Fujitsu Releases Testing Platform for Mobile Apps
Fujitsu has developed a testing platform technology for mobile applications that can generate a uite of test-cases for medium size mobile applications.
This platform can reduce the time for test case generation from several days to a few hours, greatly speeding up the development and deployment time for mobile applications.
Mobile applications are typically developed in small teams with short development cycles and limited testing resources. Additionally, most test cases for mobile applications are written manually and can take from several days to a week to develop. Fujitsu Laboratories of America has developed an automated testing platform which generates a high quality suite of test cases for testing a given mobile application in a few hours.
The platform automatically generates a model of the user-interface (UI) of a mobile application and then generates a high-coverage suite of test cases for the application using this UI model. The model generator uses static and dynamic program analysis techniques to extract the behavior of the mobile application and a back-end machine learning engine to infer a model of the application based on this extracted behavior. The test generator then uses the model extracted by the model generator to generate a suite of test-cases to test the application.
Mobile applications are typically developed in small teams with short development cycles and limited testing resources. Additionally, most test cases for mobile applications are written manually and can take from several days to a week to develop. Fujitsu Laboratories of America has developed an automated testing platform which generates a high quality suite of test cases for testing a given mobile application in a few hours.
The platform automatically generates a model of the user-interface (UI) of a mobile application and then generates a high-coverage suite of test cases for the application using this UI model. The model generator uses static and dynamic program analysis techniques to extract the behavior of the mobile application and a back-end machine learning engine to infer a model of the application based on this extracted behavior. The test generator then uses the model extracted by the model generator to generate a suite of test-cases to test the application.