Third International Workshop on Machine Learning Techniques for Software Quality EvaluationTuesday, August 27, 2019, Tallinn, EstoniaOrganisersFrancesca Arcelli Fontana,U. Milano-Bicocca, ItalyBartosz Walter,TU Poznan, PolandApostolos Ampatzoglou,U. Macedonia, GreeceFabio Palomba,U. Zürich, SwitzerlandGilles Perrouin, UNamur, BelgiumMathieu Acher,U. Rennes, Inria, CNRS, IRISA,FranceMaxime Cordy,SnT, U. of Luxembourg, LuxembourgXavier Devroey,TU Delft, NetherlandsPublicity ChairGemma Catolino,U. Salerno, ItalyImportant dates (AoE)
- Abstract: May, 27
- Full paper: June, 3
Submission site:https://easychair.org/conferences/?conf=maltesque2019Call for PapersThe assessment of software qualityis one of the most multifaceted (e.g., structural quality, product quality, process quality, etc.) and subjective aspects of software engineering (since in many cases it is substantially based on expert judgement). Such assessments can be performed at almost all phases of software development (from project inception to maintenance) and at different levels of granularity (from source code to architecture). However, human judgement is: (a) inherently biased by implicit, subjective criteria applied in the evaluation process, and (b) its economical effectiveness is limited compared to automated or semi-automated approaches. To this end, researchers are still looking for new, more effective methods of assessing various qualitative characteristics of software systems and the related processes.In recent years we have been observing a rising interestin adopting various approaches to exploit machine learning (ML) and automated decision-making processes in several areas of software engineering. These models and algorithms help to alleviate human subjectivity in order to make informed decisions based on available data and evaluated with objective criteria. Thus, the adoption of ML techniques is a promising way to improve software quality evaluation. Conversely, learning capabilities are increasingly embedded within software, including in critical domains such as automotive and health. This calls for the application of quality assurance techniques to ensure the reliable engineering of ML-based software systems.The aim of the workshopis to provide a forum for researchers and practitioners to present and discuss new ideas, trends and results concerning the application of ML methods to software quality evaluation and the application of software engineering techniques to self-learning systems. We expect that the workshop will help in (1) the validation of existing ML methods for software quality evaluation as well as their application to novel contexts, (2) the effectiveness evaluation of ML methods, both compared to other automated approaches and the human judgement, (3) the adaptation of ML approaches already used in other areas of science in the context of software quality, (4) the design of new techniques to validate ML-based software, inspired by traditional software engineering techniques.Topics of interestinclude, but are not limited to:
- Application of machine-learning in software quality evaluation,
- Analysis of multi-source data,
- Knowledge acquisition from software repositories,
- Adoption and validation of machine learning models and algorithms in software quality,
- Decision support and analysis in software quality,
- Prediction models to support software quality evaluation,
- Validation and verification of learning systems,
- Automated machine learning,
- Design of safety-critical learning software,
- Integration of learning systems in software ecosystems.
Submissions.We expect papers up to 6 pages in the ESEC/FSE conference format (ACM, double-column). Each paper will be reviewed by three PC members. Accepted papers will be part of ESEC/FSE proceedings.Special Issue.Selected papers will be invited for extension in a special issue (proposal under review) of the Journal of Systems and Software (JSS, Elsevier).Programme committeeElvira-Maria Arvanitou, University of Macedonia, GreeceEarl T. Barr, University College London, United KingdomStamatia Bibi, University of Western Macedonia, GreeceGemma Catolino, University of Salerno, ItalyAlexander Chatzigeorgiou, University of Macedonia, GreeceJürgen Cito, Massachusetts Institute of Technology, United StatesEleni Constantinou, University of Mons, BelgiumSteve Counsell, Brunell University, United KingdomDario Di Nucci, Vrij Universiteit Brussel, BelgiumAndres Diaz Pace, ISISTAN-CONICET/UNICEN University, ArgentinaRémi Emonet, Laboratoire Hubert Curien, FranceDaniel Feitosa, University of Groningen, The NetherlandsBenoit Frenay, University of Namur, BelgiumSuman Jana, Columbia University, United StatesGeorge Kakarontzas, Technological Educational Institute of Thessaly, GreeceYves Le Traon, University of Luxembourg, LuxembourgLech Madeyski, Wroclaw University of Technology, PolandKarl Meinke, KTH Royal Institute of Technology, SwedenTim Menzies , NC State University, United StatesMirosław Ochodek, Poznan University,PolandHaidar Osman, University of Bern, SwitzerlandAnnibale Panichella, Delft University of Technology, The NetherlandsJean-François Raskin, Université Libre de Bruxelles, BelgiumKoushik Sen, University of California – Berkeley, United StatesAlison Smith-Renner, University of Maryland, Decisive Analytics Corporation, United StatesDavide Taibi, Free University of Bozen, ItalyDamian A. Tamburri, Jheronimus Academy of Data Science, The Netherlands
Gilles PERROUIN
FNRS Research Associate
Computer Science Department – PReCISE
Namur Digital Institute – NaDIT.+32 (0)81 724 981
gilles.perrouin@unamur.beUniversité de Namur ASBL
Rue de Bruxelles 61 – 5000 Namur
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