Hybrid Approach for Automated Test Data Generation

Software testing has long been thought to be a good technique improve the software quality and reliability. Path is most reliable key method for improving among all approaches. On other hand, test data big impact on activity’s ability detect errors or defects. To solving problem, one must locate entire search space relevant input encompass different paths in testable program. satisfy path coverage, it vital look at accumulated across thorough area. A new approach based ant colony optimization negative selection algorithm (HACO-NSA) presented this research which overcome flaws associated with search-based by generated automated data. The optimum objective generate appropriate maximise coverage enhance data’s efficacy, as result, adequacy validated using path-based fitness function. In NSA generation stage, suggested alters detectors creation ACO. proposed evaluated metrics such average generation, time, success rate comparison done random testing, Different benchmark programs have used object-oriented system. findings show that hybrid methodology escalates percentage curtail size, reduces redundancy enhances efficiency. follows IEEE 829-2008 documentation process.