Investment Rating - The report does not explicitly state an investment rating for the industry or company Core Insights - The exploration and application of large models in end-to-end testing are aimed at enhancing efficiency and collaboration in the testing domain, leveraging AI capabilities to automate and optimize testing processes [4][19][28] - The integration of AI tools and traditional testing methods is crucial for improving testing efficiency and reducing collaboration costs [21][28] - The report emphasizes the importance of a structured approach to AI implementation, focusing on the development of intelligent agents that can facilitate automated testing and enhance user experience [28][39] Summary by Sections Background - The company operates a one-stop residential service platform covering second-hand housing, new housing, rentals, and home decoration [9] - The business model involves complex scenarios requiring multi-role collaboration and personalized intelligent matching [12] Problems and Pain Points - Traditional testing processes face challenges such as serial collaboration among multiple roles, leading to inefficiencies and delayed quality feedback [15][17] - The complexity of tools across various domains increases maintenance costs and complicates the integration of AI into existing workflows [17][18] Solutions and Overall Approach - The report outlines a solution that combines AI-driven testing strategies with traditional methods, focusing on the development of intelligent agents to automate the testing process from interaction to verification [28][39] - The approach includes creating a standardized testing workflow that integrates AI capabilities to enhance testing efficiency and accuracy [31][36] Technical Practices - Key technical points include the creation of assistants that utilize specific tools and prompts to solve testing-related problems, enabling automated planning and execution [34][39] - The integration of AI with traditional testing tools is highlighted as a means to enhance the overall testing process, including automated test case generation and bug reporting [40][51] Summary and Outlook - The report concludes with a focus on sustainable development of model capabilities, emphasizing the need for knowledge accumulation and structured data management to enhance testing efficiency [58][59] - Future applications are expected to focus on improving the entire testing process, from product development to testing, ensuring rigorous and efficient workflows [60]