Release Notes

8 Cognitive Automation
2 The SDLC Opportunity Landscape
Ben Pring, of Cognizant’s Future of Work Division and author of “What to do When Machines do Everything”
likes to frame AI and the future of work in this way: “X + AI”. X is EVERY task that you perform. Your job is to
figure out what X is in your organization or project context, then take the lead on assessing the potential
improvements to that task that AI can provide.
Figure 2 provides an inventory of some of the day-to-day Engineering tasks that you find in many organization’s
SDLC that require brainpower, generally rely on reference data, and are then combined with deep domain
knowledge and prior experience to complete the task at hand. These are just some of examples that span the
SW Development Lifecycle, and given the challenges in your organization, might be considered as candidates
for Cognitive Automation.
Automating any one of these tasks within an organization is likely to involve a heavy lift, because of the
proprietary nature of the data, process, and domain.
Dell EMC Server Validation has been busy experimenting with and implementing Cognitive Automation
solutions in the Testing phases of the SDLC. This next section captures the experiences of several of those
efforts and demonstrates the proprietary nature of the data, processes, and tasks that lead to ‘customizations’
of implementations in this space.
2.1 AI-Assisted User Interface Automation
One of the challenges with established practices of automating Graphical User Interface (GUI) Testing is the
dependence on the Document Object Model (DOM) of the underlying Application Under Test (AUT). When
these elements with the application change, the associated test scripts must change too, resulting in
maintenance costs of existing test scripts.
Figure 2 - Assessing SDLC Opportunities for AI