DOI: https://doi.org/10.36347/sjet.2025.v13i06.001

Pages: 357-376

This research investigates the efficacy and challenges of employing generative Artificial Intelligence (AI) models—specifically Anthropic Claude, OpenAI GPT, and Google Gemini, orchestrated via the Cline extension in Visual Studio Code—to construct a .NET MAUI test application for validating FinTech Software Development Kits (SDKs). The study focuses on automating the generation of XAML for user interfaces and C# for backend logic, targeting critical FinTech workflows such as wallet provisioning on Android and iOS platforms. The methodology involved an iterative AI-assisted development process, encompassing AI-driven planning, code generation, extensive human-led refinement, and rigorous testing using a mock SDK and Appium for UI automation. Hypothesized results suggested significant acceleration in initial boilerplate code generation, though a substantial portion (40-60% for XAML, 30-50% for C#) required manual rework to address framework-specific nuances, ensure code quality, and implement robust error handling. Key challenges identified include the AI's inconsistent understanding of .NET MAUI's XAML dialect, limitations in managing complex UI state, occasional AI hallucinations, and the need for highly specific, context-rich prompts. Despite these hurdles, the final human-refined test application successfully automated the validation of the designated FinTech workflow across both platforms. The findings indicate that while AI serves as a powerful accelerator in test application development, expert human oversight remains indispensable for ensuring the quality, security, and framework compliance of the generated code. The study concludes that AI dramatically reshapes the role of the test automation engineer towards that of an AI orchestrator and critical validator, and outlines future research directions including the development of fine-tuned AI models for specific frameworks like .NET MAUI and AI-driven test data generation for complex FinTech scenarios.