Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/644609
Title: Automatic GUI test by using SIFT matching
Authors: Xiaoxin Fang;Bin Sheng;Ping Li;Dan Wu;Enhua Wu
subject: SIFT|image recognition|random fern|GUI test
Year: 2016
Publisher: IEEE
Abstract: In software development process, the last step is usually the Graphic User Interface(GUI) test, which is part of the final user experience (UE) test. Traditionally, there exist some GUI test tools in the market, such as Abbot Java GUI Test Framework and Pounder, in which testers pre-configure in the script all desired actions and instructions for the computer, nonetheless requiring too much of invariance of GUI environment; and they require reconfiguration in case of GUI changes, therefore still to be done mostly manually and hard for non-programmer testers to. Consequently, we proposed GUI tests by image recognition to automate the last process; we managed to innovate upon current algorithms such as SIFT and Random Fern, from which we develop the new algorithm scheme retrieving most efficient feature and dispelling inefficient part of each algorithm. Computers then apply the algorithm, to search for target patterns themselves and take subsequent actions such as manual mouse, keyboard and screen I/O automatically to test the GUI without any manual instructions. Test results showed that the proposed approach can accelerate GUI test largely compared to current benchmarks.
Description: 
URI: http://localhost/handle/Hannan/177244
http://localhost/handle/Hannan/644609
ISSN: 1673-5447
volume: 13
issue: 9
Appears in Collections:2016

Files in This Item:
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7582314.pdf1.28 MBAdobe PDFThumbnail
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Title: Automatic GUI test by using SIFT matching
Authors: Xiaoxin Fang;Bin Sheng;Ping Li;Dan Wu;Enhua Wu
subject: SIFT|image recognition|random fern|GUI test
Year: 2016
Publisher: IEEE
Abstract: In software development process, the last step is usually the Graphic User Interface(GUI) test, which is part of the final user experience (UE) test. Traditionally, there exist some GUI test tools in the market, such as Abbot Java GUI Test Framework and Pounder, in which testers pre-configure in the script all desired actions and instructions for the computer, nonetheless requiring too much of invariance of GUI environment; and they require reconfiguration in case of GUI changes, therefore still to be done mostly manually and hard for non-programmer testers to. Consequently, we proposed GUI tests by image recognition to automate the last process; we managed to innovate upon current algorithms such as SIFT and Random Fern, from which we develop the new algorithm scheme retrieving most efficient feature and dispelling inefficient part of each algorithm. Computers then apply the algorithm, to search for target patterns themselves and take subsequent actions such as manual mouse, keyboard and screen I/O automatically to test the GUI without any manual instructions. Test results showed that the proposed approach can accelerate GUI test largely compared to current benchmarks.
Description: 
URI: http://localhost/handle/Hannan/177244
http://localhost/handle/Hannan/644609
ISSN: 1673-5447
volume: 13
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7582314.pdf1.28 MBAdobe PDFThumbnail
Preview File
Title: Automatic GUI test by using SIFT matching
Authors: Xiaoxin Fang;Bin Sheng;Ping Li;Dan Wu;Enhua Wu
subject: SIFT|image recognition|random fern|GUI test
Year: 2016
Publisher: IEEE
Abstract: In software development process, the last step is usually the Graphic User Interface(GUI) test, which is part of the final user experience (UE) test. Traditionally, there exist some GUI test tools in the market, such as Abbot Java GUI Test Framework and Pounder, in which testers pre-configure in the script all desired actions and instructions for the computer, nonetheless requiring too much of invariance of GUI environment; and they require reconfiguration in case of GUI changes, therefore still to be done mostly manually and hard for non-programmer testers to. Consequently, we proposed GUI tests by image recognition to automate the last process; we managed to innovate upon current algorithms such as SIFT and Random Fern, from which we develop the new algorithm scheme retrieving most efficient feature and dispelling inefficient part of each algorithm. Computers then apply the algorithm, to search for target patterns themselves and take subsequent actions such as manual mouse, keyboard and screen I/O automatically to test the GUI without any manual instructions. Test results showed that the proposed approach can accelerate GUI test largely compared to current benchmarks.
Description: 
URI: http://localhost/handle/Hannan/177244
http://localhost/handle/Hannan/644609
ISSN: 1673-5447
volume: 13
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7582314.pdf1.28 MBAdobe PDFThumbnail
Preview File