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MRGS-ART: Metamorphic Relation and Group Selection Based on adaptive random testing

SOFTWARE testing VERIFICATION & RELIABILITY 2025年第1期35卷

作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Graham Zhou, Zhi Quan Univ Nottingham Ningbo China Sch Comp Sci Ningbo Zhejiang Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

Metamorphic testing (MT) is effective in detecting software failures;it detects failures by examining the metamorphic relations (MRs) among source test cases (STCs), follow-up test cases (FTCs) and their respective outputs. The STCs together with the corresponding FTCs, considered as a whole, are called metamorphic groups (MGs). MT performance relies heavily on the MRs and MGs. Previous studies have mainly focused on improving MT performance by identifying effective MRs, or through generation of MGs with high quality, but have somewhat neglected the selection of MRs and MGs from existing ones. In this paper, we address this issue by introducing a new metric for guiding the selection of effective MR-MG pairs from a new perspective: The MR-MG pair is chosen such that the MR makes the current MG as far away as possible from the executed MGs. We design an MR-MG pair selection algorithm, named metamorphic relation and group selection based on adaptive random testing (MRGS-ART), to implement our metric. The intuition behind MRGS-ART is that we attempt to improve MT performance by achieving an even distribution of STCs and FTCs in their corresponding input domains for all the MRs used. Experimental results indicate that MRGS-ART can enhance MT performance. We believe that this is the first comprehensive and systematic demonstration, from the perspective of both MRs and MGs, that making STCs and FTCs evenly distributed in their corresponding input domains can improve MT performance. Finally, by analysing the experimental results, we provide guidance on how to most effectively implement MRGS-ART.

关键词： adaptive random testing executed test case metamorphic group metamorphic relation metamorphic testing

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adaptive random testing: The ART of test case diversity

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JOURNAL OF SYSTEMS AND SOFTWARE 2010年第1期83卷 60-66页

作者： Chen, Tsong Yueh Kuo, Fei-Ching Merkel, Robert G. Tse, T. H. Swinburne Univ Technol Fac Informat & Commun Technol Hawthorn Vic 3122 Australia Univ Hong Kong Dept Comp Sci Pokfulam Hong Kong Peoples R China

random testing is not only a useful testing technique in itself, but also plays a core role in many other testing methods. Hence, any significant improvement to random testing has an impact throughout the software testing community. Recently, adaptive random testing (ART) was proposed as an effective alternative to random testing. This paper presents a synthesis of the most important research results related to ART. In the course of our research and through further reflection, we have realised how the techniques and concepts of ART can be applied in a much broader context, which we present here. We believe such ideas can be applied in a variety of areas of software testing, and even beyond software testing. Amongst these ideas, we particularly note the fundamental role of diversity in test case selection strategies. We hope this paper serves to provoke further discussions and investigations of these ideas. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Software testing random testing adaptive random testing adaptive random sequence Failure-based testing Failure pattern

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adaptive random testing based on distribution metrics

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JOURNAL OF SYSTEMS AND SOFTWARE 2009年第9期82卷 1419-1433页

作者： Chen, Tsong Yueh Kuo, Fei-Ching Liu, Huai Swinburne Univ Technol Fac Informat & Commun Technol Hawthorn Vic 3122 Australia

random testing (RT) is a fundamental software testing technique. adaptive random testing (ART), an enhancement of RT, generally uses fewer test cases than RT to detect the first failure. ART generates test cases in a random manner, together with additional test case selection criteria to enforce that the executed test cases are evenly spread over the input domain. Some studies have been conducted to measure how evenly an ART algorithm can spread its test cases with respect to some distribution metrics. These studies observed that there exists a correlation between the failure detection capability and the evenness of test case distribution. Inspired by this observation, we aim to study whether failure detection capability of ART can be enhanced by using distribution metrics as criteria for the test case selection process. Our simulations and empirical results show that the newly proposed algorithms not only improve the evenness of test case distribution, but also enhance the failure detection capability of ART. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Software testing random testing adaptive random testing Test case distribution Discrepancy Dispersion

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adaptive random testing through test profiles

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SOFTWARE-PRACTICE & EXPERIENCE 2011年第10期41卷 1131-1154页

作者： Liu, Huai Xie, Xiaodong Yang, Jing Lu, Yansheng Chen, Tsong Yueh Swinburne Univ Technol Ctr Software Anal & Testing Hawthorn Vic 3122 Australia Huazhong Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430074 Peoples R China

random testing (RT), which simply selects test cases at random from the whole input domain, has been widely applied to test software and assess the software reliability. However, it is controversial whether RT is an effective method to detect software failures. adaptive random testing (ART) is an enhancement of RT in terms of failure-detection effectiveness. Its basic intuition is to evenly spread random test cases all over the input domain. There are various notions to achieve the goal of even spread, and each notion can be implemented by different algorithms. For example, 'by exclusion' and 'by partitioning' are two different notions to evenly spread test cases. Restricted random testing (RRT) is a typical algorithm for the notion of 'by exclusion', whereas the notion of 'by partitioning' can be implemented by either the technique of bisection (ART-B) or the technique of random partitioning (ART-RP). In this paper, we propose a generic approach that can be used to implement different notions. In the new approach, test cases are simply selected based on test profiles that are in turn designed according to certain notions. In this study, we design several test profiles for the notions of 'by exclusion' and 'by partitioning', and then use these profiles to illustrate our new approach. Our experimental results show that compared with the original RRT, ART-B, and ART-RP algorithms, our new approach normally brings at least a higher failure-detection capability or a lower computational overhead. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： software quality software testing random testing adaptive random testing test profile

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adaptive random testing with CG constraint

Adaptive random testing with CG constraint

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28th Annual International Computer Software and Applications Conference

作者： Chan, FT Chan, KP Chen, TY Yiu, SM Univ Hong Kong Sch Profess & Continuing Educ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)0769522092

In this paper, we introduce a C. G. constraint on adaptive random testing (ART) for programs with numerical input. One rationale behind adaptive random testing is to have the test candidates to be as widespread over the input domain as possible. However, the computation may be quite expensive in some cases. The C. G. constraint is introduced to maintain the widespreadness while reducing the computation requirement in terms of number of distance measures. Three variations of C. G. constraints and their performance when compared with ART are discussed.

