Following its long tradition, SOFSEM is the annual winter conference for new and original research at the forefront of computer science.
SOFSEM 2018 is devoted to leading research in the foundations of computer science, software engineering, and data and knowledge-based systems. The program will have invited and contributed talks,
PROGRAM COMMITTEE CHAIR
A Min Tjoa (TU Wien, Austria)
SOFSEM 2018 TRACKS
A. FOUNDATIONS OF COMPUTER SCIENCE
Chairs:
Jan van Leeuwen (Utrecht University, The Netherlands)
Jiří Wiedermann (Academy of Sciences, Prague, Czech Republic)
B. SOFTWARE ENGINEERING: Advanced METHODS, APPLICATIONS, TOOLS
Chair:
Stefan Biffl (TU Wien, Austria)
C. DATA, INFORMATION and KNOWLEDGE ENGINEERING
Chair:
Ladjel Bellatreche (Laboratoire du LIAS, France)
For PhD students there is:
D. STUDENT RESEARCH FORUM. The Forum gives PhD students the opportunity
to present their research and receive feedback on their projects.
Forum Chair:
Roman Špánek (Technical University of Liberec, Czech Republic)
PROCEEDINGS
Proceedings will be published in the Series Lecture Notes in Computer Science of Springer Verlag.
SUBMITTING PAPERS
SOFSEM 2018 solicits original research contributions in any one of its tracks
or the interface between them. Paper submission uses EasyChair, see http://sofsem2018.ocg.at for detailed instructions.
IMPORTANT DATES:
Paper Abstracts Submission: June 23, 2017
Full Papers Submission: June 30, 2017
Notification (Acceptance/Rejection): September 21, 2017
Camera-ready Papers: October 4, 2017
Poster Submission: November 20, 2017
Conference: January 29 - February 2, 2018
ORGANIZATION:
AUSTRIAN COMPUTER SOCIETY (OCG)
Organizing Committee Chair
Ronald Bieber (OCG, Austria)
CONTACT
Christine Haas
Austrian Computer Society (OCG)
1010 Vienna | Wollzeile 1
Austria
christine.haas@ocg.at
http://sofsem2018.ocg.at
Software Engineering: advanced Methods, Applications, and Tools (SEMAT)
Software is the source of most of the value added in modern software-intensive systems, such as process-centered information systems, web-based systems, mobile systems, games, or intelligent technical systems.
The size, complexity, and criticality of software-intensive systems require innovative and economic approaches for their development and evolution. In today's competitive world, software quality is a key to the success and stability of organizations.
The SEMAT track presents and discusses the research of novel and innovative methods and technologies to software engineering, including both software product and development process aspects. Methods and tools that support the improvement of software processes and products aim at significantly increasing both the quality of software-intensive systems and the productivity of software development.
The SEMAT track will bring together researchers and practitioners to share SEMAT innovations and experiences.
Topics of interest include, but are not limited to:
Methods and tools for better software processes
Process modeling, composition, and enactment/simulation
Agile/lean development
User-centered development
Method engineering
Quality assurance, inspections, testing
Software architecture of complex software-intensive systems
Architecture, components, services
Software reuse, product lines, and software ecosystems
Model-based software engineering methods and tools
Model-based development
Model transformations
Model versioning
Model and meta-model co-evolution
Model-based testing
Data-driven improvement of methods, models, and tools
Quantitative models for development processes and products
Continuous delivery/integration and DevOps, software process and product evolution with feedback from operation.
Legacy modernization/migration
Model mining techniques
Repository mining
Empirical studies and experimental approaches
Methods and tools for software engineering applications, including the areas
Process-centered information systems
Web-based systems
Mobile systems
Game development
Intelligent technical systems (Internet of Things)
In particular, we encourage submissions demonstrating the benefits or limitations of SEMAT approaches through case studies, experiments, and quantitative data.
