MALIR-SE 2013 - 1st International Workshop on MAchine Learning and Information Retrieval for Software Evolution MALIR-SE 2013

Machine Learning and Information Retrieval (MALIR) techniques and tools play a fundamental role in collecting and elaborating information essential to take decisions and to guide and to improve the maintenance of software systems. These techniques are used for several engineering activities such as: requirements engineering, specification mining and reconstruction, software repositories analysis and reuse, system testing, as well as prediction of cost, effort, and defectiveness. For example, machine learning techniques could help in building software systems having high performance and the capability to be adaptive and reactive to environmental and conditions changes. Indeed, by learning from experiences and observations such systems can work having a limited knowledge of the application domain and of the environment. Information Retrieval, instead, could help in collecting, retrieval and maintaining information useful to take decisions even in case of limited, partial, and unstructured source of information.
Recently, there has been an increasing interest of the community toward the adoption and customization of Machine Learning and Information Retrieval in software system development and evolution. The workshop aims at providing a forum for discussions about the adoption of Machine Learning and Information Retrieval in software maintenance and evolution, that is a forum to establish a roadmap about the state-of-the-art as well as about future directions of the research in these topics.
The workshop topics include all those concerning Machine Learning and Information Retrieval in software evolution; in particular but not limiting to:
Using and defining Machine Learning and Information Retrieval techniques in all the phases of the software maintenance and evolution
Evaluating and comparing Machine Learning and Information Retrieval techniques and tools
New trends in adopting Machine Learning and Information Retrieval
Combining Machine Learning and Information Retrieval techniques for software maintenance and evolution
Exploring new needs in software evolution for applying Machine Learning and Information Retrieval
Reporting lessons learned in applying Machine Learning and Information Retrieval
Identifying strengths and weaknesses of Machine Learning and Information Retrieval techniques
Systematic reviews and surveys about the state-of-the-art practices for the application of Machine Learning and Information Retrieval techniques