Developing Optimal Directed Random Testing Technique to Reduce Interactive Faults-Systematic Literature and Design Methodology

The objective is to minimize ambiguous test cases by using Adaptive Genetic Algorithm (AGA). The researchers are concerned with the problem of randomly generated test cases. It can contain some ambiguous test cases, which lead to problems at the organizational level. A random algorithm will generate random test cases each time it is run, and it will have resemblance each time. Another problem related to random algorithms is that running them can take a lot of time. To minimize these issues we propose a new technique, which will reduce the given drawbacks. They proposed an AGA, which will provide legal input in each case where it applied. Thus the problem of ambiguity will decrease. In this research, the near optimal inputs will be generated based on the AGA, which will reduce the illegal inputs. The fault detection rate is used as the fitness function in AGA. To remove the fault proneness, the AGA uses the coverage metrics of the test cases.