MASES 2018
The 1st International Workshop on Machine Learning and Software Engineering in Symbiosis
Montpellier, France, September, 2018 - Co-located with ASE
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About MASES 2018

Major breakthroughs in Artificial Intelligence (AI) have raised strong interests from research and industry towards Machine Learning (ML), the discipline of AI that aims at building software that automatically learns from data. As a result, ML systems increasingly gain popularity and will soon proliferate in a broad range of domains. However, they also raise many questions, in particular regarding their reliable engineering. Conversely, recent advances in Software Engineering (SE) themselves rely on ML. Several software development activities can thus now benefit from AI-based assistance and we expect many more in the coming years.

This workshop aims at bringing together the SE and ML communities to reflect on the potential symbioses between their respective disciplines. As such, it targets innovative ML applications improve SE practices, as well as new engineering methods for ML-based systems.


Call for Papers

Call for Papers

Major breakthroughs in Artificial Intelligence (AI) have raised strong interests from research and industry towards Machine Learning (ML), the discipline of AI that aims at building software that automatically learns from data. As a result, ML systems increasingly gain popularity and will soon proliferate in a broad range of domains. However, they also raise many questions, in particular regarding their reliable engineering. Conversely, recent advances in Software Engineering (SE) themselves rely on ML. Several software development activities can thus now benefit from AI-based assistance and we expect many more in the coming years.

This workshop aims at bringing together the SE and ML communities to reflect on the potential symbioses between their respective disciplines. As such, it targets innovative ML applications improve SE practices, as well as new engineering methods for ML-based systems.

Topics of Interest

Topics include, but are not limited to:

  • Machine learning for software engineering
    • Applications of machine learning to software analysis, Verification and Validation,
    • Naturalness-based code analysis,
    • Analysis of software repositories,
    • Human-machine collaboration for engineering software systems,
    • Performance prediction of software systems,
    • Natural language processing for requirements extraction.
  • Engineering methods for machine-learning systems
    • Automated machine learning,
    • Scalable infrastructure for machine learning,
    • Validation and verification of learning systems,
    • Requirements engineering for machine-learning systems,
    • Design of safety-critical learning software,
    • Integration of learning systems in software ecosystems.

Types of Submissions

We invite original papers from 2 to 10 pages in the conference format (two columns IEEE conference publication format) describing positions and visions as well as technical contributions and experience reports. Reports on existing research projects (e.g., H2020) and industrial perspectives are also welcome. Each contribution will be reviewed by at least three members of the programme committee.

Important Dates

TDB

Submission Site

TBA
Organisation

Organisation

Program Chairs

Gilles Perrouin
PReCISE, NADI, University of Namur
Mathieu Acher
University of Rennes 1 / Inria Rennes
Maxime Cordy
PReCISE, NADI, University of Namur / University of Luxembourg
Xavier Devroey
SERG, Delft University of Technology

Program Committee

TBA