Phy-gital Roundtable: Breakfast Roundup from Germany and Netherlands

02 May '15 | Debjyoti Paul

German Shoppers: Meet Them in the Fast Lane to Phy-gital

15 January '15 | Ralf Reich

Shoppers Will Share Personal Information (But They Don’t Want to be “Friends”)

15 January '15 | Anil Venkat

Modernize or Perish: Property and Casualty Insurers and IT Solutions

14 January '15 | Manesh Rajendran

Benelux Reaches the Phy-gital Tipping Point: Omnichannel Readiness is Crucial

13 January '15 | Anil Gandharve

The New Omnichannel Dynamic: Finding Core Principles Across Industries

13 January '15 | Debjyoti Paul

Technology does not disrupt business – CIO day 2014 Roundup

02 December '14 | Anshuman Singh

Apple Pay – The Best Is Yet To Come

02 December '14 | Indy Sawhney

Digital transformation is a business transformation enabled by technology

01 December '14 | Amit Varma

3 Stages of FATCA Testing and Quality Assurance

06 October '14 | Raman Suprajarama

3 Reasons why Apple Pay could dominate the payments space

18 September '14 | Gaurav Johri

Beacon of Hope: Serving Growth and Customer Satisfaction

05 August '14 | Debjyoti Paul

The Dos and Don’ts of Emerging Technologies Like iBeacon

30 July '14 | Debjyoti Paul

What You Sold Us On – eCommerce Award Finalist Selections

17 July '14 | Anshuman Singh

3 Steps to Getting Started with Microsoft Azure Cloud Services

04 June '14 | Koushik Ramani

8 Steps to Building a Successful Self Service Portal

03 June '14 | Giridhar LV

Innovation outsourced – a myth or a mirage or a truth staring at us?

13 January '14 | Ramesh Hosahalli

What does a mobile user want?

03 January '14 | Gopikrishna Aravindan

Leveraging Web Analytics for Risk Based Testing

Posted on: 20 October '11

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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  • Manish Grover

    Nice. I think these additional sources will also be great to integrate. Of course more the better. The end objective is to integrate the testing team into product/program strategy, not just as a “testing” point after the fact.

    1. Business impact
    2. Age of feature
    3. Impact or dependency linkages
    4. Knowledge retention ratio of current team

    It will be awesome to build a model around these….DAS…are you listening?