Pierre-Christophe Bué

  • Citations Per Year
Learn More
This paper presents a computer aided model-based test generation method. We propose this approach as a complement to the LTG (Leirios Test Generator) method, which extracts functional tests out of a formal behavioral model M by means of static (or structural) selection criteria. Our method computes additional tests by applying dynamic (or behavioral)(More)
This paper is about generating tests from dynamic selection criteria called test purposes, in addition to structural tests, obtained from static selection criteria. We present a method that re-uses a behavioral model and an abstract test concretization layer developed for structural testing, and relies on additional test purposes. We propose, in the B(More)
In a model-based testing approach as well as for the verification of properties, B models provide an interesting modeling solution. However, for industrial applications, the size of their state space often makes them hard to handle. To reduce the amount of states, an abstraction function can be used. The abstraction is often a domain abstraction of the(More)
In a model-based testing approach as well as for the verification of properties, B models provide an interesting solution. However, for industrial applications, the size of their state space often makes them hard to handle. To reduce the amount of states, an abstraction function can be used, often combining state variable elimination and domain abstractions(More)
In this paper we present a Model-Based Testing approach with which we generate tests from an abstraction of a source behavioural model. We show a new algorithm that computes the abstraction as an under-approximation of the source model. Our first contribution is to combine two previous approaches proposed by Ball and Pasareanu et al. to compute May, Must+(More)
We present in this paper a technique based on symbolic animation of models that aims at producing model-based tests. In order to guide the animation of the model, we rely on the use of a deterministic finite automaton (DFA) of the model that is built using a well-known machine learning algorithm, that considers a complex model as a black-box component,(More)
In a model-based testing approach as well as for the verification of properties by modelchecking, B models provide an interesting solution. But for industrial applications, the size of their state space often makes them hard to handle. To reduce the amount of states, an abstraction function can be used, often combining state variable elimination and domain(More)
  • 1