Testview calculus larson10/27/2022 We show that our method allows us to findīugs much faster (up to 16 times according to our experiments) thanĮxhaustive methods. This, we perform random walks in the featured transition system Linear-Time Logic (LTL) within a given simulation budget. The first case considers the problem ofįinding all variants that violate a given property expressed in We investigate their utility in twoĬomplementary use cases. New methods allow the sampling of executions from one or more Model that represents jointly the state spaces of all variants. #Testview calculus larson verification#To reduce verification time, we propose toĬombine SMC with featured transition systems (FTS)-a Variants, which makes the verification problem harder than forĬlassical systems. State-space of such systems is exponential in the number of Identify bugs in variability-intensive systems (VIS). We propose a new Statistical Model Checking (SMC) method to The proposed approach achieves the highest mutation score in positive and negative testing for all case studies in comparison with two existing methods (regular expression-based test generation and context-based random test generation), using four different techniques. To critically validate the proposed approach, three case studies (a sequence detector, a traffic light controller, and a RISC-V processor) are used and the strengths and weaknesses of the approach are discussed. All the steps are supported by a toolchain that is already implemented and is available online. The current paper comprises a preparation step (consisting of the sub-steps model construction, model mutation, model conversion, and test generation) and a composition step (consisting of the sub-steps pre-selection and construction of Ideal test suites). Using the techniques known from automata theory, test selection criteria are developed and formally show that they fulfill the major requirements of Fundamental Test Theory, that is, reliability and validity. These test sequences are then executed on original (fault-free) and mutant (faulty) HDL programs, in the sense of mutation testing. Test sequences are generated from both original (fault-free) and mutant (faulty) models in the sense of positive and negative testing, forming a holistic test view. Based on the Fundamental Test Theory of Goodenough and Gerhart (IEEE Trans Softw Eng SE-1(2):156–173, 1975), this paper proposes an approach to model-based ideal testing of hardware description language (HDL) programs based on their behavioral model. We apply our method to an example specification and evaluate the resulting test sets with coverage metrics on a Java implementation.Īn ideal test is supposed to show not only the presence of bugs but also their absence. Second, in sharp contrast to program-based mutation analysis, equivalent mutant identification is also automatic. First, test case generation is automatic each counterexample is a complete test case. There are substantial advantages to combining a model checker with mutation analysis. We define two classes of operators: those that produce test cases from which a correct implementation must differ, and those that produce test cases with which it must agree. The counterexamples can easily be turned into complete test cases, that is, with inputs and expected results. A model checker generates counterexamples which distinguish the variations from the original specification. The operators define a form of mutation analysis at the level of the model checker specification. We define syntactic operators, each of which produces a slight variation on a given model. We apply a model checker to the problem of test generation using a new application of mutation analysis.
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