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Leverage the Data in Your QA Tools to Strengthen Testing

A lot of data is captured in the tools you use for product delivery. These insights give you a full picture of the health of your products and focus your tests on the scenarios that matter.

Given the flexibility and customization of the reporting capabilities of your QA tools, you can essentially measure anything that is meaningful to your business and IT management over time. However, this data can also be used to quickly and proactively identify how to focus your limited resources to increase quality.

Your QA tools captures the results of the various QA activities you perform throughout your product delivery process like static code analysis and metrics, unit testing, functional testing, and system testing. Aggregating all of this data together and analyzing it will reveal trends and patterns of known and potential defects and issues that can occur while the product is developing (i.e. defect forecasting).

This analyzed data can also identify the various user and system scenarios that should be tested but are not easily found through common test design practices (e.g. state diagrams, data flow diagrams, use case and business process flows).

Defect forecasting is still a differentiating feature among QA tools despite the capability being available for quite some time. One of the leaders in this space is Parasoft and its Process Intelligence Engine (PIE) that triggers alerts in your workflows based on observations previously made in your delivery process.

Micro Focus offers a Predictive Analytics Tool for its ALM products to enable continuous assessment of pipelines. Other tools in this space do not provide that same level of sophistication but enable static code analysis and continuous source code inspection capabilities that can indicate potential issues without the execution of the code themselves, such as Perforce’s Helix QAC and VisualCodeGrepper.

Our Take

There is significant potential in the use of QA tool data to improve quality in the right areas. While much of the predictive analytics capabilities are bells and whistles today, it will only be a matter of time before we start to see organizations looking to enhance their automation practices and expand test coverage using these capabilities. Focus your tooling selection on addressing your core QA practice until these forecasting capabilities mature in the industry.

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