Formal mutation testing for Circus

International audienceContext: The demand from industry for more dependable and scalable test-development mechanisms has fostered the use of formal models to guide the generation of tests. Despite many advancements having been obtained with state-based models, such as Finite State Machines (FSMs) and Input/Output Transition Systems (IOTSs), more advanced formalisms are required to specify large, state-rich, concurrent systems. Circus, a state-rich process algebra combining Z, CSP and a refinement calculus, is suitable for this; however, deriving tests from such models is accordingly more challenging. Recently, a testing theory has been stated for Circus, allowing the verification of process refinement based on exhaustive test sets. Objective: We investigate fault-based testing for refinement from Circus specifications using mutation. We seek the benefits of such techniques in test-set quality assertion and fault-based test-case selection. We target results relevant not only for Circus, but to any process algebra for refinement that combines CSP with a data language. Method: We present a formal definition for fault-based test sets, extending the Circus testing theory, and an extensive study of mutation operators for Circus. Using these results, we propose an approach to generate tests to kill mutants. Finally, we explain how prototype tool support can be obtained with the implementation of a mutant generator, a translator from Circus to CSP, and a refinement checker for CSP, and with

10421 Abstracts Collection -- Model-Based Testing in Practice

From 17.10. to 22.10.2010, the Dagstuhl Seminar 10421 ``Model-Based Testing in Practice \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Model-Based Robustness Testing in Event-B Using Mutation

International audienceRobustness testing aims at finding errors in a system under invalid conditions, such as unexpected inputs. We propose a robust-ness testing approach for Event-B based on specification mutation and model-based testing. We assume that a specification describes the valid inputs of a system. By applying negation rules, we mutate the precondition of events to explore invalid behaviour. Tests are generated from the mutated specification using ProB. ProB has been adapted to efficiently process mutated events. Mutated events are statically checked for satisfiability and enability using constraint satisfaction, to prune the transition search space. This has dramatically improve the performance of test generation. The approach is applied to the Java Card bytecode verifier. Large mutated specifications (containing 921 mutated events) can be easily tackled to ensure a good coverage of the robustness test space

Testing robots using CSP

This paper presents a technique for automatic generation of tests for robotic systems based on a domain-specific notation called RoboChart. This is a UML-like diagrammatic notation that embeds a component model suitable for robotic systems, and supports the definition of behavioural models using enriched state machines that can feature time properties. The formal semantics of RoboChart is given using tockCSP, a discrete-time variant of the process algebra CSP. In this paper, we use the example of a simple drone to illustrate an approach to generate tests from RoboChart models using a mutation tool called Wodel. From mutated models, tests are generated using the CSP model checker FDR. The testing theory of CSP justifies the soundness of the tests

Outlook Magazine, Spring 2013

https://digitalcommons.wustl.edu/outlook/1189/thumbnail.jp

Illinois State University Life, Vol. 18, No. 5, April 1984

The official faculty-staff-parent newsletter of Illinois State University, April 1984 issue.https://ir.library.illinoisstate.edu/isulife/1066/thumbnail.jp

Coevolution of Second-order-mutant

One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case