Variability-intensive systems (VIS) form a large and heterogeneous class of systems whose behaviour can be modified by enabling or disabling predefined features. Variability mechanisms allows the adaptation of software to the needs of their users and the environment. However, VIS verification and validation (VV) is challenging: the combinatorial explosion of the number of possible behaviours and undesired feature interactions are amongst such challenges. To tackle them, Featured Transitions Systems (FTS) were proposed a decade ago to model and verify the behaviours of VIS. In an FTS, each transition is annotated with a combination of features determining which variants can execute it. An FTS can model all possible behaviours of a given VIS. This compact model enabled us to create efficient VV algorithms taking advantage of the behaviours shared amongst features resulting in a reduction of the VV effort by several orders of magnitude. In this paper, we will cover the formalism, its applications and sketch promising research directions.@en

Mutation analysis is a popular technique for assessing the strength of test suites. It relies on the mutation score, which indicates their fault-revealing potential. Yet, there are mutants whose behaviour is equivalent to the original system, wasting analysis resources and preventing the satisfaction of a 100% mutation score. For finite behavioural models, the Equivalent Mutant Problem (EMP) can be transformed to the language equivalence problem of non-deterministic finite automata for which many solutions exist. However, these solutions are quite expensive, making computation unbearable when used for tackling the EMP. In this paper, we report on our assessment of a state-of-the-art exact language equivalence tool and two heuristics we proposed. We used 12 models, composed of (up to) 15,000 states, and 4710 mutants. We introduce a random and a mutation-biased simulation heuristics, used as baselines for comparison. Our results show that the exact approach is often more than ten times faster in the weak mutation scenario. For strong mutation, our biased simulations can be up to 1000 times faster for models larger than 300 states, while limiting the error of misclassifying non-equivalent mutants as equivalent to 8% on average. We therefore conclude that the approaches can be combined for improved efficiency.@en

In this work, we ask a question whether Convolutional Neural Networks are more suitable for side-channel attacks than some other machine learning techniques and if yes, in what situations. Our results point that Convolutional Neural Networks indeed outperform machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for Convolutional Neural Networks when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similarly or sometimes even better. The experiments with guessing entropy indicate that methods like Random Forest or XGBoost could perform better than Convolutional Neural Networks for the datasets we investigated.@en