Pattern Matching in Link Streams: a Token-based Approach

International audienceLink streams model the dynamics of interactions in complex distributed systems as sequences of links (interactions) occurring at a given time. Detecting patterns in such sequences is crucial for many applications but it raises several challenges. In particular, there is no generic approach for the specification and detection of link stream patterns in a way similar to regular expressions and automata for text patterns. To address this, we propose a novel automata framework integrating both timed constraints and finite memory together with a recognition algorithm. The algorithm uses structures similar to tokens in high-level Petri nets and includes non-determinism and concurrency. We illustrate the use of our framework in real-world cases and evaluate its practical performances

Exploiting model morphology for event-based testing

Model-based testing employs models for testing. Model-based mutation testing (MBMT) additionally involves fault models, called mutants, by applying mutation operators to the original model. A problem encountered with MBMT is the elimination of equivalent mutants and multiple mutants modeling the same faults. Another problem is the need to compare a mutant to the original model for test generation. This paper proposes an event-based approach to MBMT that is not fixed on single events and a single model but rather operates on sequences of events of length k ≥ 1 and invokes a sequence of models that are derived from the original one by varying its morphology based on k. The approach employs formal grammars, related mutation operators, and algorithms to generate test cases, enabling the following: (1) the exclusion of equivalent mutants and multiple mutants; (2) the generation of a test case in linear time to kill a selected mutant without comparing it to the original model; (3) the analysis of morphologically different models enabling the systematic generation of mutants, thereby extending the set of fault models studied in related literature. Three case studies validate the approach and analyze its characteristics in comparison to random testing and another MBMT approach

Nested-unit Petri nets

International audiencePetri nets can express concurrency and nondeterminism but neither locality nor hierarchy. This article presents an extension of Petri nets, in which places can be grouped into so-called "units" expressing sequential components. Units can be recursively nested to reflect both the concurrent and hierarchical nature of complex systems. This model called NUPN (Nested-Unit Petri Nets) was originally developed for translating process calculi to Petri nets, but later found also useful beyond this setting. It allows significant savings in the memory representation of markings for both explicit-state and symbolic verification. Thirteen software tools already implement the NUPN model, which has also been adopted for the benchmarks of the Model Checking Contest (MCC) and the parallel problems of the Rigorous Examination of Reactive Systems (RERS) challenges

Concurrent Regular Expressions and their Relationship to Petri Nets

We define algebraic systems called concurrent regular expressions which provide a modular description of languages of Petri nets. Concurrent regular expressions are extension of regular expressions with four operators - interleaving, interleaving closure, synchronous composition and renaming. This alternative characterization of Petri net languages gives us a flexible way of specifying concurrent systems. Concurrent regular expressions are modular and hence easier to use for specification. The proof of equivalence also provides a natural decomposition method for Petri nets. 1 Introduction Formal models proposed for specification and analysis of concurrent systems can be categorized roughly into two groups: algebra based and transition based. The algebra based models specify all possible behaviors of concurrent systems by means of expressions that consist of algebraic operators and primitive behaviors. Examples of such models are path expressions[3], behavior expressions[21] and extend..