While leveraging AI for testing apps for quality, enterprises may face multiple challenges such as identifying the exact use cases, lack of awareness about what really needs to be done, verifying the app behavior based on the data that has been input, testing apps for functionality, performance, scalability, security, & more. Cigniti’s extensive experience in the use of AI, ML, & analytics helps enterprises improve their automation frameworks & QA practices. Cigniti provides AI/ML-led testing and performance engineering services for your QA framework through implementation
With a strong focus on AI algorithms for test suite optimization, defect analytics, customer sentiment analytics, scenario traceability, integrated requirements traceability matrix (RTM), rapid impact analysis, comprehensive documentation and log analytics. In this era of daily deployments and DevOps transformation, organizations need to automate the test requirement traceability and versioning to accelerate the QA cycle, reduce overheads in test management, and provide superior quality governance.
AI-tool to find, categorize, and distribute the overall sentiment of a conversation for better decision making.
Automated prioritization of test cases/scripts-based on Machine learning. Analytics-driven workload modelling for defect prediction, code coverage.
Automated change detection in the object properties across scripts for every new release. Self-healing of test scripts based on the application changes.
Another component of the a model-based testing tool that automatically generates intelligent software testing procedures.