An Review on Automated Test Data Generation with Java Environment

Software testing has long been regarded as a useful method for raising the caliber and dependability of software. Path testing is the principal technique for improving software quality and the most reliable software testing approach overall. In a realm of software testing, the demand for efficient and comprehensive automated test data generation techniques is ever-increasing. Addressing this need, we propose a novel Hybrid ACO-NSA approach tailored for Java environments, which combines Ant Colony Optimization (ACO) and Negative Selection Algorithm (NSA). Our approach involves a methodical procedure that starts with turning the programme being tested into a control flow graph (CFG) and continues with using NSA for redundancy reduction and ACO for route tracing. It is our goal to generate highly effective test data sets with great coverage by recasting the test data generation issue as a hybrid of ACO and NSA problems. Through extensive testing and analysis, our system outperforms the state-of-the-art approaches, with complete route coverage achieved with an average of three generations and a success rate of 99.8 percent. This exceptional performance underscores the efficiency and effectiveness of the Hybrid ACO-NSA approach, positioning it as a promising solution for automated test data generation in Java programming contexts.