Automated Software Testing Cases Generation Framework to Ensure the Efficiency of the Gesture Recognition Systems

Software testing for a system is necessary not only to identify whether its attainment of required software qualities but also to identify whether it is defect-free or not. But there is not any standard automated testing cases generation framework in computer vision communities, especially in gesture recognition. This paper has proposed an automatic software testing cases generation framework to ensure the efficiency of the gesture recognition systems. Our goal is to build a standardized framework for testing the performances of existing gesture recognition systems. In our research, we have considered the ISO/IEC /IEEE 291129 -2013 standard for software testing process and ISO/IEC /IEEE 291129 -2015 for software testing techniques. In our proposed framework, we have considered five parameters such as rotation, contrast, scaling, background, and noise which are used to generate test cases based on existing renowned gesture recognition systems. We have selected five gesture recognition systems as experimental purposes. The test process calculates accuracy using our generated test cases and compares with the existing systems results. Finally, a comparative analysis is given to improve the efficiency of the system.