Predictive Analytics

Predictive Analytics

  • Plan for Agile releases

    Reduce development risks and costs while supporting Agile/Waterfall methodologies. Plan for quality and test management across complex app portfolios.

  • Build quality into codes

    Actively optimize code builds for efficient and accurate app development of integrated solutions; in highly regulated, legacy enterprise environments.

  • Scale continuous testing

    Accelerate app quality for continuous assessment that work at enterprise scale; to reduce defect rates with test efficiencies and root cause analysis.

Plan-Develop-Test-Operate app delivery with Predictive

Intelligent predictions and recommendations reduce defect backlogs and optimize resource usage, for dev-test teams to enjoy continuous quality application delivery.

Harness the power of application dark data for actionable insights.

Start planning for dev-test success using Predictive.

Setup Agile application release for success. From story points to capacity requirements, analyze dev-test teams’ past performance for more accurate planning and estimation of the actual development timelines.

Build Predictive into the continuous assessment pipelines.

Improve efficiency and accuracy when writing code across a complex application portfolio. Learn to identify code commits that will break the build, while avoiding rework by recommending desired code for reuse.

Test continuously with Predictive, for quality app delivery.

Accelerate continuous application testing by predicting defect rates. Reduce time to fix by identifying the root cause of a failed test. Based on code changes, offer recommendations of desired tests for reuse.

Collaborate across dev-test and operations with Predictive.

Promote collaboration among Development, Test, and Operations teams. Track and report on test coverage to actual use with production data, for test efficiencies that identify missed defects and resource gaps.

Under the hood with HPE and Predictive

  • Learn

    Machine learning techniques applied to historical data in the application development lifecycle setups analytics driven planning cycles, for one source of truth.

  • Analyze

    Multivariate analysis algorithms developed to heuristically learn and improve on predictions and recommendations; actively mitigate app risks and optimize delivery.

  • Recommend

    Predict defect convergence with production data promotes test efficiencies. Using “What-if” analysis that examines for impact as modeled across different scenarios.