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Cited by 10 publications
References 71 publications
Context: 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 a more sophisticated chain of tools that support the use of symbolic tests. Results: We formally characterise mutation testing for Circus, defining the exhaustive test sets that can kill a given mutant. We also provide a technique to select tests from these sets based on specification traces of the mutants. Finally, we present mutation operators that consider faults related to both reactive and data manipulation behaviour. Altogether, we define a new fault-based test-generation technique for Circus. Conclusion: We conclude that mutation testing for Circus can truly aid making test generation from state-rich model more tractable, by focussing on particular faults.
Context: 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 a more sophisticated chain of tools that support the use of symbolic tests. Results: We formally characterise mutation testing for Circus, defining the exhaustive test sets that can kill a given mutant. We also provide a technique to select tests from these sets based on specification traces of the mutants. Finally, we present mutation operators that consider faults related to both reactive and data manipulation behaviour. Altogether, we define a new fault-based test-generation technique for Circus. Conclusion: We conclude that mutation testing for Circus can truly aid making test generation from state-rich model more tractable, by focussing on particular faults.
This paper concerns testing from an input output transition system (IOTS) model of a system under test that interacts with its environment through asynchronous first in first out (FIFO) channels. It explores methods for analysing an IOTS without modelling the channels. If IOTS M produces sequence σ then, since communications are asynchronous, output can be delayed and so a different sequence might be observed. Thus M defines a language T r(M ) of sequences that can be observed when interacting with M through FIFO channels. We define implementation relations and equivalences in terms of T r(M ): an implementation relation says how IOTS N must relate to IOTS M in order for N to be a correct implementation of M . It is important to use an appropriate implementation relation since otherwise the verdict from a test run might be incorrect and because it influences test generation. It is undecidable whether IOTS N conforms to IOTS M and so also whether there is a test case that can distinguish between two IOTSs. We also investigate the situation in which we have a finite automaton P and either wish to know whether T r(M ) ∩ L(P ) is empty or whether T r(M ) ∩ T r(P ) is empty and prove that these are undecidable. In addition, we give conditions under which conformance and intersection are decidable.
Due to the complexity of the nuclear industrial distributed control system (DCS), input-domain testing techniques, including random testing and combinatorial testing, are usually utilized to test the control logics in nuclear industrial DCS. To improve the fault detection e of random testing, the adaptive random testing technique selects a test case that significantly differs from all existing test cases. Similarly, to improve the fault detection efficiency of combinatorial testing, the greedy combinatorial testing technique adopts a greedy strategy to generate test cases that cover more uncovered tuple-combinations of parametric values. In this paper, we designed an experiment to compare the fault detection efficiency between adaptive random testing technique and greedy combinatorial testing technique for control logics of nuclear industrial DCS. Through the analysis of the fault detection ratios, the f-measure values, and the values of average percent of faults detected (APFD) on two experimental subjects, including the commonly used benchmarks in the field of Boolean-specification testing as well as a group of Boolean expressions extracted from the control logics in nuclear industrial DCS, the experimental results give us the following conclusions: (1) If the test suites' sizes are relatively small, the fault detection efficiencies of the two techniques are very close though there is a slight advantage in adaptive random testing; (2) With the gradual increase of test suites' sizes, the fault detection efficiency of greedy combinatorial testing is beyond adaptive random testing gradually. Such a result can help us select the appropriate testing techniques in the testing of the control logics in nuclear industry DCS.INDEX TERMS Input-domain testing, fault detection efficiency, adaptive random testing, combinatorial testing, nuclear industrial DCS.
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