International Workshop on Machine Learning Technologies in Software Engineering
Lawrence, Kansas, U.S.A.
November 12, 2011.
http://malets11.doc.ic.ac.uk/

In association with the 26th International Conference on Automated Software Engineering.


*** ABOUT THE WORKSHOP ***

Software engineering practitioners and researchers continue to face huge demands in the development and maintenance of software specifications, analysis and design. This is mainly driven by the increased complexity, dynamicity and volatility of software systems as they pose new challenges at each stage of the software engineering process. Adaptability, reliability and resilience are characteristics that software systems (from the systems-in-the-small to the system-in-the-large) must guarantee both at static and run-time level. Machine Learning (ML) techniques have, on the other hand, witnessed advances in methodologies, algorithms and tools, with emphasis on the development of efficient, more expressive and scalable systems. They have played a key role in performing complex tasks in various application domains, but their potential applications in the many fields of computer science including software engineering are still being unfold.

The MALETS workshop aims to provide an interdisciplinary platform where new technologies in ML and their integration in the software engineering process are presented and discussed. The focus of this workshop is to bring together researchers and practitioners working in the areas of Software Engineering and Machine Learning to discuss existing issues, recent developments, applications, experience reports and novel ideas of application of ML in all aspects of Software Engineering.

Topics of interest are theoretical foundations and practical approaches related, but not limited, to the integration of the following two areas of research:

SOFTWARE ENGINEERING
-Software requirements elaboration
-Security and fault tolerance
-Software reusability
-Requirements prioritization
-Software quality/reliability estimation
-Self-adaptive systems
-Risk modeling and analysis
-Automated software specification
-Testing, verification and validation
-Autonomic systems
-Software design
-Process & workflow management
-Pervasive systems
-Software effort prediction and cost estimation
-Pervasive systems
-Model-driven and component-based software development

MACHINE LEARNING
-Inductive learning
-Abductive reasoning
-Theory revision
-Case-based reasoning
-Statistical learning
-Online learning
-Bayesian inference
-Text Mining
-Explanation-based learning
-Similarity-based learning
-Genetic algorithms
-Artificial Neural Networks
-Data mining


*** PAPER SUBMISSION ***

We invite submissions in either of the following forms:
- Research papers presenting novel approaches and new research results (8 pages).
- Position papers describing new ideas not yet fully developed or preliminary tool support (4 pages).

Submitted papers must be written in English, should not have been submitted for review or published elsewhere and must follow ASE 2011 submission guidelines. Submissions are accepted via easy chair from the workshop homepage.

Authors of best papers will be invited to submit a revised and extended version of their papers to the IET Software journal.


*** IMPORTANT DATES ***

Submission deadline: August 26, 2011.
Author notification: September 30, 2011.
Camera-ready copy: October 21, 2011.


*** ORGANIZING COMMITTEE ***

Dalal Alrajeh,
Imperial College London
Tim Menzies,
West Virginia University
Alessandra Russo,
Imperial College London

*** PROGRAM COMMITTEE ***

Bram Adams, Queen's University, Canada
Marta Arias, Universitat Politcnica de Catalunya, Spain
Christian Bird, Microsoft Research, USA
Luke Dickens, Imperial College London, UK
Tapio Elomaa, Tampere University of Technology, Finland
Artur Garcez, City University, UK
Dimitra Giannakopoulou, NASA Ames Research, USA
Jane Cleland-Huang, DePaul University, USA
Yasutaka Kamei, Kyushu University, Japan
Axel van Lamsweerde, Universite Catholique de Louvain, Belgium
George D. Magoulas, Birkbeck University, UK
Bashar Nuseibeh, Open University, UK
Vasile Palade, Oxford University, UK
Sebastian Uchitel, UBA, Argentina / Imperial College London, UK
Du Zhang, California State University, USA
Thomas Zimmermann, Microsoft Research, USA
Andrea Zisman, City University, UK