Web Canvas Testing Through Visual Inference

Bajammal, Mohammad; Mesbah, Ali

doi:10.1109/icst.2018.00028

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…Bajammal and Mesbah [6] propose an approach for automated visual testing of the <canvas>, and report high accuracy. However, their approach is evaluated by injecting only a single type of <canvas> bug.…”

Section: Related Workmentioning

confidence: 99%

A Taxonomy of HTML5 Canvas Bugs

Macklon¹,

Viggiato²,

Bezemer³

et al. 2022

Preprint

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The HTML5

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Section: Related Workmentioning

confidence: 99%

A Taxonomy of HTML5 Canvas Bugs

Macklon¹,

Viggiato²,

Bezemer³

et al. 2022

Preprint

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The HTML5

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“…Choudhary et al [21] propose an approach that detects cross-browser compatibility by examining visual differences between the same page running in multiple browsers. Bajammal et al [22] propose an approach to analyze and test web canvas element through visual inference of the state of the canvas and its objects, and allowing canvas elements to be testable using common DOM testing approaches. Stocco et al [23] employ computer vision techniques for visual-based web test repair and migrating DOM-based tests to visual tests.…”

Section: Related Workmentioning

confidence: 99%

Page Segmentation using Visual Adjacency Analysis

Bajammal¹,

Mesbah²

2021

Preprint

Self Cite

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Page segmentation is a web page analysis process that divides a page into cohesive segments, such as sidebars, headers, and footers. Current page segmentation approaches use either the DOM, textual content, or rendering style information of the page. However, these approaches have a number of drawbacks, such as a large number of parameters and rigid assumptions about the page, which negatively impact their segmentation accuracy. We propose a novel page segmentation approach based on visual analysis of localized adjacency regions. It combines DOM attributes and visual analysis to build features of a given page and guide an unsupervised clustering. We evaluate our approach, implemented in a tool called Cortex, on 35 real-world web pages, and examine the effectiveness and efficiency of segmentation. The results show that, compared with state-ofthe-art, Cortex achieves an average of 156% increase in precision and 249% improvement in F-measure.

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“…Several techniques [31‐37] are designed to detect and Cross‐Platform Incompatibilities and Cross‐Browser Issues in web pages by analysing HTML, CSS and Javascript. Yet other techniques, such as ReDeCheck [38‐42], WebSee [11‐13,43‐45], VFDetector [46], CANVASURE [47], Ply [48], Fighting Layout Bugs [49], Sikuli [50], and techniques based on computer vision [51] and CSS/Javascript analysis [52], detect certain types of presentation failures in web pages. There also exists a group of parallel techniques [53‐55] focusing on the detection and reporting of GUI violations in mobile apps.…”

Section: Related Workmentioning

confidence: 99%

Effective automated repair of internationalization presentation failures in web applications using style similarity clustering and search‐based techniques

Mahajan

Alameer

McMinn

et al. 2020

Software Testing Verif & Rel

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Summary Companies often employ (i18n) frameworks to provide translated text and localized media content on their websites in order to effectively communicate with a global audience. However, the varying lengths of text from different languages can cause undesired distortions in the layout of a web page. Such distortions, called Internationalization Presentation Failures (IPFs), can negatively affect the aesthetics or usability of the website. Most of the existing automated techniques developed for assisting repair of IPFs either produce fixes that are likely to significantly reduce the legibility and attractiveness of the pages or are limited to only detecting IPFs, with the actual repair itself remaining a labour intensive manual task. To address this problem, we propose a search‐based technique for automatically repairing IPFs in web applications, while ensuring a legible and attractive page. The empirical evaluation of our approach reported that our approach was able to successfully resolve 94% of the detected IPFs for 46 real‐world web pages. In a user study, participants rated the visual quality of our fixes significantly higher than the unfixed versions and also considered the repairs generated by our approach to be notably more legible and visually appealing than the repairs generated by existing techniques.

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Web Canvas Testing Through Visual Inference

Cited by 15 publications

References 20 publications

A Taxonomy of HTML5 Canvas Bugs

A Taxonomy of HTML5 Canvas Bugs

Page Segmentation using Visual Adjacency Analysis

Effective automated repair of internationalization presentation failures in web applications using style similarity clustering and search‐based techniques

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