Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the Cuckoo Search algorithm

Ahmed, Bestoun S.; Abdulsamad, Taib Sh.; Potrus, Moayad Yousif

doi:10.1016/j.infsof.2015.05.005

Cited by 89 publications

(88 citation statements)

References 57 publications

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“…In fact, MIPOG establishes the best overall results for CA(N; 4, 5 7 ) and CA(N; 4, 5 11 ). ITCH is the runner up giving the best overall result for CA(N; 4, 5 12 ).…”

Section: Discussionmentioning

confidence: 95%

“…Cuckoo Search (CS) [11] is a recent strategy for t-way test generation. At the start, the algorithm generates random initial nests.…”

Section: Meta-heuristic-based Strategiesmentioning

confidence: 99%

“…Any particular meta-heuristic can either be available for selection or penalized in the Tabu list depending on its prior performance. Meanwhile, the value of c1= 1.375, c2= 1.375, ω = 0.3 and pa = 0.25 are adopted from existing work reported in the context of adopting PSO [7] and CS [11] as t-way `test generation strategy respectively.…”

Section: Hhh Parameter Calibrationmentioning

confidence: 99%

“…We compare HHH against existing strategies based on the benchmark experiments in terms of test sizes as defined in [9,11,56,[63][64][65][66][67][68]. Considering that the test sizes are absolute and not affected by the computational platform, our comparison spans many t-way strategies from meta-heuristic-based to general computation ones.…”

Section: Benchmarking With Existing Strategies On Test Sizesmentioning

confidence: 99%

“…Search Based Software Engineering (SBSE) [2][3][4], is a relatively new field that has proposed meta-heuristic based t-way strategies (e.g. Genetic Algorithms (GA) [5], Particle Swarm Optimization (PSO) [6,7], Harmony Search Algorithm (HS) [8], Ant Colony Algorithm (ACO) [5], Simulated Annealing (SA) [9,10] and Cuckoo Search (CS) [11]). The adoption of these meta-heuristic based strategies appears to be effective for obtaining good quality solutions, as reported in benchmarking experiments related to t-way testing [7,8].…”

Section: Introductionmentioning

confidence: 99%

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A Tabu Search hyper-heuristic strategy for t-way test suite generation

Zamli

Alkazemi

Kendall

2016

Applied Soft Computing

101

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This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning Based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks.

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“…In fact, MIPOG establishes the best overall results for CA(N; 4, 5 7 ) and CA(N; 4, 5 11 ). ITCH is the runner up giving the best overall result for CA(N; 4, 5 12 ).…”

Section: Discussionmentioning

confidence: 95%

“…Cuckoo Search (CS) [11] is a recent strategy for t-way test generation. At the start, the algorithm generates random initial nests.…”

Section: Meta-heuristic-based Strategiesmentioning

confidence: 99%

Section: Hhh Parameter Calibrationmentioning

confidence: 99%

Section: Benchmarking With Existing Strategies On Test Sizesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Tabu Search hyper-heuristic strategy for t-way test suite generation

Zamli

Alkazemi

Kendall

2016

Applied Soft Computing

101

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An Optimal Solution for Test Case Generation Using ROBDD Graph and PSO Algorithm

Kalaee

Rafe

2015

Quality & Reliability Eng

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Software testing is one of the most important techniques to examine the behavior of the software products to assure their quality. An effective and efficient testing approach must balance two important but conflicting requirements. One of them is the accuracy that needs a large number of test cases for testing, and the second one is reducing the time and cost, which requires a few test cases. Even for small software, the number of possible test cases is typically very large, and exhaustive testing is impractical. Hence, selecting appropriate test suite is necessary. Cause–effect graph testing is a common black‐box testing technique, which is equivalently representing Boolean relations between input parameters. However, the other traditional black‐box strategies cannot identify the relations that it may result in loss of some of the important test cases. Although the cause–effect graph is regarded very promising in specification testing, it is observed that most of the proposed approaches using the graph are complex or generate impossible and a large number of test cases. This observation has motivated our research to propose an efficient strategy to generate minimal test suite that simultaneously achieves high coverage of input parameters. To do so, at first, we identify major effects from the cause–effect graph using reduced ordered binary decision diagram (ROBDD). ROBDD makes the related Boolean expression of the graph concise and obtains a unique representation of the expression. Using the ROBDD, it is possible to reduce the size of the generated test suite and to perform testing faster. After that, our proposed method utilizes particle swarm optimization (PSO) algorithm to select the optimal test suite, which covers all pairwise combinations of input parameters. The experimental results show that our approach simultaneously achieves high efficacy and reduces cost of testing by selecting appropriate test cases, respectively, to both test size and coverage size. Also, it outperforms some existing state‐of‐the‐art strategies in the black‐box testing. Copyright © 2015 John Wiley & Sons, Ltd.

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