الفهرس 
Abstract
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Abstract
Computers and programs are major parts in the life. Software testing is an important is sue in the software development process. A good test process increases the reliability and useability of software process. The goal of software testing is to generate a set of minimal number of test cases such that it reveals as many defects as possible by satisfying a partic ular criteria called test adequacy criteria e.g. path coverage. While software testing is very signi cant, it is also very expensive, a laborious and time-consuming work. The complexity of software testing can reach to exponential time. Test automation may be able to reduce or eliminate the cost of actual testing. Manually creating test data is a very time consuming task and prone to human error [2]. So, automatic test data generation become an important subject which is studied in the eld of software testing. A computer can follow a sequence of steps more quickly than a person, and it can run the tests overnight to present the results in the morning [1].Genetic Algorithms (GA) have been successfully used to automate the generation of tests for software. The test data are derived from the program’s structure with the aim to traverse every branch in the software. The investigation uses tness functions based on two points number of nodes which are visited and gene probability. The input variables are represented in gray code and as an image of the machine memory. The power of GA lies in their ability to handle complex structure input data. Thus, GA is a good candidate to generate a test data for software Testing using standard GA is used as a comparison of the performance of a proposed GA Test Data Generators. The advantage of GA is that, through the search and optimization process, test sets are improved where the value of variables is at or close to the input domain boundaries.