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Leveraging Web Analytics for Risk Based Testing

Web analytics involves collection, measurement and analysis of metrics related to end user activity on a web site. Modern web analytics systems like Omniture, Tealeaf, and Google Analytics, etc. offer powerful tools and infrastructure for measuring and analyzing website traffic patterns and usage. These web analytics systems provide data on various traffic and end user statistics including the number of visitors, page views, average session duration, popular pages, common user workflows / click paths, etc. Historically, these metrics were primarily utilized for market and business research. Web analytics systems have grown fairly sophisticated over the last decade. The current generation systems can capture more granular and finer statistics for accurate “visualization” of end user behavior patterns and interactions with a website. These detailed metrics are extremely useful for proactive web application optimization. Web analytics systems are increasingly being utilized by business as well as technical stakeholders for managing, measuring, analyzing and enhancing end user experience.

The power of web analytics can be used by testing teams for understanding end user behavior and patterns. What business workflows and their underlying test scenarios are most widely used and how they are traversed – becomes transparent. The test cases constituting the paths are now measurable and the efforts quantifiable. Automatically we are building in a risk based knowledge about what is important (by usage) to the end user. We know what is at risk if it is not tested, or tested to a higher level of validation. These statistics can help the test team design robust, accurate and optimized regression suites. These will ensure a risk based approach to testing. So in the unlikely event that your project has decreased testing time, you can state the facts about the risk to the business, based in web statistics, for tests not being executed.

The number of test cases from a web application explodes due to some of the following:

  • Numerous application (Entry – Exit) paths are possible
  • Large number of end users and different behavior received from unknown origins
  • Explosion in browser/OS combination
  • Mobile browsers

Web statistics will tell you where the risks are and when crunch is on and the time to test is reduced. As a result, you can confidently state the risks to the business.


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