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Cognizant's Testing Bets: Crowdtesting, Communities & Machine Learning


Cognizant recently hosted NelsonHall in its mobile center of expertise in Grenoble, France, to discuss how it is expanding its Quality Engineering & Assurance (QE&A) practice.

QE&A is a very significant practice within Cognizant - NelsonHall estimates that it represents over 14% of Cognizant’s global headcount. Unsurprisingly, digital, DevOps, and agile are priorities: accordingly, QE&A is creating platforms (combinations of open source software, proprietary accelerators, and COTS), and reskilling its delivery personnel. This is a significant shift, and one with significant side effects: QE&A has been expanding its partnership ecosystem, adding niche digital skills from smaller ISVs, and also gaining access to skills (Mirabeau, Idea Couture, and ReD Associates) that enhance its provision of UX testing services.

Based on the premise that the future of usability testing lies in customer-based interviews complemented by niche software capabilities, QE&A is making several bets within UX testing. It is investing in its fastest offering, which has evolved from a portal offering, through a service catalog giving access to testing services and testing software, with the addition of crowdtesting services. fastest sources testers both internally (within Cognizant, from QE&A and more broadly from the larger organization), and externally. We will be commenting further on the features of fastest and how it compares with the likes of Applause, passbrains, BugFinders, Testbirds, Qualitrix, or QA InfoTech, and Wipro.

Outside of UX testing, Cognizant QE&A is currently launching its QA Hub with the intention of creating a testing online community where it shares some of its accelerators, provides demos of COTS (HPE and CA), and shares some of its KM. QA Hub exists under two versions: a client-dedicated one, and one open to all organizations. QE&A is intending to build for software testing a community that will be similar to GitHub and gain attention from clients, as well as increase further the profile of QE&A as an employer. Along this line, QE&A has released to the open source community its Cognizant Intelligent Test Scripter (CITS). Here again the objectives are gaining visibility among potential employees, and among the open source community.

QE&A’s other major bet is around co-innovation:

  • QE&A is driving its ML effort, making use of a repository of ~30k defects and aiming to link identified defects and remediation activities
  • With static code analysis ISV, CAST Software, QE&A wants to help clients identify within a given application which modules or lines of code have poor quality, and require attention with further testing.

This is a short-term focus. In the longer term, QE&A wants to get prepared for testing services around IoT, connected equipment/cars, and robots.

QE&A has a contract with an international robotics firm and is using its test centre in Grenoble to test humanoid robots with embedded software, from instruction to the robot to mobile app testing allowing interactions with the robot. QE&A is driving the industrialization of robot testing: QE&A has 20k test scripts and 6k manual test cases. Some of the automation is done thanks to the client’s own robot command and control platform. QE&A is also adding its own automation for API testing, and for mobile app testing.

In envisioning the future of robot and connected device testing, QE&A is finding that testing robots/connected devices/autonomous cars in real-life conditions will not suffice - QE&A believes that only simulation will help recreate test scenarios comprehensively. To some extent, this relates to human safety; robots (as much as autonomous cars) will require testing conditions that manual testing or even crowdtesting cannot recreate. Of course, this is years ahead. For now, UX testing, crowdtesting, and portfolio shift are the company’s main priorities, with several more announcements expected in H2 2017.

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