MASES 2018
The 1st International Workshop on Machine Learning and Software Engineering in Symbiosis
Montpellier, France, September 3, 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, title in 24pt font and full text in 10pt font, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option) 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. The proceedings will be published by IEEE through the IEEE Xplore platform. The review process is single-blind. Each contribution will be reviewed by at least three members of the programme committee.

Important Dates

  • Submission of full papers: June 15, 2018 June 25, 2018
  • Notification of acceptance: July 20, 2018
  • Camera Ready: July 30, 2018
  • Workshop date: September 3, 2018

Submission Site

Submissions will be handled via EasyChair: https://easychair.org/conferences/?conf=mases18

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

Mathieu Acher
INRIA (France)
Earl Barr
University College London (United Kingdom)
Jordi Cabot
Open University of Catalonia (Spain)
Jürgen Cito
Massachusetts Institute of Technology (USA)
Maxime Cordy
University of Namur (Belgium)
Jesse Davis
Katholieke Universiteit Leuven (Belgium)
Xavier Devroey
Delft University of Technology (The Netherlands)
Rémi Emonet
Laboratoire Hubert Curien (France)
Robert Feldt
Blekinge Institute of Technology (Sweden)
Benoît Frénay
Université de Namur (Belgium)
Elisa Fromont
Université de Rennes 1 (France)
Patrick Heymans
University of Namur (Belgium)
Suman Jana
Columbia University (USA)
Marta Kwiatkowska
University of Oxford (United Kingdom)
Yves Le Traon
University of Luxembourg (Luxembourg)
Karl Meinke
KTH Royal Institute of Technology (Sweden)
Tim Menzies
NC State University (USA)
Tien Nguyen
The University of Texas at Dallas (USA)
Fabio Palomba
University of Zurich (Switzerland)
Annibale Panichella
Delft University of Technology (The Netherlands)
Sebastiano Panichella
University of Zurich (Switzerland)
Gilles Perrouin
University of Namur (Belgium)
Jean-Francois Raskin
Université Libre de Bruxelles (Belgium)
Pierre-Yves Schobbens
University of Namur (Belgium)
Koushik Sen
University of California - Berkley (USA)
Alyson Smith
Decisive Analytics Corporation (USA)
Paolo Tonella
Fondazione Bruno Kessler (Italy)
Zhenchang Xing
Australian National University (Australia)

Keynotes

Gaël Varoquaux

Machine learning and brain imaging researcher
Research faculty (CR1), Parietal team, INRIA (FR)