Predictive analytics for DevOps

Predictive analytics for DevOps means automating and measuring ‘everything’ are two fundamental DevOps principles for enabling to deliver features and fixes faster. There are good metrics for measuring the speed of value deliveries. Deployment frequency indicates how fast a team can deliver software to production. Lead time metrics provide useful insights on how fast a team can translate requirements into code and deploy them to users. However, looking at the speedometer alone is not enough. It’s easy to deliver something fast but much harder to deliver fast but with quality. Therefore DevOps teams need to build quality in and invest in automating integration, testing and deployment to obtain continuous feedback on functional and technical quality.

So how do you know if you have “quality built in”? In Qentinel Pace, predictive analytics for DevOps uses data from several sources and uses this timeseries data to model the whole software development stack. AI and data is used to find out what are the most influencial predictors of the software quality, what quality prediction is and confidence level of the prediction.

 

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How does it work?

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What sources of data can be utilized?

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How to utilize results?

Value Creation Model gives meaning to metrics

DevOps accelerates the pace of value deliveries by allowing the teams to deliver software frequently to customers without compromising quality. Measuring ‘everything’ is one fundamental principle in DevOps but the metrics need to be practical and actionable. Value Creation Model provides an approach to present the assumed causalities among the metrics and link them as leading indicators to the goals of DevOps.

The metrics can be organized into metric trees that allow calculating indices that show the overall status of the most important focus areas. We can leverage the DevOps Value Creation Model also for developing predictive analytics solutions. This presented approach, Quality Intelligence® for DevOps, helps the DevOps teams to make more data-driven release decisions and find the right levers to turn to produce more value with software, faster.

Read more from whitepaper How DevOps Creates Value and How to Measure It.

Qentinel Pace has Quality intelligence® for DevOps ready to collect data from several common tools,

  • Qentinel Pace itself in terms of all test data it provides,
  • Code quality tools,
  • application life-cycle management (ALM) tools such as Jira and Azure DevOps, Git,
  • help desk and issue management tools such as ZenDesk, and
  • basically any tools that provide an API for the required measurement data. In addition, it is possible to define manual data sources for manual data input.

Predictive analytics for DevOps answers every day operative questions on quality and speed:

  • Release to production decision: is this release as good quality as now in production?
  • Prioritizing actions to avoid service outages
  • Selecting improvement actions to maintain good user experience
  • Enable acting proactively to prepare for problems in digital services functionality or performance
  • Find meaningful points where to focus on when you want to improve your productivity
  • Estimate your sw supplier productivity, is it improving

AI utilizes collected data to give  predictions of quality

Watch AI Architect Henri Terho explaining our approach for predictive analytics for DevOps.

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