White Paper

Predictive Analytics for ALM: Getting Your Data Ready

Is your data ready for Predictive Analytics?

What if you could avoid common problems in your software development lifecycle as overestimating capacity, high-defect rates, missed release timelines, and test coverage gaps?

That’s the promise of predictive analytics. You can use the emerging PA features in tools such as HPE Application Lifecycle Management to accelerate development, improve quality, and mitigate risk­. There’s just one catch: These machine learning techniques need to analyze your historical data in order to make those predictions. Is your data ready?

This report offers practical tips such as:

  • Always save all of your projects
  • Use at least one ALM project per project, across several releases, rather than using just one project per release
  • Use a standard project template
  • Continuously update defect status

Register today for a complimentary copy of Getting Your ALM Data Ready and learn how, with the right data, you can apply PA in these key areas:

  • Predictive planning – Use it to improve feature size estimates based on story points, identify and correct inaccurate estimates, improve requirements prioritization, and identify under- or overcapacity situations.
  • Predictive development –  Identify code check-ins that will break the build before they’re checked in, get code-completion suggestions, analyze source code for defects or complexity, and promote code reuse by identifying existing code that provides desired functionality.
  • Predictive testing – Predict the rate at which defects will be fixed and the injection rate of defects, identify the defects most likely to cause escalations in production, and determine the root cause of failed tests.
  • Predictive operations - Identifying gaps between end-user actions and the workflows that your tests are covering, reduce the likelihood of escaped defects, and link customer defects with user requirements.

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Is your data ready for Predictive Analytics?

What if you could avoid common problems in your software development lifecycle as overestimating capacity, high-defect rates, missed release timelines, and test coverage gaps?

That’s the promise of predictive analytics. You can use the emerging PA features in tools such as HPE Application Lifecycle Management to accelerate development, improve quality, and mitigate risk­. There’s just one catch: These machine learning techniques need to analyze your historical data in order to make those predictions. Is your data ready?

This report offers practical tips such as:

  • Always save all of your projects
  • Use at least one ALM project per project, across several releases, rather than using just one project per release
  • Use a standard project template
  • Continuously update defect status

Register today for a complimentary copy of Getting Your ALM Data Ready and learn how, with the right data, you can apply PA in these key areas:

  • Predictive planning – Use it to improve feature size estimates based on story points, identify and correct inaccurate estimates, improve requirements prioritization, and identify under- or overcapacity situations.
  • Predictive development –  Identify code check-ins that will break the build before they’re checked in, get code-completion suggestions, analyze source code for defects or complexity, and promote code reuse by identifying existing code that provides desired functionality.
  • Predictive testing – Predict the rate at which defects will be fixed and the injection rate of defects, identify the defects most likely to cause escalations in production, and determine the root cause of failed tests.
  • Predictive operations - Identifying gaps between end-user actions and the workflows that your tests are covering, reduce the likelihood of escaped defects, and link customer defects with user requirements.