Model Checking Conformance with Scenario-Based Specifications

Glusman, Marcelo; Katz, Shmuel

doi:10.1007/978-3-540-45069-6_32

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…The term consistency checking is used as the term conformance checking, which is a concept in process mining that is used to ensure that event executions comply with the process model [3]. Conformance checking can also be used as a validation technique in model checking together with specifications [66] and preconditions [80]. Similar to [3], we propose a validation technique for software traces and probabilistic models.…”

Section: Consistency Checking Of Probabilistic Models and Softwarementioning

confidence: 99%

Quantitative Verification of Stochastic Regular Expressions

Yaman

Pavese

Grunske

2021

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In this article, we introduce a probabilistic verification algorithm for stochastic regular expressions over a probabilistic extension of the Action based Computation Tree Logic (ACTL*). The main results include a novel model checking algorithm and a semantics on the probabilistic action logic for stochastic regular expressions (SREs). Specific to our model checking algorithm is that SREs are defined via local probabilistic functions. Such functions are beneficial since they enable to verify properties locally for sub-components. This ability provides a flexibility to reuse the local results for the global verification of the system; hence, the framework can be used for iterative verification. We demonstrate how to model a system with an SRE and how to verify it with the probabilistic action based logic and present a preliminary performance evaluation with respect to the execution time of the reachability algorithm.

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Section: Consistency Checking Of Probabilistic Models and Softwarementioning

confidence: 99%

Quantitative Verification of Stochastic Regular Expressions

Yaman

Pavese

Grunske

2021

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“…In [8], LSCs are used as a language for specifying properties of UML models. In [12] model-checking is used to prove certain equivalence properties between different executions of a collection of scenarios and [24,25] extend the results to scenarios that specify aspects.…”

Section: Related Workmentioning

confidence: 99%

Model-checking behavioral programs

Harel

Lampert

Marron

et al. 2011

Proceedings of the Ninth ACM International Conference on Embedded Software

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System specifications are often structured as collections of scenarios and use-cases that describe desired and forbidden sequences of events. A recently proposed behavioral programming approach, which evolved from the visual language of live sequence charts (LSCs), calls for coding software modules in alignment with such scenarios. We present a methodology and a supporting model-checking tool for verifying behavioral Java programs, without having to first translate them into a specific input language for the model checker. Our method facilitates early discovery of conflicting or under-specified scenarios, which can often be resolved by adding new scenarios rather than by changing existing code. Also, counterexamples provided by the tool are themselves event sequences that can serve directly for refinements and corrections. Our tool reduces the size of the execution state-space using an abstraction that focuses on behaviorally interesting states and treats transitions between them as atomic.

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“…Given a set of properties and a set of pointcut designators, this approach automatically generates sufficient conditions on the program's pointcuts to enable verification of advice in isolation. In a series of papers [3,4,9,30,41,[43][44][45], Katz and his group have addressed various issues of model checking aspect-oriented code. Model checking tasks were automatically generated for the woven code of aspect-oriented programs [41].…”

Section: Aspect Modeling and Checkingmentioning

confidence: 99%

“…Model checking tasks were automatically generated for the woven code of aspect-oriented programs [41]. They treated crosscutting scenarios as aspects and used model checking to prove the conformance between the scenario-based specification of aspects and the systems with aspects woven into them [43,45]. They also proposed an approach to generic modular verification of code-level aspects [44].…”

Section: Aspect Modeling and Checkingmentioning

confidence: 99%

Testing aspect‐oriented programs with finite state machines

El-Ariss

et al. 2010

Software Testing Verif & Rel

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Aspect-oriented programming yields new types of programming faults due to the introduction of new constructs for dealing with crosscutting concerns. To reveal aspect faults, this paper presents a framework for testing whether or not aspect-oriented programs conform to their state models. It supports two families of strategies (i.e. structure-oriented and property-oriented) for automated generation of aspect tests from aspect-oriented state models. A structure-oriented testing strategy derives tests and test code from an aspect-oriented state model to meet a given structural coverage criterion, such as state coverage, transition coverage, or round trip. A property-oriented testing strategy generates test code from the counterexamples of model checking. Two such strategies are checking an aspect-oriented state model against trap properties and checking mutants of aspect models against system properties. Mutation analysis of aspect-oriented programs is used to evaluate the effectiveness of these testing strategies. The experiments demonstrate that testing aspect-oriented programs against their state models can detect many aspect faults. The comparative evaluations also reveal that the structure-oriented and property-oriented testing strategies complement each other-some aspect faults were detected by the structure-oriented strategies, but not by the propertyoriented strategies and vice versa.Finite state machines have long been used for modeling and testing object-oriented programs. For example, a number of publications [6][7][8][9][10][11] have demonstrated that finite state models of individual classes and clusters of classes are effective formalisms for class testing and even integration testing of object-oriented programs. As AOP languages are typically built upon object-oriented languages (e.g. AspectJ is based on Java), finite state machines are also expected to be useful for testing aspect-oriented programs. As such, the objective of the research in this paper is to investigate how finite state models can be used to test aspect-oriented programs.This paper presents a framework, MACT (Model-based Aspect/class Checking and Testing), for testing whether or not aspect-oriented programs conform to their aspect-oriented state models. MACT provides explicit aspect-oriented notations (e.g. pointcut, advice, inter-model declarations, aspect and aspect precedence) for describing aspects at the abstraction level of UML protocol state machines [12]. MACT also provides two groups of strategies (i.e. structure-oriented and propertyoriented) for generating aspect tests from aspect-oriented state models. A structure-oriented strategy derives tests to meet a given structural coverage criterion, such as state coverage, transition coverage, and round trip. A property-oriented strategy generates tests from the counterexamples of model checking. Two such strategies are checking an aspect-oriented state model against trap properties (i.e. negated system properties) [13] and checking mutants (i.e. incorrect variations) [14] of aspe...

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Model Checking Conformance with Scenario-Based Specifications

Cited by 5 publications

References 10 publications

Quantitative Verification of Stochastic Regular Expressions

Quantitative Verification of Stochastic Regular Expressions

Model-checking behavioral programs

Testing aspect‐oriented programs with finite state machines

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