Random testing revisited

Loo, PS; Tsai, W.

doi:10.1016/0950-5849(88)90037-7

Cited by 54 publications

(14 citation statements)

References 4 publications

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“…Recently, a number of novel strategies termed Adaptive Random Testing (ART) have been demonstrated to significantly increase the effectiveness of random testing [Chen et al 2007a[Chen et al , 2007b[Chen et al , 2003[Chen et al , 2004a[Chen et al , 2004b[Chen et al , 2001. Benefits of random testing [Agrawal 1978;Duran and Ntafos 1984;Loo and Tsai 1988;Ntafos 1998;Schneck 1979], and by extension ART, include the low cost associated with the random generation of test cases, and the ease of automation associated with the technique. ART methods are centered on the assumption that failure-triggering inputs occur within clusters in the input domain [Chan et al 1996;Ammann and Knight 1988;Finelli 1991;Chan et al 2004].…”

Section: Adaptive Random Testingmentioning

confidence: 99%

Automated cookie collection testing

Tappenden

Miller

2014

ACM Trans. Softw. Eng. Methodol.

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Cookies are used by over 80% of Web applications utilizing dynamic Web application frameworks. Applications deploying cookies must be rigorously verified to ensure that the application is robust and secure. Given the intense time-to-market pressures faced by modern Web applications, testing strategies that are low cost and automatable are required. Automated Cookie Collection Testing (CCT) is presented, and is empirically demonstrated to be a low-cost and highly effective automated testing solution for modern Web applications. Automatable test oracles and evaluation metrics specifically designed for Web applications are presented, and are shown to be significant diagnostic tests. Automated CCT is shown to detect faults within five real-world Web applications. A case study of over 580 test results for a single application is presented demonstrating that automated CCT is an effective testing strategy. Moreover, CCT is found to detect security bugs in a Web application released into full production.

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Section: Adaptive Random Testingmentioning

confidence: 99%

Automated cookie collection testing

Tappenden

Miller

2014

ACM Trans. Softw. Eng. Methodol.

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“…Lots of research has been conducted in this field and diverse methods to improve failure detection capability of test cases have been proposed ( [2,3,4,5,6]). A standard approach for automatic selection of test cases is random testing [7,8]. The intuition behind random testing is to select test cases randomly until a stopping condition such as detecting a failure, executing predefined number of test cases or ending of a time limitation is met.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Random Testing by Static Partitioning

Sabor¹,

Thiel²

2015

2015 IEEE/ACM 10th International Workshop on Automation of Software Test

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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.

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“…In Software Testing, there has been much debate over the use of Random Testing (RT) as an effective method [6], [7], [12], [14]. When testing software, a test case is a combination of inputs to the software which represent a single use of the software; Random Testing refers to the selection of test cases at random from the input domain.…”

Section: Introductionmentioning

confidence: 99%

“…When testing software, a test case is a combination of inputs to the software which represent a single use of the software; Random Testing refers to the selection of test cases at random from the input domain. There are many reasons why Random Testing remains a popular choice: in addition to its simplicity, and the efficiency of test case generation [7], reliability estimates and statistical analyses are also easily performed. In fact, many real-life applications do make use of Random Testing [10], [11], [13], [15].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Adaptive Random Testing

Chan

Chen

Towey

2006

2006 Sixth International Conference on Quality Software (QSIC'06)

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Adaptive Random Testing (ART) methods are Software Testing methods which are based on Random Testing, but which use additional mechanisms to ensure more even and widespread distributions of test cases over an input domain. Restricted Random Testing (RRT) is a version of ART which uses exclusion regions and restricts test case generation to outside of these regions. RRT has been found to perform very well, but its use of strict exclusion regions (from within which test cases cannot be generated) has prompted an investigation into the possibility of modifying the RRT method such that all portions of the Input Domain remain available for test case generation throughout the duration of the algorithm. In this paper, we present a probabilistic approach, Probabilistic ART (PART), and explain two different implementations. Preliminary empirical data supporting the methods is also examined.

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Random testing revisited

Cited by 54 publications

References 4 publications

Automated cookie collection testing

Automated cookie collection testing

Adaptive Random Testing by Static Partitioning

Probabilistic Adaptive Random Testing

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