Automatic Detection of Potential Layout Faults Following Changes to Responsive Web Pages (N)

2015 ◽  
Author(s):  
Thomas A. Walsh ◽  
Phil McMinn ◽  
Gregory M. Kapfhammer
Keyword(s):  
Automatic Detection ◽  
Web Pages

scholarly journals Detecção Automática de Incompatibilidades Cross-Browser utilizando Redes Neurais Artificiais

2016 ◽  
Vol 2 (2) ◽  
pp. 55
Author(s):  
Fagner Christian Paes ◽  
Willian Massami Watanabe
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Automatic Detection ◽  
False Positives ◽  
Web Pages ◽  
Inspection Process ◽  
Cascading Style Sheets ◽  
Mozilla Firefox ◽  
Web Developers ◽  
The Web

Cross-Browser Incompatibilities (XBIs) represent inconsistencies in Web Application when introduced in different browsers. The growing number of implementation of browsers (Internet Explorer, Microsoft Edge, Mozilla Firefox, Google Chrome) and the constant evolution of the specifications of Web technologies provided differences in the way that the browsers behave and render the web pages. The web applications must behave consistently among browsers. Therefore, the web developers should overcome the differences that happen during the rendering in different environments by detecting and avoiding XBIs during the development process. Many web developers depend on manual inspection of web pages in several environments to detect the XBIs, independently of the cost and time that the manual tests represent to the process of development. The tools for the automatic detection of the XBIs accelerate the inspection process in the web pages, but the current tools have little precision, and their evaluations report a large percentage of false positives. This search aims to evaluate the use of Artificial Neural Networks for reducing the numbers of false positives in the automatic detection of the XBIs through the CSS (Cascading Style Sheets) and the relative comparison of the element in the web page.

scholarly journals Automatic Detection of Illegitimate Websites with Mutual Clustering

2016 ◽  
Vol 6 (3) ◽  
pp. 995
Author(s):  
Kanaka Durga ◽  
V Rama Krishna
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Automatic Detection ◽  
Threshold Value ◽  
Research Community ◽  
Web Pages ◽  
Web Content ◽  
Web Contents ◽  
Similar Content ◽  
Web Clustering

In the websites the contents will be are similarity when we compared with other search engines. So to check the similar content in the websites and its web contents we created a overhead to the search engine which will severely effect its performance & quality. So to detect the silmilar or same content or web documenattion some techniques are implemented by web crawling research community. So it is one of major factor for the search engines to provide some applicatory data to users in the first page itself. So to avoid such issues we proposed a methodlogy called Automatic Detection of illegitimate websites with Mutual Clustering (ADIWMC) paper we are presenting a peculiar and efficacious path for the detection of similarities in the web pages in web clustering. Detection of same and similar web pages and web content will be done by storing the crawled web pages into depository. Initially the adwords will be extracted from the crawled pages and similarity checking will be done between the two pages based in the usage of adwords. So a threshold value is set for this, if the similarity checking percentage is greater than the threshold then similarity content is reduced and improves the depositary and improves the search engine quality. In the sections of existing analysis and the proposed analysis we are clearly exploring how it works.

Automatic Detection of Illegitimate Websites with Mutual Clustering

2016 ◽  
Vol 6 (3) ◽  
pp. 995
Author(s):  
Kanaka Durga ◽  
V Rama Krishna
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Automatic Detection ◽  
Threshold Value ◽  
Research Community ◽  
Web Pages ◽  
Web Content ◽  
Web Contents ◽  
Similar Content ◽  
Web Clustering

In the websites the contents will be are similarity when we compared with other search engines. So to check the similar content in the websites and its web contents we created a overhead to the search engine which will severely effect its performance & quality. So to detect the silmilar or same content or web documenattion some techniques are implemented by web crawling research community. So it is one of major factor for the search engines to provide some applicatory data to users in the first page itself. So to avoid such issues we proposed a methodlogy called Automatic Detection of illegitimate websites with Mutual Clustering (ADIWMC) paper we are presenting a peculiar and efficacious path for the detection of similarities in the web pages in web clustering. Detection of same and similar web pages and web content will be done by storing the crawled web pages into depository. Initially the adwords will be extracted from the crawled pages and similarity checking will be done between the two pages based in the usage of adwords. So a threshold value is set for this, if the similarity checking percentage is greater than the threshold then similarity content is reduced and improves the depositary and improves the search engine quality. In the sections of existing analysis and the proposed analysis we are clearly exploring how it works.

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