Artificial Intelligence & Machine Learning for Quality Assurance

Traditional quality assurance (QA), focused on validating requirements, bypasses a wealth of information that can be obtained from sources like project documentation, test artifacts, defect logs, test results, production incidents, etc.

Xcellencer brings machine learning together with analytics to unlock the power of this data and drive automation and innovation, improving QA efficiencies beyond the reach of traditional QA practices. Artificial intelligence (AI) algorithms learn from test assets to provide intelligent insights like application stability, failure patterns, defect hotspots, failure prediction, etc. These insights will help anticipate, automate, and amplify decision-making capabilities, thereby building quality early in the project lifecycle. Infosys has developed an in-house, machine learning platform which will help in multiple phases of the software testing life cycle, leading to more efficient execution and reduced effort.

We have created unique solutions for artificial intelligence or machine learning led QA. Our key offerings in AI/ML led QA include:

Test suite optimization – Identifies duplicate/similar and unique test cases

Predicting the next – To help predict the key parameters of software testing processes based on historical data.

Log Analytics – Identifies hotspots and automatically execute test cases

Traceability – Identifies complex scenarios from the requirements traceability matrix (RTM) and extract keywords to achieve test coverage

Customer sentiment Analytics – Analyzes data from social media and provides an interactive visualization of feedback trends


  • Improved quality – Prediction, prevention, and automation using self-learning algorithms
  • Faster time to market – Significant reduction in efforts with complete E2E test coverage
  • Cognitivity – Scientific approach for defect localization, aiding early feedback with unattended execution
  • Traceability – Missing test coverage against requirement as well as, identifying dead test cases for changed or redundant requirement
  • One integrated platform – Adaptable to client technology landscape, built on open source stack
  • Defect analytics – Identifies high-risk areas in the application which helps in risk-based prioritization of regression test cases