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AI for Quality Engineering

AI/ML-driven Analytics for Development Pipelines

AI/ML-driven Analytics for Development Pipelines

Testing spend takes up about 30% of overall project cost. In large testing projects, costs can go even higher due to the complexity involved in right resource allocation and prioritizing the testing tasks.

With the advances in Machine Learning, availability of libraries and algorithms through popular scripting languages such as Python, and faster and cheaper computing power, we identified that the time was right to use all of this information to introduce Artificial Intelligence for testing projects – with the goal of improving business outcomes and accelerating our testing.

Overview of the Tech Mahindra’s AI based Testing Solutions

eAnalytics - AI/ML Powered Predictive Analytics for Testing Projects
Key Features:
  • Predict the outcome
  • Using Explainable AI, detect the likely causes (Hotspots) of failure prediction
  • Using the hotspot analysis, provide automated Actionable Insights
MAGiX – AI Based Visual Testing
Why It’s Important?
  • Visual bugs adversely affect the customer experience and the brand value
  • Visual bugs can affect the revenue (even if application function as expected)
  • Omni-channel digital apps make it difficult to catch visual bugs across all browsers, devices and platforms
CHA.ai – Change Based on AI : CHA.ai
Key Features
  • Reduced time for Casual analysis
  • Less dependency on SMEs for RCA
  • Single source of truth for all logs (Elastic Stack)
  • Minimum test support organization required
Dexter – Chatbot for testing projects

Dexter, “not just a Chatbot”, let organizations improve the efficiency of their IT and Operation teams through simplified and intuitive access to functionalities for their regular dev, test, triage, support and social needs.

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