BackIP: Mutation Based Test Data Generation Using Hybrid Approach

Abstract

Fault-based testing is a powerful technique to ensure the quality of software by evaluating the efficacy of the test suits and also used to check the thoroughness of testing performed by other software testing techniques. However, it is very complicated and computationally expensive testing method. Literature shows that there is a tremendous effort to give formal solutions and heuristics methods. Recently, state-of-the-art approaches based on hybrid optimization techniques have been proven to be suitable for cost effective results. This work implements and presents a multi-objective novel hybrid method by combining Backtracking search optimization algorithm and Integer programming approach(BackIP). Unlike, some other approaches, BackIP is a test input data generation method which includes test data generation, mutation analysis, and test suite reduction simultaneously. Experimental comparison is conducted on a widely used benchmark java programs and results show that the proposed approach achieves test data generation with mutation score up to 94% and improved test suite reduction between 70% to 94% as compared to the state-of-the-art techniques.