MaLTeSQuE2022: The 6th edition of the International Workshop on Machine Learning Techniques for Software Quality Evolution Singapore, Singapore, November 18, 2022 |
Conference website | https://maltesque2022.github.io |
Submission link | https://easychair.org/conferences/?conf=maltesque2022 |
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:
- The validation of existing machine learning methods for software quality assessment, as well as their application to novel contexts;
- The evaluation of machine learning methods compared to other automated approaches, as well as to human judgment;
- The adaptation of machine learning approaches already used in other areas of science in the context of software quality;
- The design of new techniques to validate software based on machine learning, inspired by traditional software engineering techniques;
- 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.
Authors of selected papers accepted at MaLTeSQuE 2022 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.