Leverage Artificial Intelligence for testing the Quality of Your enterprise applications

Accelerate your Digital Transformation journey using the power of AI

Artificial Intelligence-led Testing & Performance Engineering Enablement


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 incorporates AI/ML elements within your existing overall QA framework through implementation of IP to achieve AI/ML-led testing & performance engineering enablement:

  • Defect Analytics
    • Use of AI-based Sentiment Analytics: BlueSwan Cigniti Enterprise Sentiment Analyzer (CESA) – AI-tool to find, categorize, and distribute the overall sentiment of a conversation (Positive, Negative, Neutral) for better decision making
    • Realtime Dashboard & AI-based Predictive Analytics: BlueSwan Verita™ – Analytics-driven workload modelling for defect prediction, code coverage, response time, & scalability prediction
  • Performance Engineering approach
    • ML-based analytics driven Performance Predictions: Workload modeling & response times
  • Regression Optimization
    • Automated collation of the dependent test cases/scripts-based on the changes: Cigniti Impact Analyzer – solution for test suite impact analysis during Change Requests, Patches, & upgrades
    • Automated prioritization of test cases/scripts-based on Machine learning
  • Smart Automation
    • Automated change detection in the object properties across scripts for every new release
    • Self-healing of test scripts based on the application changes

Assured Benefits of Cigniti’s AI Testing Services


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.

Cigniti’s Intelligent Test Case Management Strategy offers assured benefits of:

  • Faster deployments
  • Matured governance over test data and test suite
  • Better traceability with backward & forward integration
  • Holistic approach with early feedback with unattended execution
  • One integrated platform
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Cigniti’s AI & ML Center of Excellence


Access Cigniti’s AI & ML CoE that includes:

  • Verita: a critical component of Cigniti’s next-generation IP, BlueSwan – a quality engineering platform and dashboard with predictive analytics capabilities
  • Prudentia: another component of BlueSwan – a model-based testing tool that automatically generates intelligent software testing procedures and scenarios using models of system requirements, leveraging a patented algorithm
  • Social Sentiment Analyzer Tool: an in-house tool created by the Cigniti Innovations and R&D team, powered by Machine Learning & AI that identifies and categorize feedback expressed by end-users to determine (both objective and subjective) their experience from a product & service quality perspective and create actionable insights.
  • An enhanced Cigniti test automation framework that supports various RPA tools for matured automation practices
  • A Test Automation CoE with ML and AI-assisted tools such as Applitools, SauceLabs, Testim, Sealights, Test.AI, Mabl and ReTest
  • Algorithms to test your AI systems

AI-led testing approach &
focus areas


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, at Cigniti, we have established a 4-pronged AI-led testing approach that includes:

  • Discover – Smart asset creation using data repositories including defects, tickets, logs etc. for analysis
  • Learn – Identify relationships between test assets such as defects and software requirements for insights
  • Sense – Predict occurrence of an incident, impact, and likelihood led by analytics and insights
  • Respond – Respond to an incident, input the resolution and results for continuous learning

Our Partnerships


Eggplant
Appvance
Applitools

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Use our expertise in defect predictive analytics and test execution to ensure 100% test coverage for your AI-based applications.

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