Prioritizing Variable-Strength Covering Array

Huang, Rubing; Chen, Jinfu; Zhang, Tao; Wang, Rongcun; Lu, Yilong

doi:10.1109/compsac.2013.84

Cited by 14 publications

(9 citation statements)

References 34 publications

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“…Wang et al [15] combined test case weight and test case cost with Bryce's "pure prioritization strategy" in order to re-prioritize CAs. Besides, Huang et al [6] proposed two heuristic methods to prioritize a special CA named variable-strength covering array or VCA, which is also available to be used to guide the CA prioritization. Additionally, Huang et al [8] proposed a new method based on incremental interaction coverage in order to prioritize the arbitrary interaction test suites, which is not limited to CAs or VCAs.…”

Section: Traditional Interaction Test Suite Prioritizationmentioning

confidence: 99%

Adaptive random prioritization for interaction test suites

Huang

Chen

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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Combinatorial interaction testing (CIT), a black-box testing method, has been well studied in recent years. It aims at constructing an effective interaction test suites, so as to identify the faults that are caused by interactions among parameters. After interaction test suites are generated by CIT, the execution order of test cases in the test suite becomes critical due to limited testing resources. To determine test case order, the prioritization of interaction test suites has been employed. As we know, random prioritization (RP) of test cases has been considered as simple but ineffective. Existing research unveils that adaptive random prioritization (ARP) of test cases is an alternative and promising candidate that may replace RP. However, previous ARP techniques may not be used to prioritize interaction test suites due to the lack of source-code-related information in interaction test suite, such as statement coverage, function coverage, or branch coverage. In this paper, we not only propose the ARP strategy in order to prioritize interaction test suites by using interaction coverage information, without the source-code-related information, but also unify the RP strategy and traditional interaction-coverage based prioritization strategy (ICBP). Additionally, simulation studies indicate that the ARP strategy performs better than the RP strategy, test-case-generation prioritization, and reverse test-casegeneration prioritization, and can also be more time-saving than ICBP while greatly maintaining similar, or even better, effectiveness.

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Section: Traditional Interaction Test Suite Prioritizationmentioning

confidence: 99%

Adaptive random prioritization for interaction test suites

Huang

Chen

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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“…To reduce the prioritisation cost, a new FICBP technique has been proposed that uses repeated base‐choice coverage, FICBP with repeated base‐choice coverage (FICBPR) [36]. Although FICBPR leverages a similar mechanism to FICBP, it only assigns a value of 1 to the prioritisation strength

λ

and forgets previous prioritisation details when the coverage of 1‐wise value combinations is fully achieved. IICBP: IICBP [37, 38] first uses a small prioritisation strength

λ false(λ \geq 1 false)

, and presents it to the FICBP algorithm for prioritising the candidates. Once all

λ

‐wise value combinations have been covered by selected test cases, IICBP increases the prioritisation strength with an increment i –

λ = λ + i false(i \geq 1 false)

– and then uses this new prioritisation strength for the FICBP algorithm to prioritise remaining ATCs.…”

Section: Preliminaries and Backgroundmentioning

confidence: 99%

Prioritising abstract test cases: an empirical study

et al. 2019

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Test case prioritization (TCP) attempts to schedule the order of test case execution such that faults can be detected as quickly as possible. TCP has been widely applied in many testing scenarios, such as regression testing, and fault localization. Abstract test cases (ATCs) are derived from models of the system under test, and have been applied to many testing environments, such as model based testing, and combinatorial interaction testing. Although various empirical and analytical comparisons for some ATC prioritization (ATCP) techniques have been conducted, to the best of our knowledge, no comparative study focusing on the most current techniques has yet been reported. In this study, we investigated 18 ATCP techniques, categorized into four classes. We conducted a comprehensive empirical study to compare 16 of the 18 ATCP techniques in terms of their testing effectiveness and efficiency. We found that different ATCP techniques could be cost-effective in different testing scenarios, allowing us to present recommendations and guidelines for which techniques to use under what conditions.

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“…Rather the interaction may vary between groups of inputs. This has been described as a particular type of CIT and called the variable-strength CIT (VS-CIT) [13].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

Zamli

Ahmed

Mahmoud

et al. 2018

Swarm Intelligence - Volume 3: Applications

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Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact; variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem [1]. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system [2,3,4], to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems.We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.

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Prioritizing Variable-Strength Covering Array

Cited by 14 publications

References 34 publications

Adaptive random prioritization for interaction test suites

Adaptive random prioritization for interaction test suites

Prioritising abstract test cases: an empirical study

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

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