关键词： random testing adaptive random testing center of gravity constraint

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adaptive random testing by Bisection and Comprehensive Distance 9th

Adaptive Random Testing by Bisection and Comprehensive Dista...

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9th International Workshop on Structured Object-Oriented Formal Language (SOFL)+The Modeling, Simulation and Verification Language (MSVL) for Reliability and Security

作者： Mao, Chengying Quan, Mengting Chen, Zhilei Chen, Tsong Yueh Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Jiangxi Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3122 Australia

ISBN: (纸本)9783030414184;9783030414177

adaptive random testing (ART) has been proved to be effective in improving the failure detection ability of random testing. As a lightweight ART algorithm, the ART by bisection (ART-B) can realize test case generation in a linear order of time complexity, but its ability for finding failures is not so strong. In this study, the dynamic bisection is used to generate candidates randomly from the empty regions as much as possible. For each candidate, two types of distances are taken into account. Then, a comprehensive distance metric is defined to determine the next test case from the candidate set. For the nearest neighbor query in the proposed ART by Bisection and Comprehensive Distance (ART-BCD), the distance-ware forgetting is adopted to ensure its computational cost is still in the linear order. To validate the effectiveness of ART-BCD algorithm, both simulation experiments and empirical studies are performed for comparative analysis. The experimental results show that ART-BCD is better than or comparable to the ART-B and other typical algorithms, such as the fixed-size-candidate-set ART (FSCS-ART), in most cases, especially for the block failure pattern.

关键词： Software testing adaptive random testing Failure detection ability Dynamic bisection Comprehensive distance metric

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adaptive random testing with enlarged input domain

Adaptive random testing with enlarged input domain

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6th International Conference on Quality Software

作者： Mayer, Johannes Schneckenburger, Christoph Univ Ulm Dept Appl Informat Proc D-89069 Ulm Germany

ISBN: (纸本)0769527183

adaptive random testing (ART) subsumes a family of random testing techniques that are designed to be more effective than pure random testing. These methods spread test cases more evenly within the input domain than a uniform distribution does. In the present paper, it is investigated why standard ART methods are less effective for higher failure rates. Therefore, the spatial distribution of the test cases generated by these methods is analyzed-also in higher dimensions-with a new approach. Based on the results of the analysis, improved algorithms are proposed that are equally effective for all failure rates as an empirical study reveals.

关键词： random testing adaptive random testing test data selection

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adaptive random testing By Static Partitioning 10

Adaptive Random Testing By Static Partitioning

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International 10th Workshop on Automation of Software Test

作者： Sabor, Korosh Koochekian Thiel, Stuart Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

ISBN: (纸本)9781467370226

Despite the importance of the random testing approach, random testing is not used in isolation, but plays a core role in many testing methods. On the basis that evenly distributed test cases are more likely to reveal non-point pattern failure regions, various adaptive random testing ( ART) methods have been proposed. Many of these methods use a variety of distance calculations, with corresponding computational overhead;newly proposed methods like ART by bisection, random partitioning or dynamic iterative partitioning try to decrease computational overhead while maintaining the performance. In this article we have proposed a new ART method that has similar performance to existing ART methods while having less computational overhead.

关键词： random testing adaptive random testing static static partitioning

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adaptive random testing by localization

Adaptive random testing by localization

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11th Asia-Pacific Software Engineering Conference (APSEC 2004)

作者： Chen, TY Huang, DH Swinburne Univ Technol Sch Informat Technol Hawthorn Vic 3122 Australia

ISBN: (纸本)0769522459

Based on the intuition that widely spread test cases should have greater chance of hitting the non-point failure-causing regions, several adaptive random testing (ART) methods have recently been proposed to improve traditional random testing (RT). However, most of the ART methods require additional distance computations to ensure an even spread of test cases. In this paper, we introduce the concept of localization that can be integrated with some ART methods to reduce the distance computation overheads. By localization, test cases would be selected from part of the input domain instead of the whole input domain, and distance computation would be done for some instead of all previous test cases. Our empirical results show that the fault detecting capability of our method is comparable to those of other ART methods.

关键词： random testing adaptive random testing localization

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adaptive random testing for XSS Vulnerability 26

Adaptive Random Testing for XSS Vulnerability

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26th Asia-Pacific Software Engineering Conference (APSEC)

作者： Lv, Chengcheng Zhang, Long Zeng, Fanping Zhang, Jian Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728146485

XSS is one of the common vulnerabilities in web applications. Many black-box testing tools may collect a large number of payloads and traverse them to find a payload that can be successfully injected, but they are not very efficient. Previous research has paid less attention to how to improve the efficiency of black-box testing to detect XSS vulnerability. To improve the efficiency of testing, we develop an XSS testing tool. It collects 6128 payloads and uses a headless browser to detect XSS vulnerability. The tool can discover XSS vulnerability quickly with adaptive random testing method. We conduct an experiment using 3 extensively adopted open source vulnerable benchmarks and 2 actual websites to evaluate the adaptive random testing method. The experimental results indicate that the adaptive random testing method can effectively improve the fuzzing method by more than 27.1% in reducing the number of attempts before accomplishing a successful injection.

关键词： XSS Vulnerability adaptive random testing Fuzzing

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