An applicable family of data flow testing criteria

Frankl, Phyllis G.; Weyuker, Elaine J.

doi:10.1109/32.6194

Cited by 436 publications

(310 citation statements)

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“…These criteria define which kinds of entities in the program must be covered (i.e., executed at least once) during testing [6]. Some of these criteria require that control-flow entities such as statements or branches are covered, whereas other criteria require the coverage of data-flow entities such as definitionuse pairs (du-pairs) (see PDG in Section II-B).…”

Section: A Test Adequacy Criteria and Testing Strategiesmentioning

confidence: 99%

“…One problem with adequacy criteria is that it is often impossible to achieve 100% satisfaction because some test requirements are either infeasible (i.e., not coverable by any execution) [6] or too difficult and expensive to cover. Thus, in reality, testers must balance software-quality needs with budget constraints.…”

Section: A Test Adequacy Criteria and Testing Strategiesmentioning

confidence: 99%

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Applying aggressive propagation-based strategies for testing changes

Santelices

Harrold

2011

2011 Fourth IEEE International Conference on Software Testing, Verification and Validation

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Abstract-Test-suite augmentation for evolving softwarethe process of augmenting a test suite to adequately test software changes-is necessary for any program that undergoes modifications as part of its development and maintenance cycles. Recently, we presented a new technique for test-suite augmentation based on leveraging the propagation conditions for the effects of changes. Although empirical studies show that this technique can be quite effective for testing changes, the experiments have been limited because of the complexity of the implementation. In this paper, we present a new and more efficient approach for propagation-based testing of changes that can reach much longer propagation-distances and can focus the testing more precisely on those behaviors of changes that can actually affect the output. Using an implementation of this new approach, we performed a study on a set of changes on Java programs for which we compared, to a much larger extent, our propagation-based strategy with other existing techniques for testing changes. The results of the study not only confirm the superior effectiveness of propagation-based strategies over these other techniques for testing changes, but also quantify that superiority and clarify the conditions under which our approach is most effective.

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Section: A Test Adequacy Criteria and Testing Strategiesmentioning

confidence: 99%

Section: A Test Adequacy Criteria and Testing Strategiesmentioning

confidence: 99%

Applying aggressive propagation-based strategies for testing changes

Santelices

Harrold

2011

2011 Fourth IEEE International Conference on Software Testing, Verification and Validation

View full text Add to dashboard Cite

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“…[9]). Structural analysis of procedures is usually based on the analysis of their control flow graph that may be supplemented by data flow information.…”

Section: 2 Structural Coverage Of Programsmentioning

confidence: 99%

“…The relation between criteria is the inclusion relation [9]. Criterion A includes criterion B, if and only if each test set that covers A also covers B.…”

Section: 2 Hierarchy Of the Criteriamentioning

confidence: 99%

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Test Criteria Definition for B Models

Behnia¹,

Waeselynck²

1999

FM’99 — Formal Methods

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Abstract. Test criteria are defined in order to guide the selection of subsets of the input domain to be covered during testing. A unification of two categories of test criteria, program based and specification based, i s presented. Such a unification is possible for B models because the specification, refinement concepts and implementation are captured in one notation. The notion of control flow graph is extended to handle the abstract constructs of the generalized substitution language, and a link between the coverage of the graph and the coverage of the before-after predicate i s established. A set of criteria for the coverage of the control flow graph i s proposed. These criteria are partially ordered according to their stringency, so that the coverage strategy may be tuned according to the complexity of the operation under test. IntroductionTesting is a partial verification technique that consists in exercising a target piece of software by supplying it with a sample of input values. Since exhaustive testing is generally not tractable, the tester is faced with the problem of selecting a proper subset of the input domain. The selection is guided by test criteria that specify a set of elements to be covered during testing. This paper focuses on the definition of test criteria for B models. It extends previous work establishing a validation framework for the B development process [13]. The aim is to track down specification faults originating from a misunderstanding of the functional requirements, or from the failure to adequately express an understood requirement. The B formal development process can be seen as a series of stages where more and more concrete models of the application are built, the final code being just a compiled version of the most concrete one. In order to validate these models, we have defined a uniform testing framework, irrespective of the development stage and of whether the test inputs are supplied to the final code or to the formal models [13]. Within the uniform framework, we wish to be equipped with coverage criteria that can be applied not only to the most abstract model (i.e. before refinement), but also to any intermediate model obtained during development. This must be so because, in typical B projects, the smallest meaningful model with respect to the functional requirements is likely to involve a few steps of refinement (see e.g. the modeling approach adopted

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The dynamic domain reduction procedure for test data generation

Offutt

Jin

Pan

1999

Softw: Pract. Exper.

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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 constraintbased 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. Constraint-based test data generationPrevious work [6,22] presented an approach to test data generation that uses control-flow analysis, symbolic evaluation, and information about mutants to automatically generate test † Note that this definition does not include all test criteria. For example, testing can be based on mean-time-to-failure, which does not use test requirements or a coverage measure. Non-structural criteria are not considered in this research.

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An applicable family of data flow testing criteria

Cited by 436 publications

References 10 publications

Applying aggressive propagation-based strategies for testing changes

Applying aggressive propagation-based strategies for testing changes

Test Criteria Definition for B Models

The dynamic domain reduction procedure for test data generation

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