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Fault coupling is the phenomenon whereby a test set is able to detect faults when they occur in isolation, but fails to do so when they occur in combination. It is widely regarded as a nuisance in fault-based approaches to software testing, which focus on the detection of single faults and normally neglect multiple faults. This paper presents a theoretical study of fault coupling, based on a simple model of fault-based testing. This provides for the presence of two faults that interact with each other and thus includes the possibility of fault coupling between them. The model is analysed mathematically, the conclusion reached being that fault coupling only occurs infrequently. This result provides support for current approaches to fault-based testing, but it is not quite enough to conclude that they are thereby validated. In effect, the paper generalizes the results of a previous paper that dealt with the restricted case where the functions underlying programs are bijective as well as finite. The restriction that functions be bijective is lifted here, but they are still required to be finite. Though the same theoretical framework is used in both cases, and more or less the same results are obtained, the techniques employed to arrive at the results in the general case are quite different.

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While mutation testing has proved to be an effective way of finding software faults, currently it is only applied to relatively small programs. One of the main reasons for this is the human analysis required in detecting equivalent mutants. Here program slicing is used to simplify this problem. Progam slicing is also used to reduce the number of equivalent mutants produced.

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Test data generation is one of the most technically challenging steps of testing software, but most commercial systems currently incorporate very little automation for this step. This paper presents results from a project that is trying to find ways to incorporate test data generation into practical test processes. The results include a new procedure for automatically generating test data that incorporates ideas from symbolic evaluation, constraint-based testing, and dynamic test data generation. It takes an initial set of values for each input, and dynamically ‘pushes’ the values through the control-flow graph of the program, modifying the sets of values as branches in the program are taken. The result is usually a set of values for each input parameter that has the property that any choice from the sets will cause the path to be traversed. This procedure uses new analysis techniques, offers improvements over previous research results in constraint-based testing, and combines several steps into one coherent process. The dynamic nature of this procedure yields several benefits. Moving through the control flow graph dynamically allows path constraints to be resolved immediately, which is more efficient both in space and time, and more often successful than constraint-based testing. This new procedure also incorporates an intelligent search technique based on bisection. The dynamic nature of this procedure also allows certain improvements to be made in the handling of arrays, loops, and expressions; language features that are traditionally difficult to handle in test data generation systems. The paper presents the test data generation procedure, examples to explain the working of the procedure, and results from a proof-of-concept implementation.

This paper investigates the mutation scores achieved by individual operators of the Mothra mutation system and their associated costs in order to determine the most efficient operators. The cost of mutation analysis includes both test set generation and equivalent mutant detection. The score and cost information is then used as a heuristic for choosing a subset of the operators for use in efficient selective mutation testing. Experiments were performed using a sample of 11 programs and a number of test sets for each program. The results show that the use of efficient operators can provide significant efficiency gains for selective mutation if the acceptable mutation score is not very close to one. When mutation scores very close to one are required, a randomly selected proportion of the mutants provides a more efficient strategy than a subset of efficient operators.

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Many researchers have pursued the establishment of a low-cost, effective testing and validation strategy at the program level as well as at the specification level. Mutation Testing is an error-based approach, originally introduced for program testing, that provides testers a systematic way to evaluate how good a given test set is. Some studies have also investigated its use to generate test sets. In this article, the application of Mutation Testing for validating Estelle specifications is proposed. A mutant operator set for Estelle—one of the crucial points for effectively applying Mutation Testing—is defined, addressing: the validation of the behavior of the modules, the communication among modules and the architecture of the specification. In this scope, these operators can be taken as a fault model. Considering this context, a strategy for validating Estelle-based specification is proposed and exemplified using the Alternating-bit protocol.