En beskrivning av manuellt test : Svagheter och styrkor med och utan stöd avett testverktyg

Test is an area in system development. Test can be performed manually or automated. Test activities can be supported by Word documents and Excel sheets for documenting and executing test cases and as well for follow up, but there are also new test tools designed to support and facilitate the testing process and the activities of the test. This study has described manual test and identified strengths and weaknesses of manual testing with a testing tool called Microsoft Test Manager (MTM) and of manual testing using test cases and test log templates developed by the testers at Sogeti. The result that emerged from the problem and strength analysis and the analysis of literature studies and firsthand experiences (in terms of creating, documenting and executing test cases) addresses the issue of the following weaknesses and strengths. Strengths of the test tool is that it contains needed functionality all in one place and it is available when needed without having to open up other programs which saves many steps of activity. Strengths with test without the support of test tools is mainly that it is easy to learn and gives a good overview, easy to format text as desired and flexible to changes during execution of a test case. Weaknesses in test with the support of test tools include that it is difficult to get a good overview of the entire test case, that it is not possible to format the text in the test steps. It is as well not possible to modify the test steps during execution. It is also difficult to use some of the test design techniques of TMap, for example a checklist, when using the test tool MTM. Weaknesses with test without the support of the testing tool MTM is that the tester gets many more steps of activities to do compared to doing the same activities with the support of the testing tool MTM. There is more to remember because the documents the tester use are not directly linked. Altogether the strengths of the test tool stands out when it comes to supporting the testing process.

Hand Gesture Detection & Recognition System

The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.