Software Analytics: Mining Software Open Datasets and Repositories (STREAM)

Special Session Scope

Motivation: In recent years, software engineering has benefited from collecting, processing, and visualizing large volumes of data that are produced along the software development process, the use of software applications per se, and from the people that are involved in the development and operation of software. By using data stemming from systems that support software development and maintenance (such as JIRA, GitHub, the Maven Repository, etc.), one can track the progress of software development, as well as the quality of software processes and products. Lessons learnt from exploiting the aforementioned data sources through qualitative or quantitative studies are considered extremely useful for software processes’ and products’ improvement. So far, advances in software engineering provide us with progressively improved software development and the ability to continuously deploy software products. However, these new software products, systems, or services create new challenges to their specification, development, quality assurance, and security, where innovative solutions are required. To be able to take the next step in further advancing modern software development, we need to apply research methods and tools for mining, analyzing, representing, and properly using large quantities of software data. The mission of this session is to provide a forum, in which practitioners and researchers can exchange experiences and ideas around software analytics, mining software repositories, software data visualization, and big data systems in software engineering.

Topics

Topics of interest include, but are not restricted to:

Methods, tools, and applications of software analytics or mining software repositories for software process and product improvement
Machine learning and artificial intelligence for software engineering
Software engineering for machine learning and artificial intelligence
Visualization methods for representing software data that can support software engineering processes including program comprehension, software testing, refactoring, performance analysis, etc.
Methods and tools that use software data for the specification, design, development, quality assurance, deployment, and operation of software systems and products
Human and social aspects of developing modern software development approaches, software systems, and products that use developers’ or users’ feedback
Security and privacy regarding techniques that use software data and developers’ and users’ feedback in software engineering
Empirical studies that rely on software analytics, data science, software data visualization, and mining software repositories
Industrial experience with software analytics and data science in software engineering
Repository mining and management for modelling artefacts
Model searching, indexing, retrieval, storage, and automated program repair
Software evolution analysis as mined from software repositories
Dependency Management: Build tools, continuous integration, external dependencies, 3rd party libraries, and system configuration
Experiences with Collaborative Software Development Tools (e.g., GitHub, Bitbucket), Issue Trackers, Bug Trackers of Industrial and Open Source Software Development
Software engineering for emerging technologies such as smart contracts and blockchain

Track Organizers

Maria Kechagia, m.kechagia@ucl.ac.uk, University College London, United Kingdom
Elvira-Maria Arvanitou, e.arvanitou@uom.edu.gr, University of Macedonia, Greece

Program Committee

Francesca Arcelli-Fontana, University of Milano-Biccoca, IT
Areti Ampatzoglou, Aristotle University of Thessaloniki, GR
Rami Bahsoon, University of Birmingham, UK
Stamatia Bibi, University of Western Macedonia, GR
Panagiota Chatzipetrou, Orebro University School of Business, SE
Theodore Chaikalis, Accenture, GR
Sridhar Chimalakonda, IIT Tirupati, IN
Xavier Devroey, University of Namur, BL
Daniel Feitosa, University of Groningen, NL
Marios Fragkoulis, Delft University of Technology, NL
Matthias Galster, University of Canterbury, NZ
Simos Gerasimou, University of York, UK
Stefanos Georgiou, Queen’s University , CA
Valentina Lenarduzzi, Lappeenranta University of Technology, FI
Zengyang Li, Central China Normal University, CN
Peng Liang, Wuhan University, CN
Lech Madeyski, Wroclaw University of Science and Technology, PL
Matias Martinez, Université Polytechnique Hauts-de-France, FR
Nikolaos Mittas, International Hellenic University, GR
Elisa Yumi Nakagawa, University of Sao Paulo, BR
Davide Taibi, Tampere University of Technology, FI
Tushar Sharma, Dalhousie University, CA
Antonio Martini, University of Oslo, NO