Part of SPPI – Software Process and Product Improvement Track
August 26-28, 2020
In software engineering, the Technical Debt metaphor relates sub-optimal technical solutions to financial debts. Such solutions can have benefits in the short term, but they might create extra costs (interest) in the future, especially in terms of software maintainability and evolvability.
The Technical Debt metaphor is useful to translate technical issues into items that can be understood by non-technical stakeholders (esp. management), who need to evaluate and prioritize the business value of improving internal qualities.
Over the past years, the Software Engineering research community has made great progress in theories and tools to manage Technical Debt. However, there are several open issues with respect to the application of Technical Debt in practice, from its identification and the calculation of the principal (cost of refactoring) and interest to the support of decision making with respect to the repayment of Technical Debt. Moreover, most of the work has focused on the source code level, while evidence has shown that the really critical issues are caused by 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 special session on the practical and theoretical aspects of the Technical Debt. We especially welcome empirical studies and industrial experiences.
The topics of interest include, but are not limited to:
The conference proceedings in the last years have been published by the IEEE Computer Society. The format is the IEEE two-column proceedings format (8 pages for full papers and 4 pages for short papers).
Submission Guidelines: SEAA 2020 encourages the submission of full research papers (maximum 8 pages), short papers and tool demo papers (maximum 4 pages), and posters. Papers follow a single-blinded reviewing process and must contain original unpublished work, describe significant novel contributions, and provide evidence on the validation of results. In particular, reports on industrial applications are welcome.
Submissions URL: https://easychair.org/conferences/?conf=seaa2020 (choose Software Engineering and Technical Debt track when submitting)
Conference Publishing Services (CPS) will publish accepted papers in the conference proceedings and the proceedings will be submitted to the IEEE Xplore Digital library and indexing services.
Please note that it is planned to select best papers among all tracks of SEAA and present them with an award. A selection of best papers will be invited to submit extended versions for tentative publication in a requested Special Issue of a Journal (under negotiation).
Special Session Organizers:
Paris Avgeriou, firstname.lastname@example.org, University of Groningen, The Netherlands
Alexander Chatzigeorgiou, email@example.com, University of Macedonia, Greece
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
Zadia Codabux, University of Saskatchewan, Canada
Daniel Feitosa, University of Groningen, The Netherlands
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
Patroklos Papapetrou, Elastic
Klaus Schmid, University of Hildesheim, Germany
Andriy Shapochka, SoftServe Inc.
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