Optimizing the Software Testing Efficiency Using Genetic Algorithm - Implementation Methodology

A recent study by the national institute of standards and technology found that national annual cost of an inadequate infrastructure for software testing is estimated to range from$22.2 to $59.5 billion which are about 0.6 percent US gross domestic product, hence software testing is an important activity in software development cycle. The goal of software testing is to design a set of minimal number of test cases so it reveals as many faults as possible. Exhaustive software testing is rarely possible. Testing has two goals, one is to demonstrate to the developer and customer the software meets its requirements. Second is to discover faults or defects in software where the behavior of the software is incorrect or does not conform to its specifications. This paper explains about how software testing can be performed using Genetic Algorithm to identify most critical paths (where more errors can be generated) so that software testing can be optimized i.e an implementation methodology.