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تسجيل مستخدم جديدSWFC-ART: A Cost-effective Approach for Fixed-Size-Candidate-Set Adaptive Random Testing through Small World Graphs
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Adaptive random testing (ART) improves the failure-detection effectiveness of random testing by leveraging properties of the clustering of failure-causing inputs of most faulty programs: ART uses a sampling mechanism that evenly spreads test cases within a softwares input domain. The widely-used Fixed-Sized-Candidate-Set ART (FSCS-ART) sampling strategy faces a quadratic time cost, which worsens as the dimensionality of the software input domain increases. In this paper, we propose an approach based on small world graphs that can enhance the computational efficiency of FSCS-ART: SWFC-ART. To efficiently perform nearest neighbor queries for candidate test cases, SWFC-ART incrementally constructs a hierarchical navigable small world graph for previously executed, non-failure-causing test cases. Moreover, SWFC-ART has shown consistency in programs with high dimensional input domains. Our simulation and empirical studies show that SWFC-ART reduces the computational overhead of FSCS-ART from quadratic to log-linear order while maintaining the failure-detection effectiveness of FSCS-ART, and remaining consistent in high dimensional input domains. We recommend using SWFC-ART in practical software testing scenarios, where real-life programs often have high dimensional input domains and low failure rates.
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As an enhanced version of Random Testing (RT), Adaptive Random Testing (ART) aims to improve the failure detection effectiveness of RT by distributing the test cases more evenly in the input domain. Many ART algorithms have been proposed based on dif
ferent criteria. Among them, the Fixed-Size-Candidate-Set ART (FSCS-ART) is one of the most effective and classical algorithms. FSCS-ART ensures high failure detection effectiveness using selecting the candidate test case which farthest from the previously executed test cases as the next test case. Although FSCS-ART has good failure detection effectiveness, it also has some drawbacks, such as computational overhead problem. In this paper, we propose an enhanced version of FSCS-ART, namely Vantage Point Partitioning Strategy based ART (VPPS-ART). VPPS-ART attempts to solve the computational overhead problem of FSCS-ART using vantage point partitioning strategy and ensures the failure detection effectiveness of FSCS-ART. VPPS-ART achieves the partitioning of the input domain space by using a Vantage Point tree (VP-tree) and finds the nearest executed test cases of a candidate test case in the partitioned sub-domains, which reduces the time overhead significantly compared to the entire input domain search computation. Besides, to match the dynamic insertion process of FSCS-ART, we modify the structure of the traditional VP-tree to support dynamic data. The simulation results present that VPPS-ART has a great lower time overhead compared to FSCS-ART, and also guarantees similar or better failure detection effectiveness than FSCS-ART. The VPPS-ART also shows strength in comparison with the KDFC-ART algorithms, a series of enhanced ART algorithms based on KD-tree. Our empirical studies also reveal that VPPS-ART is more cost-effective compared to FSCS-ART and KDFC-ART.
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