IntelligenTester - Software Test Sequence Optimization Using Graph Based Intelligent Search Agent

Software testing plays a vital role in quality software development. Usually, the number of test cases required to develop error-free software, will be very high. Since, exhaustive testing is not possible; the test cases that we need to generate should be optimal and also should cover the entire software and reveal as many errors as possible. In the proposed approach, the intelligent search agent (ISA) will take the decision of optimized test sequences by searching through the SUT, which is represented as a graph in which each node is associated with a heuristic value and each edge is associated with an edge weight. The intelligent agent will find the best sequence by following the nodes that satisfy the fitness criteria and generates the optimized test sequences from the set of all test paths of the SUT. Finally, we compared ISA with ACO and proved that ISA is taking less time and cost in generating optimal test sequences.