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 quality evaluation, 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 evaluation as well as their application to novel contexts;
- The effectiveness evaluation of machine learning methods, both compared to other automated approaches and the 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.
SODALITE will be featured within the presentation of the paper "DeepIaC: Deep Learning-based Linguistic Anti-pattern Detection in IaC" written by Nemania Borovits (Tilburg University/JADS), Indika Kumara (Eindhoven University of Technology/JADS), Parvathy Krishnan (Tilburg University/JADS), Stefano Dalla Palma (Tilburg University/JADS), Dario Di Nucci (Tilburg University/JADS), Fabio Palomba (University of Salerno), Damian Andrew Tamburri (Eindhoven University of Technology/JADS), Willem-Jan van den Heuvel (Tilburg University/JADS).