Configuring effective navigation models and abstract test cases for web applications by analysing user behaviour

Sprenkle, Sara; Pollock, Lori; Simko, Lucy

doi:10.1002/stvr.1496

Cited by 19 publications

(21 citation statements)

References 29 publications

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“…This modification has been made since we discovered after a few runs that the algorithm produced a lot of traces with repeated actions, which is of little interest for product prioritization. Repeti- [54] experimentally demonstrate that increasing the n in n-gram generation of the usage model does increase the size of the generated model in a nonlinear way (as long as n is between 2 and 10). Increasing the n value in our case would just result in an unnecessary increase in the model complexity.…”

Section: Resultsmentioning

confidence: 97%

“…the usage model only represents valid behaviour of the SPL) depends on the inference method used and the number and size of logs used as entry of this method. Methods producing correct usage models may be interesting but are more computationally expensive and may not scale for large logs [54]. Less accurate methods (e.g.…”

Section: Statistical Prioritization In Spl Testingmentioning

confidence: 99%

“…We derived the usage model 3 , from an anonymized Apache access log containing 12.689.030 HTTP requests provided by the IT support team of the university (5, 26 Go), using n-gram (contiguous sequence of n elements) inference technique [28,50,54]. We were able to process the entirety of our log database in approximately two hours, ending up in a usage model formed of 96 states and 2149 transitions.…”

Section: Usage Model Inferencementioning

confidence: 99%

“…Our will is to apply ideas stemming from statistical testing [54] and adapt them in an SPL context. For example, combining structural criteria with statistical testing has been discussed by Gouraud et al [29] and Thévenod-Fosse and Waeselynck [55].…”

Section: Related Workmentioning

confidence: 99%

“…These extraction methods are agnostic regarding SPL features, and the resulting usage model will represents the behaviour of one or more products of the product line. One may merge logs of different products of the same product line and use model inference techniques to build the usage model [54,59]. The correctness of the usage model (i.e.…”

Section: Statistical Prioritization In Spl Testingmentioning

confidence: 99%

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Statistical prioritization for software product line testing: an experience report

et al. 2015

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Software product lines (SPLs) are families of software systems sharing common assets and exhibiting variabilities specific to each product member of the family. Commonalities and variabilities are often represented as features organized in a feature model. Due to combinatorial explosion of the number of products induced by possible features combinations, exhaustive testing of SPLs is intractable. Therefore, sampling and prioritization techniques have been proposed to generate sorted lists of products based on coverage criteria or weights assigned to features. Solely based Communicated by Prof. on the feature model, these techniques do not take into account behavioural usage of such products as a source of prioritization. In this paper, we assess the feasibility of integrating usage models into the testing process to derive statistical testing approaches for SPLs. Usage models are given as Markov chains, enabling prioritization of probable/rare behaviours. We used featured transition systems, compactly modelling variability and behaviour for SPLs, to determine which products are realizing prioritized behaviours. Statistical prioritization can achieve a significant reduction in the state space, and modelling efforts can be rewarded by better automation. In particular, we used MaTeLo, a statistical test cases generation suite developed at ALL4TEC. We assess feasibility criteria on two systems: Claroline, a configurable course management system, and Sferion™, an embedded system providing helicopter landing assistance.

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Section: Resultsmentioning

confidence: 97%

Section: Statistical Prioritization In Spl Testingmentioning

confidence: 99%

Section: Usage Model Inferencementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Statistical Prioritization In Spl Testingmentioning

confidence: 99%

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Statistical prioritization for software product line testing: an experience report

et al. 2015

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Search‐based crash reproduction using behavioural model seeding

Derakhshanfar

Devroey

Perrouin

et al. 2020

Software Testing Verif & Rel

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Summary Search‐based crash reproduction approaches assist developers during debugging by generating a test case, which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the search process to produce realistic sequences of method calls, which create the required objects. In this study, we introduce behavioural model seeding: a new seeding method that learns class usages from both the system under test and existing test cases. Learned usages are then synthesized in a behavioural model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioural model seeding, we evaluate it against test seeding (the state‐of‐the‐art technique for seeding realistic objects) and no seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard‐to‐reproduce crashes stemming from six open‐source projects. Our results indicate that behavioural model seeding outperforms both test seeding and no seeding by a minimum of 6% without any notable negative impact on efficiency.

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Automatically identifying potential regressions in the layout of responsive web pages

Walsh

Kapfhammer

McMinn

2020

Software Testing Verif & Rel

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SummaryProviding a good user experience on the ever‐increasing number and variety of devices being used to browse the web is a difficult, yet critical, task. With responsive web design, front‐end web developers design web pages so that they dynamically resize and rearrange content to best fit the dimensions of a device's screen. However, when making code modifications to a responsive page, developers can easily introduce regressions from the correct layout that have detrimental effects at unpredictable screen sizes. For instance, the source code change that a developer makes to improve the layout at one screen size may obscure a page's content at other sizes. Current approaches to testing are often insufficient because they rely on limited tools and error‐prone manual inspections of web pages. As such, many unintended regressions in web page layout often go undetected and ultimately manifest in production websites. To address the challenge of detecting regressions in responsive web pages, this paper presents an automated approach that extracts the responsive layout of two versions of a page and compares them, alerting developers to the differences in layout that they may wish to investigate further. We implemented the approach and empirically evaluated it on 15 real‐world responsive web pages. Leveraging code mutations that a tool automatically injected into the pages as a systematic simulation of developer changes, the experiments show that the approach was highly effective. When compared with manual and automated baseline testing techniques, it detected 12.5% and 18.75% more injected changes, respectively. Along with identifying the best parameters for the method that extracts the responsive layout, the experiments show that the approach surpasses the baselines across changes that vary in their impact, but works particularly well for subtle, hard‐to‐detect mutants, showing the benefits of automatically identifying regressions in web page layout.

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Configuring effective navigation models and abstract test cases for web applications by analysing user behaviour

Cited by 19 publications

References 29 publications

Statistical prioritization for software product line testing: an experience report

Statistical prioritization for software product line testing: an experience report

Search‐based crash reproduction using behavioural model seeding

Automatically identifying potential regressions in the layout of responsive web pages

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