Blogs AI and ML powered Metrics and Analytics - SmartQE

AI and ML powered Metrics and Analytics - SmartQE

By Admin Jul 02, 2020

SmartQE is an AI and ML powered opensource intelligent framework that can automate build, release, deployment, functional, performance, security, compatibility, usability tests at one place.

SmartQE is a self-service portal with rich UI, rapid deployments, supports multi-cloud environments and customized reporting. AI and ML powered release and test automation dashboard acquire, aggregate and analyze reports to ascertain release health.

SmartQE automates application delivery life cycle from requirements mapping with sprints, user stories, code base, source control, test automation, execution and monitoring. This framework allows to integrate Collaboration & Management, Continuous development, Continuous Integration, Test and monitoring tools and technologies. SmartQE can be integrated to industry proven DevOps and TestOps opensource, Azure and Atlassian tools and technologies.

Metrics and Analytical Value Addons
  • AI and ML Powered dashboard at project, sprint, workflow, build, release, deployment, testset and testcases reports acquire, aggregate and analyze test reports to ascertain release health 100% accurate.
  • Accurate and real time release and test automation metrics.
  • Integration of test management and collaboration tools to populate single source helping you to take production go and no-go decision
  • SmartQE metrics and analytical reports APIs can be integrated to any test management and collaboration tools
  • ML provides failure prediction, will reduce regression test time by 70%
  • Automate RCA and link to continues improvement process.
  • Release automation dashboard provides build, release and deployment health
  • Reportportal AI reports help to isolate defects easily, help to fix defect quickly.
Metrics and Analytical reports provided by SmartQE
  • Regular, Hotfix, Patch and Maintenance releases automation metrics by sprint or a project.
  • Release automation pipeline jobs success and failure status with log analysis.
  • Code quality and unit test defects by code repo.
  • Test coverage & Test design automation.
  • Test data automation and Risk based test strategy
  • SLAs, KPIs can configure and publish periodically
  • Performance, Security and Infrastructure monitoring
  • SmartQE metrics and Analytical reports integrate to collaboration and test management tools – Jira or micro focus ALM
  • Sprint code build, release and deployment status against Regular, Hotfix, Patch Maintenance release types
ML Powered Test Analytical Report

Algorithms and models built in Google TensorFlow with Python programming

  1. Risk based test ML algorithms populate failure predictions for subsequent releases
  2. Populate fail predictions as below diagram
    1. By release
    2. By workflow
    3. By Project

Click any of test type line totals, will take you to test case/set dashboard

Select execution mode to execute testcases against AUT. Current agile world doesn’t have time to spend days or weeks to integrating changes and test. ML algorithms populate failure predictions help to reduce regression test cycle in an hour.

Build, release and deployment automation dashboard
Analytical Reports

SmartQE provides reports at testcases/testsest, work flows, project and AI powered reportportal. It can be integrated to any collaboration or test management tools through its API’s.

Project Report

Work flow Report

Testset Report

SmartQE integrated to Report portal

SmartQE helps to capture execution run time data, isolate and classify whether defects are part of environment or application. Granular level execution log in report portal will help developers to fix defects at earliest. Different widgets in report portal help to populate different view reports

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