Browserbite: cross‐browser testing via image processing

Saar, T.; Dumas, Marlon; Kaljuve, Marti; Semenenko, Nataliia

doi:10.1002/spe.2387

Cited by 17 publications

(9 citation statements)

References 17 publications

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“…The number of articles in relation to each browser used in the experiments were: Mozilla Firefox (10 articles), Google Chrome (7 articles), Microsoft Internet Explorer (10 articles), and Apple Safari (4 articles). It is worth noting that in [11], the experiment was conducted with 15 distinct configurations of these browsers, by changing the OS and browser versions.…”

Section: Related Work and Discussionmentioning

confidence: 99%

“…Many of the reviewed studies relied on DOM-based approaches (such as [8] which reported the use DOM elements' attributes for detecting XBIs) or Computer-vision approaches (such as [5,[9][10][11] which developed image processing algorithms for detecting XBIs). However, there were also hybrid approaches, which combined DOM-based and Computer-vision approaches (such as [4,12] which used the DOM attributes alongside screenshot comparison for detecting XBIs).…”

Section: Xbi Detection Frameworkmentioning

confidence: 99%

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Towards Cross-browser Incompatibilities Detection: Asystematic Literature Review

Watanabe¹,

Paes²,

Silva³

2019

IJSEA

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The term Cross Browser Incompatibilities (XBI) stands for compatibility issues which can be observed while rendering a web application in different browsers, such as: Microsoft Edge, Mozilla Firefox, Google Chrome, among others. In order to detect Cross-Browser Incompatibilities-XBIs, Web developers rely on manual inspection of every web page of their applications, rendering them in various configuration environments (considering multiple OS platforms and browser versions). These manual inspections are error-prone and increase the cost of the Web engineering process. This paper has the goal of identifying techniques for automatic XBI detection, conducting a Systematic Literature Review (SLR) with focus on studies which report XBI automatic detection strategies. The selected studies were compared according to the different technological solutions they implemented and were used to elaborate a XBI detection framework which can organize and classify the state-of-the-art techniques and may be used to guide future research activities about this topic. Lab 2. These tools automatically generate screenshots of a web application as rendered by multiple browsers. However, even though they make the process of identifying XBIs easier, developers still have to rely on manual inspection of the screenshots. According to Choudhary et al., XBIs can be classified in two groups [1]: Layout Issues: caused by differences in how web browsers initially render a webpage; this type of XBI is immediately visible to the user, appearing as different positioning, size, visibility or general appearance of elements of a webpage; and

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Section: Related Work and Discussionmentioning

confidence: 99%

Section: Xbi Detection Frameworkmentioning

confidence: 99%

Towards Cross-browser Incompatibilities Detection: Asystematic Literature Review

Watanabe¹,

Paes²,

Silva³

2019

IJSEA

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show abstract

“…DIC applies the images, which measure deformation from micrometer to nanometer range. [15][16][17][18][19][20][21][22][23][24][25][26][27][28] If the image has spatial and high temporal, the result could be improved by DIC. 29,30 In previous studies, cell imaging was classified through machine learning (normal cell image, drug-treated cell image, diseased cell image, and cell movement).…”

Section: Methodsmentioning

confidence: 99%

Drug investigation tool: Identifying the effect of drug on cell image by using improved correlation

et al. 2020

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In the treatment of human cancer, the existing drug response identification and assessment methods have some issues, such as high cost, slow processing, and low accuracy. Responsive methods that easily and indirectly assess patient tissue response to drugs or small molecules offer promising options for improving drug safety, and they are able to identify the right treatment for specific patients. Digital image correlation is a good technique that detects a part of the image between two images. In this article, a new method of correlation called significant improvement of correlation (SIC) was proposed. Data were taken from the confocal microscopy of two‐photon excited fluorescence (TPEF) probe images taken every 5 min for short‐term monitoring (STM) and long‐term monitoring (LTM; 12, 24, and 48 hours). Cells were cultured, and a new compound TPEF was tested and injected the drug. The effect of drug on the TPEF compound was assessed through correlation. Experimental and simulation results showed that the proposed SIC was better than the conventional correlation method. Results also revealed that the first three images had 0.8% correlation in STM and LTM Image15 and Image10 had 0.49% correlation. The other images demonstrated minimum correlation. Thus, the effect of the newly made drug on the cell and image had a minimum correlation.

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“…Web page segmentation is used in various areas of software engineering. Saar et al [5] use segmentation to test cross-browser compatibility of web pages. Their approach is based on loading the same web page in two different browsers, followed by segmenting the rendered pages in the two different browsers, and finally comparing the pairs of segments to ensure both pages have been rendered in the same fashion in both browsers.…”

Section: Background and Motivating Examplementioning

confidence: 99%

Page Segmentation using Visual Adjacency Analysis

Bajammal¹,

Mesbah²

2021

Preprint

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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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Browserbite: cross‐browser testing via image processing

Cited by 17 publications

References 17 publications

Towards Cross-browser Incompatibilities Detection: Asystematic Literature Review

Towards Cross-browser Incompatibilities Detection: Asystematic Literature Review

Drug investigation tool: Identifying the effect of drug on cell image by using improved correlation

Page Segmentation using Visual Adjacency Analysis

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