Test Modeller leverages the power of generative AI to redefine model-based testing, offering innovative integration points that significantly enhance the software testing process.
1. Co-pilot is an AI-assisted component within Test Modeller, which aids in creating and reviewing models. This smart assistant can interpret the content of the model, convert it into user stories, and is even equipped to query the model's contents. This intelligent integration point enhances the model-building process, ensuring accuracy and efficiency.
2. DataGPT is a feature in Test Modeller that leverages AI to generate data spreadsheets. This integration point utilises the generative capabilities of AI to create comprehensive datasets, which can then be used within the testing models. The resulting data is more diverse, realistic, and comprehensive, leading to more effective testing scenarios.
3. ModelGPT is another integration point that uses AI to convert text-based requirements directly into models. This automates a traditionally manual process, resulting in a significant boost in efficiency, while also reducing the potential for human error.
4. KnowledgeHub AI serves as an intelligent search engine within the Test Modeller workspace, capable of querying all models within the workspace. This integration point can also connect to external platforms such as JIRA and Confluence to query an organization's own data, providing a unified view of the testing landscape.
Test Modeller currently uses Large Language Models (LLMs) from OpenAI and Azure. However, its flexible architecture means it can easily integrate with any homegrown LLM, or a specific LLM an organization is using within their infrastructure, depending on their security requirements. This robust utilization of AI underscores Test Modeller's commitment to continuously enhancing the capabilities of model-based testing and driving the evolution of software testing methodologies. Read more about configuring modeller and adding your own LLM.