LTI Canvas Insights

How important are insights for software development? This video answers that and also introduces you to LTI Canvas Insights – Re-imagined, insights-driven application development

Key Features

Effective sprint planning with right teams

Change Impact analysis

Regression test optimization

Defect advisor for faster resolution - ML/NLP based defect matching

Governance of defect reassignments

Code Analytics for improved productivity

Defect injection hotspots

Low-touch Transition - Knowledge Fabric correlating business flows to code modules

Modernizes and Enhances Journeys across Personas

Product Owner

  • ML/NLP-driven Change Impact analysis
  • Dependency analysis of new features for release readiness
  • Requirement complexity assessment

Scrum Master

  • Quick Rough-Order-of-Magnitude (ROM) effort estimations
  • Rapid sprint planning based on velocity, capability and cross dependencies of user stories
  • Visibility into the code increments for productivity vis-à-vis quality insights

Architect

  • Impact on technical debt due to the code changes
  • Architectural hotspots vs architectural components vs. code components
  • Application resilience insights

Quality Engineer

  • Eliminate regression bloat
  • Optimize defect resolution lifecycle
  • Insights into test coverage at code component level
  • Insights for defect prediction

Developer

  • Code Reusability Insights
  • Risk identification through code analytics on critical components & dependencies
  • ML/NLP-driven smarter Defect Resolution

Resources

AI-powered Agile & DevOps platform helps a Hitech company achieve 67% reduction in regression, enabling better speed-to-market
LTI Canvas Insights

AI-powered Agile & DevOps platform helps a Hitech company achieve 67% reduction in regression, enabling better speed-to-market

AI and amplifying automation, to create defect-less code and decrease technical debt in application engineering
LTI Canvas Insights

AI and amplifying automation, to create defect-less code and decrease technical debt in application engineering

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