Practical combinatorial testing

Kuhn, D. Richard; Kacker, Raghu N.; Lei, Yu

doi:10.6028/nist.sp.800-142

Cited by 109 publications

(98 citation statements)

References 29 publications

(45 reference statements)

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“…We have implemented ABCHS-CAG in Java and three sets of experiments are conducted to make comparison on the basis of CA size and CA generation time. For the first experiment, smart phone application (SPA) benchmark problem [39] is considered. In the second experiment, we have taken a utility in a software application.…”

Section: Discussionmentioning

confidence: 99%

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A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

Bansal¹,

Sabharwal²,

Mittal³

2017

IJISA

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Abstract-Combinatorial Interaction Testing (CIT) is a cost effective testing technique that aims to detect interaction faults generated as a result of interaction between components or parameters in a software system. CIT requires the generation of effective test sets that cover all possible t-way (t denotes the strength of testing) interactions between parameters. Covering array (CA) and mixed covering array (MCA) are often used to represent test sets. This paper presents a hybrid algorithm that integrates artificial bee colony algorithm (ABC) and harmony search algorithm (HS) to construct CAs for testing all 2-way interactions (pair-wise testing) in software systems. The performance of the proposed hybrid algorithm ABCHS-CAG is compared and analyzed by performing experiments on a set of benchmark problems on pair-wise testing. The results show that ABCHS-CAG generates smaller CAs than its greedy counterparts whereas its performance is comparable to the existing state-of-the-art meta-heuristic algorithms.

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Section: Discussionmentioning

confidence: 99%

“…The wide spread and growing popularity of smart phones have led to the development of tens of thousands of smart phone applications or "apps" annually [39]. Android is one of the platforms for smart phone apps.…”

Section: A Benchmark 1:smart Phone Application Example (Spa)mentioning

confidence: 99%

A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

Bansal¹,

Sabharwal²,

Mittal³

2017

IJISA

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“…Similarly to the conventional test data generation, t-way sequences introduced by Kuhn et al [23] can be mapped onto our coverage criteria: t-wise coverage for both classes and transitions. The 1 -way sequence coverage of Kuhn et al corresponds to 1 -wise (or minimal) class coverage here.…”

Section: Coverage Criteriamentioning

confidence: 99%

Search based algorithms for test sequence generation in functional testing

Ferrer

Kruse

Chicano

et al. 2015

Information and Software Technology

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Context: The generation of dynamic test sequences from a formal specification, complementing traditional testing methods in order to find errors in the source code. Objective: In this paper we extend one specific combinatorial test approach, the Classification Tree Method (CTM), with transition information to generate test sequences. Although we use CTM, this extension is also possible for any combinatorial testing method. Method: The generation of minimal test sequences that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search-based approaches are required to find such (near) optimal test sequences. Results: The experimental analysis compares the search-based technique with a greedy algorithm on a set of 12 hierarchical concurrent models of programs extracted from the literature. Our proposed search-based approaches (GTSG and ACOts) are able to generate test sequences by finding the shortest valid path to achieve full class (state) and transition coverage. Conclusion: The extended classification tree is useful for generating of test sequences. Moreover, the experimental analysis reveals that our search-based approaches are better than the greedy deterministic approach, especially in the most complex instances. All presented algorithms are actually integrated into a professional tool for functional testing.

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“…Empirical results shown that software defects are caused by a single input parameter or a combination of a few input parameter values [17,18].…”

Section: Pairwise Testingmentioning

confidence: 99%

Input Modeling Prioritization Using Statistically User Profile for Pairwise Test Case Generation with Constraints Handling

Nakornburi¹,

Suwannasart²

2017

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Abstract. Pairwise testing is a widely used technique for software testing with the reduced size of the test suite and able to detect interactions that trigger the system's faults. In addition, pairwise testing test suites must be able to take constraints between input parameters and parameter values into account. In current practice, identifying and selecting input parameters and parameter values usually depends on tester skills that might not be sufficient. Input parameters and parameter values modeling and tools for easily guiding and prioritizing the selection of optimal input parameters and parameter values for the SUT is also required. In this work, we present an approach for prioritizing input parameters and parameter values modeling using statistical user profile. Our approach is implemented in a tool called UPPTCT which provides the ability to handle constraints on input parameters and parameter values for pairwise testing in order to generate test cases. We conduct experiments to evaluate test case effectiveness and compare our tool with other renowned pairwise test generation and constraints handling tools. The experimental results show that the effectiveness of our approach is significantly more efficient and effective than random testing as a large portion of reported defects with regard to statically user profile were caught by our approach. Furthermore, our tool performs better in some cases and performs comparable results for generating test cases upon input parameters and parameter values for both with constraints handling and without constraints handling.

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Practical combinatorial testing

Cited by 109 publications

References 29 publications

A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

Search based algorithms for test sequence generation in functional testing

Input Modeling Prioritization Using Statistically User Profile for Pairwise Test Case Generation with Constraints Handling

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