Abstract
Software test case optimization for real-time systems is a vulnerability detection methodology that assesses the resilience of targeted programs by subjecting them to irregular input data. As the volume, size, and intricacy of software continue to escalate, conventional manual test case generation has encountered challenges like insufficient logical coverage, minimal automation levels, and inadequate test scenarios. These difficulties underscore the need for innovative approaches that maximize software dependability and performance. An artificial intelligence powered fuzzing technique, which exhibits remarkable proficiency in data analysis and classification prediction. This paper examines the recent advancements in fuzzing research and conducts a comprehensive review of artificial intelligence driven fuzzing approaches in software test cases optimization. The major review explains the test case validation workflow and discusses the optimization of distinct phases within fuzzing utilizing in the software testing. Particular emphasis is placed on the implementation of artificial intelligence in the following software testing phases. This process involves position selection, which includes organizing and cleaning data; generating test cases that cover different inputs and expected outputs; selecting fuzzy input values for testing edge cases; validating the results of each test case to ensure accuracy and reliability. Finally, it synthesizes the obstacles and complexities associated with integrating artificial intelligence into software test case optimization techniques and anticipate potential future directions in the software testing.
Keywords: Artificial Intelligence, Software Testing, Test Case Optimization, Test Case Validation Techniques.