Parallelized genetic operations for SBST using Hadoop MapReduce framework

Search Based Software Testing (SBST) is one of the most explored fields in Software Testing. It suffers from the optimization problem to execute the Software Under Test (SUT). This problem is addressed mostly using Genetic Algorithm (GA) and it involves three operations namely selection, crossover and mutation to accomplish a global search to yield fitness solution to run the SUT successfully. In existing work, GA is combined with Hadoop MapReduce to give Parallel Genetic Algorithm (PGA). Here, mapper function performs parallel fitness computation and reducer function performs the GA. This PGA generates test suite that makes the entire SUT to get executed. This paper makes an attempt to existing by parallelizing fitness calculation and GA operations to generate search test data for the SUT based on branch coverage criteria.