Research interests

  • Software testing: Model-based testing, search-based software testing, DevOps and test automation, mutation testing.
  • Software variability: Variability modeling, software product line engineering, variability intensive system testing.

Projects

Software Testing AMPlification (STAMP)

Logo BSR

Millions of lines of code - written in different languages by different people at different times, and operating on a variety of platforms - drive the systems performing key processes in our society. The resulting software needs to evolve and can no longer be controlled a priori as is illustrated by a range of software problems. The 3TU.BSR research program will develop novel techniques and tools to analyze software systems in vivo - making it possible to visualize behavior, create models, check conformance, predict problems, and recommend corrective actions.

Big Software on the Run (BSR)

Logo BSR

Millions of lines of code - written in different languages by different people at different times, and operating on a variety of platforms - drive the systems performing key processes in our society. The resulting software needs to evolve and can no longer be controlled a priori as is illustrated by a range of software problems. The 3TU.BSR research program will develop novel techniques and tools to analyze software systems in vivo - making it possible to visualize behavior, create models, check conformance, predict problems, and recommend corrective actions.

Variability Intensive system Behavioural teSting (VIBeS)

Logo VIBeS

This projects aims at providing model-driven testing tools working on Transition Systems, Featured Transition Systems and Usage Models in order to perform various testing tasks: test case selection, prioritization, mutation testing, etc.

Yet Another Model Inference (YAMI)

Logo YAMI

Usage models represents the usage scenarios of the software as well as their probability. This allows one to determine the relative importance of execution scenarios (with respect to other). This project explores the possibility to reverse engineer usage models based on execution traces contained in application logs.