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MaLTeSQuE 2017 : Machine Learning Techniques for Software Quality Evaluation

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Link: http://www.cs.put.poznan.pl/maltesque/
 
When Feb 21, 2017 - Feb 21, 2017
Where Klagenfurt
Submission Deadline Dec 12, 2016
Notification Due Dec 21, 2016
Final Version Due Jan 9, 2017
Categories    software quality   machine learning
 

Call For Papers

MaLTeSQuE-2017
Workshop on Machine Learning Techniques for Software Quality Evaluation

collocated with SANER 2017

Klagenfurt, February 21st, 2017

I. MOTIVATION
In recent years we have been observing a rising interest in adopting various approaches to exploiting machine learning (ML) and automated decision-making processes in several areas of software engineering. These models and algorithms help to reduce effort and risk related to human judgment in favor of automated systems, which are able to make informed decisions based on available data and evaluated with objective criteria.
Software quality is the area that deserves particular attention. At all levels, source code quality, process quality and the quality of entire systems, researchers are still looking for new, more effective methods of evaluating various qualitative characteristics of software systems and the related processes.
Human judgement is inevitable in certain areas, but is also inherently biased by implicit, subjective criteria applied in the evaluation process. Additionally, its economical effectiveness is limited, compared to automated or semi-automated approaches. Therefore, we observe a space for applying ML even more extensively than it is done currently.
We also believe that applying ML can address uncertainty, in an effort to handle the size of complex systems, by supporting better code and design review, and
enable automation of analyses that handle fuzzy concepts (e.g., code smells).

II. OBJECTIVE
The aim of the workshop is to provide a forum for researchers and practitioners to present and discuss new ideas, trends and results concerning applying ML to software quality evaluation. We expect that the workshop will help in (1) validation of existing and exploring new applications of ML, (2) comparing their efficiency and effectiveness, both among other automated approaches and the human judgement, and (3) adapting ML approaches already used in other areas of science.

Topics of interest include, but are not limited to:
• Application of machine-learning in software evaluation,
• Multi-criteria analysis of software-related data,
• Adoption of fuzzy concept in analyzing software artifacts and processes,
• Knowledge acquisition from software repositories,
• Adoption and validation of machine learning models and algorithms in software engineering,
• Decision support and analysis in software engineering,
• Predicting models in software engineering

IV. SUBMISSIONS
We solicit research papers and prototype demonstrations. The accepted papers would be included into the SANER 2017 proceedings and will be available all participants in advance through a workshop webpage.

Each paper will be reviewed by three PC members of the workshop. Program Committee will jointly make the final decision concerning acceptance of individual papers, based on the reviews.

The paper cannot exceed 6 pages, using the IEEE Proceedings style. Papers will appear in the IEEE Digital Library. Submissions can be made by the EasyChair link.

Selected papers will be considered for publication in a special section of e-Informatica Software Engineering Journal.

V. PROGRAM COMMITTEE
• Francesca Arcelli Fontana, University of Milano-Bicocca
• Alexander Chatzigeorgiou, University of Macedonia
• Steve Counsell, Brunell University
• Jens Dietrich, Massey University
• Foutse Khomh, Polytechnique Montreal
• Lech Madeyski, Wrocław University of Technology
• Mirosław Ochodek, Poznań University of Technology
• Haidar Osman, University of Bern
• Andres Diaz Pace, UNICEN University
• Fabio Palomba, Università di Salerno
• Bartosz Walter, Poznań University of Technology
• Lu Xiao, Drexel University
• Aiko Yamashita, Oslo and Akershus University
• Marco Zanoni, University of Milano-Bicocca

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