Authors: Kiran Paul Kanikaram
Abstract: Software testing is among the most time-consuming and challenging stages of the development lifecycle to automate. Effective testing requires contextual awareness, including an understanding of code semantics and the identification of edge cases. The advancement of Large Language Models (LLMs) has enhanced the feasibility of automated testing by enabling unit, end-to-end, and exploratory testing as well as supporting a more agentic Quality Assurance (QA) process. The following paper will discuss the current use of large language models in software testing, with an emphasis on test case creation, bug detection, and self-healing automated systems based on natural language prompts. Although LLMs are providing novel ways to improve testing efficiency, there are certain obstacles that require special attention. They include incorrect output due to hallucinations, incomplete test coverage, and decreased reliability as the software grows. To better understand the obstacles, the example of a checkout module taken from the existing literature is discussed. The results show that while LLM-based testing methods can achieve useful test coverage, they do not necessarily outperform search-based methods. The analysis concludes that the value of LLMs in Quality Assurance is maximized through a human-in-the-loop approach, supported by a five-layer governance framework. As a result, the role of the QA professional is evolving toward that of a test-quality engineer and AI supervisor, requiring an expanded skillset.
