Pivotal techniques of embedded software testing case generation by genetic algorithms

In software testing, it is often desirable to find test inputs that exercise specific program features. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, numerous attempts have been made to automate the process. Often, the desired inputs must satisfy complex constraints, and this makes a random approach seem unlikely to succeed. We advance a visible arithmetic - GA (genetic algorithms) to solve this problem that how to generate efficiency testing case of embedded software for the designation path is covered with, because the key of automatic of software testing is the generation of efficiency testing case. In this paper, we discuss experiments with test generation problem that is harder than the ones discussed in earlier literature - we use a lager embedded program and more complex test adequacy criteria. In the last, we give "embedded software case generation system", which generate test case automatically for given embedded program unit. We discussed some technical details of this system. So we find a better technique based on genetic algorithms for automatic generation of testing case in embedded software