryland, Baltimore County, USA
Andriy Shapochka, SoftServe Inc., United Kingdom
Dag Sjøberg, University of Oslo, Norway
Kari Systä, Tampere University of Technology, Finland
Amjed Tahir, Massey University, New Zealand
Davide Taibi, Tampere University of Technology, Finland
Damian Andrew Tamburri, TU Eindhoven - Jeronimus Academy of Data Science, The Netherlands
Uwe Zdun, University of Vienna, Austria