Reinforcement Learning for Adaptive Software Test Case Generation

Abstract

Software testing represents a very important stage of the software development cycle, as it requires reliability, correctness and strong performance of programs. The classical software testing methods tend to use either hand crafted test cases, or automated test generation methods that are based on heuristics, which may be time-intensive, prone to errors and ineffective when dealing with complex software systems. The field of artificial intelligence, in particular reinforcement learning (RL) has become a promising method in the field of adaptive and intelligent test case generation where agents are trained to find the most optimal testing strategies by interacting with software environments. Framing software testing as a series of sequential decisions RL facilitates automatic, high-coverage, fault revealing test cases, which dynamically evolve with software behavior. This paper examines how reinforcement learning can be used to conduct software testing with focus on how it can also be used to improve test coverage, fault detection rates and automation in software quality testing. The study summarizes the existing procedures such as deep reinforcement learning, Q- Learning, and policy gradient procedures, and presents their roles in adaptive test case generation in contemporary software systems. The results are that, the RL-based methods have been shown to be better than traditional test generation methods based on statical means as they intelligently explore the input spaces and give high priorities to the critical test scenarios. The paper finds that RL methods incorporated into program evaluation systems can ensure a significant decrease in manual labor, enhance the rate of fault detection, and facilitate the creation of high-quality and resistant software system development.