Automated cookie collection testing

Tappenden, Andrew F.; Miller, James

doi:10.1145/2559936

Cited by 12 publications

(8 citation statements)

References 75 publications

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“…Ciupa et al (2008) have proposed a specific distance metric for object-oriented software. Tappenden and Miller (2013) have proposed a specific distance metric for cookies collection testing of web applications. It is understood that there are currently investigations into the application of ART in input domains involving strings, trees, finite state machines, etc.…”

Section: Various Art Algorithmsmentioning

confidence: 99%

“…They have observed that ART was the best performer, but "there is a 9% probability of wrongly claiming that ART is better than RT if that is actually not the case" (Iqbal et al, 2011). Hemmati et al (2010Hemmati et al ( , 2011 have used ART to test a safety monitoring component of a safety-critical control system written in C++ and a core component of a videoconference system written in C. Tappenden and Miller (2013) have used Evolutionary ART in their cookie collection testing of six open-source web applications. Faults were detected in five out of the six web applications.…”

Section: Applications Of Art and Toolsmentioning

confidence: 99%

See 1 more Smart Citation

An orchestrated survey of methodologies for automated software test case generation

Anand

Burke

Chen

et al. 2013

Journal of Systems and Software

560

374

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Section: Various Art Algorithmsmentioning

confidence: 99%

Section: Applications Of Art and Toolsmentioning

confidence: 99%

An orchestrated survey of methodologies for automated software test case generation

Anand

Burke

Chen

et al. 2013

Journal of Systems and Software

560

374

View full text Add to dashboard Cite

“…Cliff's δ was used in El-Attar (2014), El-Attar et al (2012) and Tappenden and Miller (2014). The probability of superiority metricP (referred to asÂ 12 ) was used in Madeyski et al (2014) and Madeyski et al (2012).…”

Section: Arguments For the Use Of Robust Statisticsmentioning

confidence: 99%

Robust Statistical Methods for Empirical Software Engineering

et al. 2016

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There have been many changes in statistical theory in the past 30 years, including increased evidence that non-robust methods may fail to detect important results. The statistical advice available to software engineering researchers needs to be updated to address these issues. This paper aims both to explain the new results in the area of robust analysis methods and to provide a large-scale worked example of the new methods. We summarise the results of analyses of the Type 1 error efficiency and power of standard parametric and non-parametric statistical tests when applied to non-normal data sets. We identify parametric and non-parametric methods that are robust to non-normality. We present an analysis of a large-scale software engineering experiment to illustrate their use. We illustrate the use of kernel density plots, and parametric and non-parametric methods using four different software engineering data sets. We explain why the methods are necessary and the rationale for selecting a specific analysis. We suggest using kernel density plots rather than box plots to visualise data distributions. For parametric analysis, we recommend trimmed means, which can support reliable tests of the differences between the central location of two or

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“…For non-numeric inputs, the measurement should take account of the functionali- ties or characteristics of the SUT. Web applications, for example, often involve cookies, and Tappenden and Miller [31] proposed a cookie-based distance metric to evenly spread test inputs for web applications.…”

Section: B Adaptive Random Testingmentioning

confidence: 99%

A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

et al. 2017

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Abstract-Random testing (RT) has been identified as one of the most popular testing techniques, due to its simplicity and ease of automation. Adaptive random testing (ART) has been proposed as an enhancement to RT, improving its fault-detection effectiveness by evenly spreading random test inputs across the input domain. To achieve the even spreading, ART makes use of distance measurements between consecutive inputs. However, due to the nature of object-oriented software (OOS), its distance measurement can be particularly challenging: Each input may involve multiple classes, and interaction of objects through method invocations. Two previous studies have reported on how to test OOS at a single-class level using ART. In this study, we propose a new similarity metric to enable multiclass level testing using ART. When generating test inputs (for multiple classes, a series of objects, and a sequence of method invocations), we use the similarity metric to calculate the distance between two series of objects, and between two sequences of method invocations. We integrate this metric with ART and apply it to a set of open-source OO programs, with the empirical results showing that our approach outperforms other RT and ART approaches in OOS testing.

show abstract

Automated cookie collection testing

Cited by 12 publications

References 75 publications

An orchestrated survey of methodologies for automated software test case generation

An orchestrated survey of methodologies for automated software test case generation

Robust Statistical Methods for Empirical Software Engineering

A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

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