Software and Hardware Testing Using Combinatorial Covering Suites

Hartman, Alan

doi:10.1007/0-387-25036-0_10

Cited by 146 publications

(107 citation statements)

References 36 publications

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“…It is implemented in Java programming language and generates test cases for pairwise testing using GA. A series of experiments were conducted in order to empirically evaluate the effectiveness of the proposed approach. The performance was evaluated using the following six benchmark problems of different sizes: 3 4 distinct possible values. These six benchmark problems were input to PWiseGen with an existing approach, as described in [18], and to our improved approach.…”

Section: Resultsmentioning

confidence: 99%

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Test Set Generation for Pairwise Testing Using Genetic Algorithms

2015

J Inf Process Syst

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In software systems, it has been observed that a fault is often caused by an interaction between a small number of input parameters. Even for moderately sized software systems, exhaustive testing is practically impossible to achieve. This is either due to time or cost constraints. Combinatorial (t-way) testing provides a technique to select a subset of exhaustive test cases covering all of the t-way interactions, without much of a loss to the fault detection capability. In this paper, an approach is proposed to generate 2-way (pairwise) test sets using genetic algorithms. The performance of the algorithm is improved by creating an initial solution using the overlap coefficient (a similarity matrix). Two mutation strategies have also been modified to improve their efficiency. Furthermore, the mutation operator is improved by using a combination of three mutation strategies. A comparative survey of the techniques to generate t-way test sets using genetic algorithms was also conducted. It has been shown experimentally that the proposed approach generates faster results by achieving higher percentage coverage in a fewer number of generations. Additionally, the size of the mixed covering arrays was reduced in one of the six benchmark problems examined.

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Section: Resultsmentioning

confidence: 99%

“…For the generation of test cases from parameter combinations, various combinatorial objects are used, such as Latin squares [4], orthogonal arrays [5], covering arrays [5,6], etc. Out of these, covering arrays (CAs) are the most widely used.…”

Section: Introductionmentioning

confidence: 99%

Test Set Generation for Pairwise Testing Using Genetic Algorithms

2015

J Inf Process Syst

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“…Many approaches [10], [15] allow constraints only in the form of forbidden configurations [16]. A forbidden combination would be translated in our model as a not statement.…”

Section: B Constraintsmentioning

confidence: 99%

CITLAB: A Laboratory for Combinatorial Interaction Testing

Gargantini

Vavassori

2012

2012 IEEE Fifth International Conference on Software Testing, Verification and Validation

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Abstract-Although the research community around combinatorial interaction testing has been very active for several years, it has failed to find common solutions on some issues. First of all, there is not a common abstract nor concrete language to express combinatorial problems. Combinatorial testing generator tools are strongly decoupled making difficult their interoperability and the exchange of models and data. In this paper, we propose an abstract and concrete specific language for combinatorial problems. It features and formally defines the concepts of parameters and types, constraints, seeds, and test goals. The language is defined by means of XTEXT, a framework for the definition of domain-specific languages. XTEXT is used to derive a powerful editor integrated with eclipse and with all the expected features of a modern editor. Eclipse is also used to build an extensible framework in which test generators, importers, and exporters can be easily added as plugins.

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“…Because here we are looking to cover 80 3-way interactions but the total interactions covered is 74. These four choices of rows (1, 2, 3), (1,2,4), (1,3,4), (2,3,4) contain all eight possible values of the three parameters and remaining six choices contain only seven possible values of the three parameters. Given fixed values of n, k, and s, the covering arrays with budget constraints problem is to construct a covering array 3-CA(n, k, s) having large coverage measure µ.…”

Section: Covering Arrays With Budget Constraintsmentioning

confidence: 99%