Call for Papers
The aim of the workshop is to provide a forum for researchers and practitioners to present and discuss new ideas, trends and results concerning the application of machine learning methods for software engineering. The focus areas of the workshop are software quality assessment, practices that contribute towards quality assurance, and the application of software engineering techniques to self-learning systems. We expect that the workshop will help with:
1. The validation of existing machine learning methods for software quality assessment, as well as their application to novel contexts;
2. The evaluation of machine learning methods compared to other automated approaches, as well as to human judgment;
3. The adaptation of machine learning approaches already used in other areas of science in the context of software quality;
4. The design of new techniques to validate software based on machine learning, inspired by traditional software engineering techniques;
5. The introduction of approaches based on machine learning to support software engineering practices that contribute to quality assurance (e.g., patterns, refactoring, etc.).
Topics of interest include, but are not limited to:
- application of machine learning in software quality assessment;
- supporting the application of software engineering practices through machine learning;
- 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 systems, learning; item validation and verification of systems based on machine learning;
- automated machine learning;
- design of safety-critical learning software;
- integration of learning systems in software ecosystems.
Evaluation Criteria and Submission
We are looking for two different types of papers:
1. Technical papers, where we invite to submit original research (even at early stages of evaluation) on how machine learning and software quality assurance can support each other. Papers must not exceed 6 pages for the main text, including figures, tables, appendices, and references. They will be part of the ESEC/FSE proceedings under the copyright of the ACM digital library.
2. Presentation abstracts which will be up to 2 pages long and will report (i) research results that are either already published or ready to be submitted to software engineering conferences/journal and (ii) industrial talks. This new track aims at stimulating the participation of industrial practitioners - who will be able to present the practices used in their contexts - as well as researchers - who may be interested in receiving feedback from the research community on early ideas. They will only be reviewed for relevance, and will not be included in the MaLTeSQuE proceedings, but the abstracts will be made available on the website of the workshop.
All papers should be submitted in the PDF format, conforming to the ACM conferences template, through HotCRP.com.
Authors of selected papers accepted at MaLTeSQuE 2021 will be invited to submit revised, extended versions of their manuscripts for a special issue of the Empirical Software Engineering (EMSE), edited by Springer. We will post as soon as possible further details of the call.
Paper Submission Deadline: June 1st, 2021
Notification: July 1st, 2021
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