SQUAL.AI

Artificial intelligence techniques, particularly machine learning ones, promise to foster breakthroughs in software engineering via the exploitation of large repository data, including code, commits, and human-written comments. This project offers to exploit such techniques to automate quality assurance for highly complex systems, which include configurable systems, AI-enabled systems, or even quantum software. While extremely different, these systems have high variability and uncertainty in common. These two characteristics challenge traditional quality assurance methods, making them inaccurate for smell detection and further complicating the oracle problem in testing. We will combine monitoring such systems and active learning to automatically learn thresholds for smell detection and assess the smells’ impact via automated testing. We will validate our findings on open-source systems fostering open science and reproducibility.

With the support of Wallonie-Bruxelles International.

Martin Balfroid
Martin Balfroid
PhD Student
Benoît Vanderose
Benoît Vanderose
Professor of Software Engineering
Xavier Devroey
Xavier Devroey
Professor of Software Engineering

My research goal is to to ease software testing by exploring new paths to achieve a high level of automation for test case design, generation, selection, and prioritization. My main research interests include search-based and model-based software testing, test suite augmentation, DevOps, and variability-intensive systems.