Syntactic Abstraction of B Models to Generate Tests

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 of the remaining variables. This paper complements previous results, based on domain abstraction for test generation, by adding a preliminary syntactic abstraction phase, based on variable elimination. We define a syntactic transformation that suppresses some variables from a B event model, in addition to a method that chooses relevant variables according to a test purpose. We propose two methods to compute an abstraction A of an initial model M. The first one computes A as a simulation of M, and the second one computes A as a bisimulation of M. The abstraction process produces a finite state system. We apply this abstraction computation to a Model Based Testing process.Comment: Tests and Proofs 2010, Malaga : Spain (2010

Génération de tests à partir de critères dynamiques de sélection et par abstraction

International audienceCet article présente une méthode de génération assistée de tests. Elle applique des critères dynamiques de sélection des tests (TP) sur un modèle formel comportemental (M) utilisé auparavant, par exemple par LTG, pour générer des tests fonctionnels à partir de critères statiques de sélection. On peut appliquer à M un critère dynamique de sélection TP mais ceci nécessite de représenter M par un automate. Pour des applications réelles, sa taille en nombre d'états et de transitions est beaucoup trop grande (voir infinie) pour être utilisable. Nous proposons une méthode pour extraire une abstraction de M à partir d'un objectif de test TP. Nous effectuons un produit synchronisé de cette abstraction avec TP afin de cibler les exécutions du système sous test qui satisfont TP. Puis nous générons des tests abstraits symboliques à partir de ce modèle réduit en appliquant les critères de couverture tous les états ou toutes les transitions. Cet ensemble de tests est valué à partir de M, concrétisé puis exécuté sur l'implémentation sous test. Cette méthode est proposée pour compléter la méthode BZ-TT de génération de tests à partir de critères statiques de sélection. L'utilisateur obtient des tests complémentaires en fournissant un critère dynamique de sélection. La méthode réutilise M, la couche de concrétisation des tests et l'infrastructure d'exécution des tests. L'originalité de l'approche est de construire une abstraction du modèle issue automatiquement de l'analyse statique d'un objectif de test formalisant des besoins de test d'une propriété dynamique du système

Associer des techniques de preuve et de résolution de contraintes pour la construction d'abstractions

National audienceCet article présente une méthode de génération assistée de tests. Elle applique des critères dynamiques de sélection des tests (TP) sur un modèle formel comportemental (M) utilisé auparavant, par exemple par LTG, pour générer des tests fonctionnels à partir de critères statiques de sélection. On peut appliquer à M un critère dynamique de sélection TP mais ceci nécessite de représenterM par un automate. Pour des applications réelles, sa taille en nombre d'états et de transitions est beaucoup trop grande (voir infinie) pour être utilisable. Nous proposons une méthode pour extraire une abstraction de M à partir d'un objectif de test TP. Nous effectuons un produit synchronisé de cette abstraction avec TP afin de cibler les exécutions du système sous test qui satisfont TP. Puis nous générons des tests abstraits symboliques à partir de ce modèle réduit en appliquant les critères de couverture tous les états ou toutes les transitions. Cet ensemble de tests est valué à partir de M, concrétisé puis exécuté sur l'implémentation sous test. Cette méthode est proposée pour compléter la méthode BZ-TT de génération de tests à partir de critères statiques de sélection. L'utilisateur obtient des tests complémentaires en fournissant un critère dynamique de sélection. La méthode réutilise M, la couche de concrétisation des tests et l'infrastructure d'exécution des tests. L'originalité de l'approche est de construire une abstraction du modèle issue automatiquement de l'analyse statique d'un objectif de test formalisant des besoins de test d'une propriété dynamique du système

