To establish the idea of integrating AI into mobile app testing strategies and turn it into a research project, you can follow these steps:

Literature Review: Conduct a comprehensive literature review to understand the existing research and tools related to both AI and mobile app testing. Identify gaps or areas where AI can enhance current testing methodologies.

Problem Statement: Clearly define the problem you aim to address with AI in mobile app testing. This could be improving test coverage, reducing testing time, enhancing bug detection, or ensuring compatibility across different devices and platforms.

Research Objectives: Outline specific objectives you want to achieve through your research, such as developing an AI-based testing framework, evaluating its effectiveness compared to traditional methods, or identifying best practices for integrating AI into existing testing workflows.

Methodology: Define the methodology you will use to conduct your research, including the AI techniques you plan to employ, the types of mobile apps you will test, the criteria for evaluation, and any experimental setups or simulations.

Prototype Development: Develop a prototype or proof-of-concept implementation of your AI-based testing framework. This could involve designing algorithms for automated test case generation, anomaly detection, or performance optimization.

Experimentation: Conduct experiments to evaluate the performance and effectiveness of your AI-based testing approach. This may include comparing it against traditional testing methods using real-world mobile apps or simulated scenarios.

Analysis and Results: Analyze the data collected from your experiments and draw conclusions regarding the efficacy of your AI-based testing strategy. Identify strengths, limitations, and areas for improvement.

Publication and Dissemination: Write a research paper detailing your findings and submit it to relevant conferences or journals in the field of software engineering, AI, or mobile computing. Present your research at conferences and share it with the academic and industry communities.

Feedback and Iteration: Solicit feedback from peers, mentors, and industry experts to refine your research approach and address any limitations or concerns. Iterate on your prototype and experimentation based on the feedback received.