Machine Learning

Predict outcomes & prevent failures of your assets with custom solutions.

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

Ikon-Tech 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, and many more. These insights will help anticipate, automate, and amplify decision-making capabilities, thereby building quality early in the project lifecycle. Ikon Tech has developed a machine learning platform that 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 to 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 extracts keywords to achieve test coverage.
  • Customer sentiment Analytics – Analyzes data from social media and provides an interactive visualization of feedback trends.
  • Defect analytics – Identifies high-risk areas in the application, which helps in risk-based prioritization of regression test cases.
Machine learning
  • 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.