نوع المشرف

مشرف رئيسي

تاريخ الاشراف على الرسالة من

2022

الى

اسم الطالب

هبه نافز محمد جلال

ملخص الرسالة

System testing is critical since it assures that users will not encounter faults. One of the testing approaches is black-box testing. The Black Box approach can uncover error classes in a White Box test. Equivalence Partitioning and Boundary Value Analysis are some of the Black Box Testing Method techniques. These approaches are used to minimize the number of test data that could be used in actual testing. Still, if the input range of data is enormous, the problem that arises is a large number of input test data even after using the Equivalence Partitioning and Boundary Analysis. In this work, Machin Learning, Genetic Algorithms, and Decision tree learning techniques are proposed to select the optimal set of input test data from the extensive range of input.