Valeria Pontillo

Valeria Pontillo is a Researcher in the Department of Informatics and Applied Informatics, focusing on software engineering, machine learning, and testing methodologies. Her work emphasizes empirical investigations into test smell detection, flaky test prediction, and security testing practices. She has contributed to datasets like E2EGit and led studies on test automation and anomaly detection. Pontillo's research has been recognized with the MSR 2025 Distinguished Dataset Award. She actively organizes workshops on topics like intelligent software assistants and security testing for complex systems.

Her research interests include software quality, empirical software engineering, and static/dynamic analysis of test code. Recent work explores the impact of test smells on manual testing, cross-project flaky test prediction, and performance testing in open-source web projects. Collaborations involve analyzing test maintenance practices and developing frameworks like QuantuMoonLight for quantum machine learning experimentation.

Pontillo's datasets and publications highlight a focus on practical applications of machine learning in testing, with over 14 peer-reviewed articles since 2021. She maintains active participation in academic events, contributing to both conference proceedings and workshop organization globally.