AI-Powered Automated Test Case Generation: Design, Implementation, and Future Plans

AI Background – The rapid growth of AI‑Generated Content (AIGC) is reshaping content creation across industries, and its powerful text‑generation capabilities enable new productivity gains in software testing.

Why AI for Test Cases – Manual test‑case writing suffers from redundancy, time consumption, and coverage gaps; AI can accelerate case creation, improve coverage, and ensure consistency.

Design Scheme

The solution follows a pipeline: PRD document → test‑point extraction → test‑case generation → Xmind export → platform sync.

Core Components

PRD File Parser – Supports Feishu documents (text, tables, spreadsheets) and extracts six steps: token acquisition, user token, block list, table extraction, sheet extraction, and result assembly.

Test‑Point Generator – Converts PRD data into test points using keyword and vector weighting (keyword_weight:0.3, vector_weight:0.7) with an AI model accuracy of 0.85.

Test‑Case Generator – Transforms AI‑generated test points into markdown‑structured test cases (name, preconditions, steps, expected results) and then into Xmind format.

Knowledge‑Base Construction – Builds a LLM‑enhanced repository of historical test cases and business documents to improve recommendation accuracy.

Implementation Results

The platform now automates the full flow from PRD parsing to test‑point generation, Xmind case creation, and synchronization, markedly improving test‑authoring efficiency.

Summary & Planning

Future work includes supporting multimodal PRD inputs (images, flowcharts), continuously refining the RAG model and knowledge base, and expanding AI capabilities for broader testing scenarios.

Key Visuals