B Model Slicing and Predicate Abstraction to Generate Tests

Accepted manuscript. Revised and extended version of a TAP'10 paper. To appear.International audienceIn 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 state variables that requires many proof obligations to be discharged, which can be very time-consuming for real applications. This paper presents a contribution to this problem that complements an approach based on domain abstraction for test generation, by adding a preliminary syntactic abstraction phase, based on variable elimination. We define a syntactic transformation that suppresses some variables from a B event model, in addition to three methods that choose relevant variables according to a test purpose. In this way, we propose a method that computes an abstraction of a source model {\mathsf{M}} according to a set of selected relevant variables. Depending on the method used, the abstraction can be computed as a simulation or as a bisimulation of {\mathsf{M}}. With this approach, the abstraction process produces a finite state system. We apply this abstraction computation to a model-based testing process. We evaluate experimentally the impact of the model simplification by variables' elimination on the size of the models, on the number of proof obligations to discharge, on the precision of the abstraction and on the coverage achieved by the test generation

Test Generation Based on Abstraction and Test Purposes to Complement Structural Tests

International audienceThis 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) selection criteria (test purposes called TP). Applying TP directly to M is usually not possible for industrial applications due to the huge (possibly infinite) size of their state space. We compute an abstraction A of M by predicate abstraction. We propose a method to define a set of abstraction predicates from information of TP. We generate symbolic tests from A by using TP as a dynamic selection criterion. Then we instantiate them on M, which allows us play the tests on the implementation the same way as we play the functional ones. Our experimental results show that our tests are complementary to the structural ones

Model-Based Testing using Symbolic Animation and Machine Learning

International audienceWe 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, whose behavior is inferred. Since the DFA obtained in this way may be an over-approximation and, thus, admit traces that were not admitted on the original model, this abstraction is refined using counter-examples made of unfeasible traces. The computation of counter-examples is performed using a systematic coverage of the DFA states and transitions, producing test sequences that are replayed on the model, providing either test cases for offline testing, or counter-examples that aim at refining the abstraction

Generating Tests from B Specifications and Dynamic Selection Criteria

International audienceThis 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 framework, a process of test generation that uses the symbolic animation mechanisms of LTG (Leirios Test Generator) based on constraint solving, and guided by the test purposes. We build for that a B model that is the synchronized product of a behavioral B abstract model and a test purpose described as a labelled transition system. We prove the correctness of this method, and show some experimental results obtained on the IAS case study. IAS is an industrial smart-card platform dedicated to the operations of Identification, Authentication and electronic Signature. Our experiments show that the tests obtained from test purposes are complementary to the structural tests

Generating tests from B specifications and dynamic selection criteria

International audienceThis 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 framework, a process of test generation that uses the symbolic animation mechanisms of Leirios Test Generator (LTG) based on constraint solving, and guided by the test purposes. We build for that a B model that is the synchronized product of a behavioral B abstract model and a test purpose described as a labeled transition system. We prove the correctness of this method, and show some experimental results obtained on the IAS case study. IAS is an industrial smart-card platform dedicated to the operations of Identification, Authentication and electronic Signature. Our experiments show that the tests obtained from test purposes are complementary to the structural tests

B Model Abstraction Combining Syntactic and Semantic Methods

International audienceIn a model-based testing approach as well as for the verification of properties by model-checking, 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 abstractions of the remaining variables. This paper illustrates a computer aided abstraction process that combines syntactic and semantic abstraction functions. The first function syntactically transforms a B event system M into an abstract one A, and the second one transforms a B event system into a Symbolic Labelled Transition System (SLTS). The syntactic transformation suppresses some variables in M. This function is correct in the sense that A is rened by M. A process that combines the syntactic and semantic abstractions has been experimented. It significantly reduces the time cost of semantic abstraction computation. This abstraction process allows for verifying safety properties by model-checking or for generating abstract tests. These tests are generated by a coverage criteria such as all states or all transitions of an SLTS

Generating Tests from B Specifications and Dynamic Selection Criteria

International audienceThis 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 framework, a process of test generation that uses the symbolic animation mechanisms of LTG (Leirios Test Generator) based on constraint solving, and guided by the test purposes. We build for that a B model that is the synchronized product of a behavioral B abstract model and a test purpose described as a labelled transition system. We prove the correctness of this method, and show some experimental results obtained on the IAS case study. IAS is an industrial smart-card platform dedicated to the operations of Identification, Authentication and electronic Signature. Our experiments show that the tests obtained from test purposes are complementary to the structural tests