This project presents an innovative approach to automating test case generation by integrating Neo4j AuraDB, a fully managed cloud graph database service, with Google's Gemini AI model. The system is designed to enhance the efficiency, scalability, and intelligence of software testing processes.
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Structured Data Representation: Utilizes Neo4j AuraDB to model software requirements and their associated test cases as nodes and relationships within a knowledge graph. This structure enables a clear and organized representation of complex data interconnections.
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Efficient Querying and Management: The graph-based structure allows for rapid querying and retrieval of information. For instance, identifying all test cases linked to a specific requirement becomes straightforward, facilitating efficient impact analysis and traceability.
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Scalability: Neo4j AuraDB's cloud infrastructure ensures that the knowledge graph can scale seamlessly with the growing volume of requirements and test cases, maintaining performance and reliability.
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Automated Test Case Generation: Employs the Gemini AI model to analyze existing requirements and test cases. By recognizing patterns and leveraging predefined test case templates, it dynamically generates new test cases.
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Template Utilization: To ensure consistency, the AI model structures the generated test cases according to predefined templates, aligning them with industry standards and best practices.
To explore this project:
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Clone the Repository:
git clone https://github.com/Emmanuel-Rono/Auto_TestCase_Generator -
Set Up Neo4j AuraDB: Sign up for Neo4j AuraDB and set up your database instance.
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Configure Gemini AI: Ensure you have access to Google's Gemini AI model. Refer to the Gemini AI documentation for setup instructions.
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Run the Application: Follow the instructions in the repository's README to execute the test case generation process.
For detailed guidance, please Contact me