Algebraic strategy to generate pairwise test set for prime number parameters and variables

Younis, Mohammed Issam; Zamli, Kamal Z.; Isa, Nor Ashidi Mat

doi:10.1109/itsim.2008.4632002

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…The principle underlying the QLSCA-based strategy is highlighted in Fig 6. Nevertheless, to apply the general QLSCA to the t − way test generation if the no of 1s in b == t then 12 Set hash k ey = b 13 Append the rest of p[index] with dont care if necessary 14 Put p into the hashmap, H s , using the hash key 15 Return H s problem, three adaptations must be made. The first adaptation involves the input parameters.…”

Section: Test Suite Generation Algorithmmentioning

confidence: 99%

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

et al. 2018

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The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

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Section: Test Suite Generation Algorithmmentioning

confidence: 99%

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

et al. 2018

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“…The CIT approach can systematically reduce the number of test cases by selecting a subset from exhaustive testing combination based on the strength of parameter interaction coverage (t) [2]. To illustrate the CIT approach, consider the web-based system example (see Table 1) [3].…”

Section: Introductionmentioning

confidence: 99%

A Strategy for Automatic Quality Signing and Verification Processes for Hardware and Software Testing

2016

Data Structure and Software Engineering

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We propose a novel strategy to optimize the test suite required for testing both hardware and software in a production line. Here, the strategy is based on two processes: Quality Signing Process and Quality Verification Process, respectively. Unlike earlier work, the proposed strategy is based on integration of black box and white box techniques in order to derive an optimum test suite during the Quality Signing Process. In this case, the generated optimal test suite significantly improves the Quality Verification Process. Considering both processes, the novelty of the proposed strategy is the fact that the optimization and reduction of test suite is performed by selecting only mutant killing test cases from cumulating t-way test cases. As such, the proposed strategy can potentially enhance the quality of product with minimal cost in terms of overall resource usage and time execution. As a case study, this paper describes the step-by-step application of the strategy for testing a 4-bit Magnitude Comparator Integrated Circuits in a production line. Comparatively, our result demonstrates that the proposed strategy outperforms the traditional block partitioning strategy with the mutant score of 100% to 90%, respectively, with the same number of test cases.

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“…58,720,256. Assuming each test case may consume 5 minutes to execute; results around 559 years to complete the exhaustive test of this 'view' tab [3]. This is similar for hardware products as well.…”

Section: Introductionmentioning

confidence: 99%

“…This is similar for hardware products as well. If a product has 20 on/off switches, to test all possible combination it may need 2 20 = 1,048,576 test cases, and consume 10 years by considering 5 minutes for each single test case [3]. Nowadays, research work in combinatorial testing aims to generate least possible test cases [4].…”

Section: Introductionmentioning

confidence: 99%

A Random search based effective algorithm for pairwise test data generation

Khatun

Rabbi

Yaakub

et al. 2011

International Conference on Electrical, Control and Computer Engineering 2011 (InECCE)

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Testing is a very important task to build error free software. As the resources and time to market is limited for a software product, it is impossible to perform exhaustive test i.e., to test all combinations of input data. To reduce the number of test cases in an acceptable level, it is preferable to use higher interaction level (t way, where t ≥ 2). Pairwise (2-way or t = 2) interaction can find most of the software faults. This paper proposes an effective random search based pairwise test data generation algorithm named R2Way to optimize the number of test cases. Java program has been used to test the performance of the algorithm. The algorithm is able to support both uniform and non-uniform values effectively with performance better than the existing algorithms/tools in terms of number of generated test cases and time consumption.

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Algebraic strategy to generate pairwise test set for prime number parameters and variables

Cited by 21 publications

References 7 publications

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

A Strategy for Automatic Quality Signing and Verification Processes for Hardware and Software Testing

A Random search based effective algorithm for pairwise test data generation

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