Track: Quality Aspects in Software Evolution
Change is an essential characteristic of software development, as software systems must respond to evolving requirements, platforms, and other environmental pressures. This constant change constitutes software evolution, a phase of the software life-cycle where the bulk of software development happens. Nevertheless, software quality generally falls short of expectations, and software systems continue to suffer from symptoms of aging as they are adapted to changing requirements and environments. The only way to overcome or avoid the negative effects of software aging is by placing change and evolution in the center of the software development process.
We seek novel contributions on how to help developers evolve software systems and to cope with aging and deterioration of quality. We invite high-quality submissions describing significant, original, and unpublished results related to but not limited to any of the listed topics.
Topics of interest include, but are not limited to:
Artificial intelligence applied to software evolution
Change and defect management
Code smells detection and visualization
Empirical studies on software maintenance and evolution
Evolution of Artificial Intelligence systems
Human aspects of software maintenance and evolution
Maintenance and evolution processes, methods, techniques and tools
Management of code clones
Mining software repositories
Reverse engineering and re-engineering
Software quality assessment
Software refactoring and restructuring
Software testing theory and practice
Technical debt in software maintenance
Chair: Péter Hegedűs, University of Szeged, Hungary
Fabio Palomba, University of Salerno, Italy
Steve Counsell, Brunel University, UK
Alexander Chatzigeorgiou, University of Macedonia, Greece
Gopi Krishnan Rajbahadur, Queen's University, Canada
Bin Lin, Università della Svizzera italiana, Switzerland
Steffen Herbold, Universität Göttingen, Germany
Felipe Ebert, Eindhoven University of Technology, Netherlands
Rolf-Helge Pfeiffer, IT University of Copenhagen, Denmark
Gemma Catolino, Tilburg University, Netherlands
Andrew Meneely, Rochester Institute of Technology, USA
Péter Hegedűs received his PhD degree in computer science from the University of Szeged in 2015. He currently works as a researcher both at the Software Engineering Department of University of Szeged and the MTA-SZTE Research Group of Artificial Intelligence.
His research interests include software maintainability models, deep learning applications, source code analysis, bug and vulnerability detection and prediction. He was a PC member in the CSMR, MSR, ICCSA and SQM conferences and currently holds a Bolyai János research scholarship. Besides teaching and research involvement, he also takes part in various software development projects as a project manager and lead developer.