AI-Driven Test Case Generation Based on DeepSeek-Chat Large-Language Model

With the increasing complexity of software systems, traditional test case generation methods have become inadequate to meet the demands for efficient and high-quality software testing[1]. This paper proposes an AI-driven test case generation technology based on the DeepSeek-Chat large model, aiming to streamline the process from requirements to automated test case generation and enhance the automation level and efficiency of software testing in the industry. Leveraging natural language processing (NLP) and machine learning (ML) techniques[2], DeepSeek-Chat is capable of automatically generating high-quality test cases, significantly improving test coverage and defect detection rates. Experimental results demonstrate that, compared to traditional methods, this technology achieves a 30% improvement in test coverage and a 25% increase in defect detection rate. This study provides a novel and efficient test case generation approach for the software testing industry, offering significant practical application value.