崔宸-AI生成checklistQUNAR测试域结合AIGC提效实践

Investment Rating - The report does not explicitly state an investment rating for the industry or company Core Insights - The integration of AI and AIGC (Artificial Intelligence Generated Content) is enhancing efficiency across various domains such as development, testing, and operations [5][7] - The use of large language models (LLMs) is driving product innovation and improving user experience [2][4] - The report highlights the significant time savings achieved through AI-generated checklists, with a potential annual savings of approximately 200 person-days (pd) [73] Summary by Sections Background - The report discusses the current challenges in communication among PM, DEV, and QA teams, which average 30 minutes to 1 hour for requirement discussions [10] - It identifies inefficiencies in self-testing and checklist creation, with time spent varying based on the complexity of the requirements [10][11] Design Concepts and Solutions - The design focuses on improving accuracy, coverage, and measurement of effectiveness in generating checklists using AI [14][15] - A structured approach is proposed for processing requirement documents to enhance the generation of test points and checklists [27][33] Effectiveness Evaluation - The report outlines metrics for evaluating the effectiveness of AI-generated checklists, including adoption rate, coverage, and recall rate [59][60] - Current adoption rates for AI-generated checklists range from 60% to 70%, while recall rates are between 30% and 40% [73] Results and Future Plans - The report indicates that over 500 projects utilize the AI checklist monthly, with a product requirement coverage of 60% to 70% [74] - Future plans include fine-tuning internal models to handle sensitive data and integrating knowledge bases to enhance AI capabilities [76]