My PhD Project in One Poster

My PhD project is called LIFTS for “LearnIng Featured Transition Systems”. It aims at automatically learning transition systems that capture the behaviour of a whole family of software-based systems. Reasoning at the family level has been shown to yield important economies of scale and quality improvements for a broad range of systems such as software product lines, adaptive and configurable systems. Yet, to fully benefit from the above advantages, a model of the system family’s behaviour is necessary. Such a model is often prohibitively expensive to create manually due to the combinatorial explosion of system variants (that is, all the configurations corresponding to the different members of the system family). For large long-lived systems with outdated specifications or for systems that continuously adapt, the modeling cost is even higher. Therefore, this thesis proposes to automate the learning of such models from existing artifacts. To advance research at a fundamental level, our learning target are Featured Transition Systems (FTS), an abstract formalism that can be used to provide a pivot semantics to a range of state-based modeling languages such as UML state diagrams (adapted to software families). More specifically, the main research questions addressed by this project are:

1. Can we learn variability-aware models efficiently?
2. Can we learn FTS in a black-box fashion? (i.e. with access to execution logs but not to source code);
3. Can we learn FTS in a white/grey-box testing fashion? (i.e. with access to source code);
4. How do the proposed techniques scale in practice?

This project was summarised in the following poster. It was presented in April 2022, during the second edition of the Women & Girls in Science Day organised by the University of Namur (Belgium).