GienTech AI Competition & Industry Innovation

Recently, the GienTech AI Agent Innovation Competition concluded successfully, featuring dozens of intelligent solution proposals across automotive technology, software testing, AI-driven automation, and operational efficiency. Against this backdrop, the Agent-Based Android In-Vehicle UT End-to-End Automation Platform showcases GienTech’s expertise in leveraging multi-agent AI to address core industry challenges.

Addressing Core Testing Challenges

GienTech’s platform targets persistent challenges in automotive Android testing: hardware-dependent environments, repetitive UT coding, slow coverage improvement, and high maintenance costs. It standardizes environment configuration, integrates CI/CD pipelines, and establishes a closed-loop workflow from analysis to reporting.

Multi-Agent Architecture for Full-Process Automation

The platform employs a multi-agent architecture with four specialized agents and one execution foundation. The Logic Analysis Agent defines UT strategies from project dependencies; the Code Generation Agent creates targeted tests using source code, AST, and Robolectric rules; the Result Summary Agent consolidates execution outcomes; the Strategy Optimization Agent iteratively improves coverage based on JaCoCo reports. The AgentOps platform coordinates file operations, compilation, and automation orchestration.

Measurable Gains in Efficiency and Quality

Compared with manual development, the platform reduces effort significantly. Single-class UT code can be generated in 5 minutes; representative modules requiring ~8 hours manually can be completed in ~3 hours. Project-level efficiency improves 35–40%. Integrated CI/CD and coverage gates improve root-cause analysis, reduce scenario omissions, and support continuous optimization throughout the development lifecycle.

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