How Large AI Models Are Transforming Software Testing

1. Concept of Large Models

Large models refer to machine learning models with massive parameters and complex computational structures, typically built from deep neural networks with billions to trillions of parameters. They aim to enhance expressive power and predictive performance, handling complex tasks and data across natural language processing, computer vision, speech recognition, recommendation systems, and more. By training on massive data, they learn intricate patterns and exhibit strong generalization, enabling accurate predictions on unseen data.

ChatGPT explains large models in a more accessible way, highlighting their human‑like inductive and reasoning abilities: essentially a deep neural network trained on massive data, whose scale leads to emergent intelligence resembling human cognition.

2. Applying Large Models to Software Testing

Test Shift‑Left

Code Review Our company offers large‑model tools such as JoyCoder, Autobot, and aichat.jd. For example, JoyCoder can interpret selected code, helping testers understand code intuitively.

Generating Pre‑test Cases When product requirements are clear, JoyCoder can directly generate upstream test cases.

Testing Process

Code Security Check Using JoyCoder → “Divine Doctor” security check, one can preliminarily detect code vulnerabilities.

Code Performance Check Select a code segment, then in JoyCoder’s dialog input: “Check the performance of the selected code” and press Enter.

3. Summary and Outlook

When large models are fed extensive product documentation, their reasoning aligns with product business logic. Once mature, they could design concrete requirements covering about 90 % of manual test cases, freeing time for automated test development and other tasks.