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Software maintenance and comprehension are important areas of software engineering which involve different activities and techniques that can be applied to improve software comprehension, including reverse engineering and re-engineering. During all these activities several quality aspects can be taken into account, from metrics evaluation to different code or architectural issues detection that can have an impact on software quality. Moreover, quality aspects need to be continuously evaluated and monitored during software development in order to improve software maintenance and comprehension.
We seek novel contributions on how quality aspects in software maintenance and comprehension can be evaluated and managed, through different analysis, techniques, and tools, as well as empirical studies and experience reports.
Topics of interest include, but are not limited to:
- Software maintenance methods, techniques and tools
- Change and defect management
- Code smells detection and visualization
- Architectural smells detection
- Software refactoring and restructuring
- Reverse engineering and re-engineering
- Empirical studies on software maintenance and comprehension
- Human aspects of software maintenance and comprehension
- Software quality assessment
- Technical debt in software maintenance
- Tool support for program comprehension
- Empirical evaluations of program comprehension techniques
- Empirical studies of software maintenance
Track Chairs:
- Francesca Arcelli Fontana, University of Milano Bicocca, Italy
- Valentina Lenarduzzi , Tampere University, Finland
Program Committee Members:
- Jesus Molina, Universidad de Murcia, Spain
- Rafael Capilla, Rey Juan Carlos University, Spain
- Jennifer Perez, Universidad Politécnica de Madrid, Spain
- Elena Navarro, University of Castilla-La Mancha, Spain
- Rolf-Helge Pfeiffer, IT University of Copenhagen
- Fabio Palomba, ETH, Zurich
- Claudia Raibulet, University of Milano Bicocca
- Luigi Lavazza, Università degli Studi dell'Insubria
- Bartosz Walter, Poznań University of Technology
- Sandro Morasca, Università degli Studi dell'Insubria
- Andrea Janes, Free University of Bolzano
- Steve Counsell, Brunel University
- Aiko Yamashita, CoE Advanced Analytics - DNB, Oslo
- Apostolos Ampatzoglou, University of Macedonia
- Alexander Chatzigeorgiou, University of Macedonia
- Antonio Martini, University of Oslo
- Mel Cinnéide, School of Computer Science, University College Dublinç
- Ilaria Pigazzini, Università degli Studi di Milano-Bicocca - Unimib, Italy
- Antonela Tommasel, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina
- Damian Andrew Tamburri, Eindhoven University of Technology & JADS
- J. Andres Diaz-Pace, ISISTAN Research Institute, UNICEN University
- Yania Crespo, University of Valladolid, Spain
- António Rito Silva, Univ. Lisboa, Portugal
- Miguel Goulão, FCT/UNL, Portugal
Francesca Arcelli Fontana has her degree and Ph.D. in Computer Science taken at the University of Milano (Italy). She is currently in the position of Associate Professor at University of Milano Bicocca. The actual research activity principally concerns the software engineering field, in particular software evolution and reverse engineering, code smell and design pattern detection through machine learning techniques, architectural smell detection and managing technical debt. She is the head of the Software Evolution and Reverse Engineering Lab at University of Milano Bicocca and member of IEEE Computer Society.
Valentina Lenarduzzi, she is a researcher at Tampere University (Finland). Her research activities are related to data analysis in software engineering, software quality, software maintenance and evolution, focusing on Technical Debt, Code and Architectural smells. She was a research assistant at the Free University of Bozen-Bolzano, (Italy) and visiting researcher at the Technical University of Kaiserslautern and Fraunhofer Institute for Experimental Software Engineering IESE (Germany). She obtained my Ph.D. in Computer Science in 2015 working on effort estimation and data analysis in software engineering.
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