STREAM: Software Analytics: Mining Software Open Datasets and Repositories

Motivation

In recent years, software engineering has benefitted 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. In addition with the rise of data-driven development, AI-tools and DevOps, new possibilities have emerged for understanding and improving software processes.

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. The integration of Machine Learning, Natural Language Processing, Large Language Models and other types of automation in analyzing these data sources enables the extraction of knowledge and recommendations assisting software developers. 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 requir

Topics

This track will consider papers related (but not limited) to the following topics:

  • 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

  • Elvira-Maria Arvanitou, earvanitou@ihu.gr, University of Macedonia, Greece
  • Stamatia Bibi, sbibi@uowm.gr, University of Western Macedonia, Greece

Program Committee

  • Areti Ampatzoglou, University of Macedonia
  • Theodore Chaikalis, University of Macedonia
  • Panagiota Chatzipetrou, Örebro University
  • Daniel Feitosa, University of Groningen
  • Simos Gerasimou, University of York
  • Fabian Gilson, University of Canterbury
  • Maria Kechagia, National and Kapodistrian University of Athens
  • Maurizio Leotta, University of Genova
  • Zengyang Li, Central China Normal University
  • Peng Liang, Wuhan University
  • Lech Madeyski, Wroclaw University of Science and Technology
  • Antonio Martini, University of Oslo
  • Elisa Yumi, University of São Paulo
  • Sulayman Sowe, RWTH University
  • Michele Tucci, University of L’Aquila