Intelligent Testing Platform — Functional Overview
I. Automated Testing Platform (easyFAT 1.0): End-to-End Automation Backbone
1. Core Functional Modules
· Test Management
Supports project creation, system configuration, and environment parameterization (including server/database settings) with user access control.
Enables four-level hierarchical management of Project → System → Module → Test Case.
Offers manual and scheduled task scheduling with parallel/serial execution options.
Adopts a master–slave distributed architecture for multi-executor collaboration, compatible with both Windows and Linux environments.
· Automated Testing Engines
① UI Automation Engine:
Based on Pywinauto/Selenium/Appium, encapsulating over 30 keyword libraries to support PC client, Web, and mobile app testing.Features intelligent element detection through visible text, relative position, and DOM structure, with adaptive scrolling and pagination.
② API Automation Engine:
Built on Pytest, offering reusable libraries for messaging, communication, assertions, and security.
Supports visual orchestration at message, API, and test case levels.
Enables automatic message generation via log capture or document import.
· Data and Test Case Management
Maintains a centralized test data pool (supports variables, functions, and SQL queries).
Separates test data from test cases and allows batch duplication across environments.
Adopts a componentized test case framework (public message/function libraries) with ≥90% component reuse rate.
Enables cross-project reuse with minimal parameter adjustment, reducing maintenance costs by up to 60%.
· Multi-Dimensional Analysis
Provides analytics across three dimensions:
o User metrics: case design/debug count
o System metrics: executed tasks, success rate, average duration
o Task metrics: pass rate, failure causes
Automatically generates detailed test reports (with screenshots and logs) and supports historical record tracing.
2. Key Technical Features
The Automated Testing Platform is built with Vue + ElementUI for the frontend and Java SpringBoot for the management layer, integrating Jenkins for continuous integration and SVN/Git for version control. It supports unified UI and API testing with a zero-code interface, allowing testers to design and execute test cases without any programming expertise.
II. Intelligent Testing Platform (easyFAST 1.0): AI-Driven Full-Stack Enablement
1. AI Technology Stack
The Intelligent Testing Platform integrates NLP (text classification, RAG-enhanced retrieval), LLM (large-model pretraining and fine-tuning), CV (OCR and image recognition), and multimodal technologies (speech–text–video alignment) to build a five-tier intelligent capability framework encompassing Perception, Understanding, Reasoning, Generation, and Optimization.
2. Full-Process Intelligent Capabilities
· Requirement Analysis Phase
① FS Generation: Aggregates raw business requirements and, via AI multi-turn dialogue and knowledge augmentation, automatically generates a Word-based Functional Specification (FS) for stakeholder review.
② Test Requirement Analysis: Based on FS and private knowledge bases, the AI produces a test outline with requirement lists, flags ambiguities, and prompts clarification — boosting review approval rates to over 85%.
· Test Design Phase
① Test Case Generation: Leverages the test outline and historical cases to generate functional and process test cases (covering positive and negative paths) with batch creation support.
② Test Strategy Generation: AI analyzes requirement priorities, schedule, and resource allocation to output optimized testing strategies and plans, accelerating decision-making.
· Execution and Maintenance Phase
① Script Generation: Automatically creates single-API and workflow scripts (with test data) from test cases and interface documents, supporting debugging.
② Execution Analysis: Aggregates logs, compares with historical defect databases, locates root causes, and provides fix recommendations.
③ Self-Healing Mechanism: Detects failures caused by data errors or network fluctuations and automatically repairs scripts — reducing maintenance workload by 40%.
· Management and Assistance Phase
① Automated Multi-Dimensional Reports: Generates structured reports (process summary, result analysis, improvement suggestions) using predefined templates.
② AI Test Assistant: Provides instant access to business knowledge, regulatory guidelines, and compliance standards via natural language queries.
③ Test Data Generation: Automatically creates compliant synthetic test data (e.g., personal customer profiles, loan accounts), improving data generation efficiency by 3×.
3. Knowledge Enhancement Framework
Builds a three-tier knowledge base to support intelligent testing and reduce hallucination in large models:
1. Document Layer: Includes requirement, design, and test documentation.
2. Knowledge Graph Layer: Built on Neo4j, mapping relationships such as dependency, inclusion, and conflict among requirements.
3. Vector Index Layer:
o Uses bge-m3 for embedding generation.
o Stores vectors in the Dify vector database with hybrid retrieval and gte-rerank re-ranking.
o Improves model response precision by 50%.
III. Automated Testing Implementation Methodology: Structured Deployment Assurance
1. Test Planning
The automated testing implementation framework covers all key aspects, including solution planning (scope, technology, environment, and data), knowledge base development (tool selection and document vectorization), model and prompt optimization (structured prompt design), and process and standards design (standardization of automated testing workflows).
2. Role Definition
The Intelligent Testing Platform clearly defines the responsibilities of each role to ensure efficient collaboration:automation developers (tool development and maintenance), test system development team (platform deployment and issue resolution), automation testers (requirement review and test case debugging), and environment administrators (environment configuration and access management).
3. Phased Implementation
· Short-Term (8 Weeks): Conduct pain-point assessment and pilot in 1–2 key scenarios (e.g., credit system regression testing) for rapid value realization.
· Long-Term: Gradually scale across core, channel, and regulatory systems, achieving 70–90% functional test coverage and building a “Quality-as-a-Service (QaaS)” ecosystem.
