OnPoint xChange: How do Cloud, DevOps, and AI/ML Impact the Customer Experience?

OnPoint xChange - How do Cloud, DevOps, and AI/ML Impact the Customer Experience?


User expectations are soaring daily with the increased use of the web and mobile platforms to deliver digital services. As a result, design, architecture, and application development timelines are shrinking using Cloud, Software-as-a-Service (SaaS), DevOps, and Off-the-Shelf technologies. While Security and Compliance remain the core concerns, the need to deliver defect-free products (products that deliver frictionless services to users) and services at the speed of business remains a high priority for Agency CIOs and Product Owners.


Mr. Vinay Shirke, CIO at ASSYST, joins OnPoint xChange to shed light on Product Quality and explain how his team innovates and delivers significant value to Federal Government Customers.


  How concerned should application and product owners be about product quality these days?


Vinay Shirke: In my opinion, there is no better time than now. In today's world, the success of a product or a service is measured in terms of the overall customer experience. Therefore, the quality of a product or service is a significant factor in the overall customer experience. Secondly, there is tremendous pressure on IT organizations to deliver IT solutions and services at the speed of business. Therefore, it becomes imperative for IT organizations to manage and orchestrate their distributed workforce across the globe to collaboratively deliver quality products and services that keep up with the business demand to be competitive in the marketplace.


  Can you be more specific about what has changed in the field?


Vinay ShirkeTraditional software development cycles gave a prominent role to testing and quality assurance. The metrics were the project's overall success, timely delivery, and remaining within the budget with no compromise on agreed quality. As I mentioned earlier, there is tremendous pressure on IT organizations to deliver at the speed of business, and to meet that demand; IT organizations must constantly innovate. In recent years, the delivery cycle times have reduced considerably with the infusion of multi-cloud, software-as-a-service (SaaS). In addition, the adoption of CI/CD and DevOps, but the complexity has undoubtedly increased due to processes, tools, and integration, thus creating a need for increasing the footprint of automation into the software development cycle to shorten the delivery cycle further and enhance the quality of the products. To produce defect-free software within the short delivery cycles, IT organizations have adopted Artificial Intelligence and Machine Learning (AI/ML).



  Can you explain why you would use AI and ML in product quality testing?


Vinay ShirkeExcellent question. AI/ML plays a critical role in accelerating the overall efficiency of DevOps by changing how teams work to develop, deliver, deploy, and test applications for improved quality, performance, security, and operations. In our experience, both automated regression and AI/ML-driven tests are critical for delivering quality products to production. For example, an automation regression test helps verify the application's functionality; however, it still leaves a gap in test coverage from User Interface (UI) verification. Integration of AI/ML test with automated regression test has helped cover the gap in the automated regression test and enhanced the quality of the product.


Take the case of visual UI testing. Traditionally, this has been a manual, time-consuming, and error-prone process done much earlier in product testing. Due to the compressed delivery cycles, it's almost impossible to include the manual process of UI testing. Automated regression test provides good coverage of functional testing; however, it does not address the visual UI test suitable for handling color, shape, size, and object location. As a result, visual UI testing generally is not part of the CI/CD pipeline process, which risks product quality and unsatisfactory user experience. By integrating AI/ML-based visual, automated UI testing into the CI/CD pipeline, you mitigate the risk, significantly enhance the product quality, and still meet the compressed delivery schedule. A few other tangible benefits include improved team productivity.


  How can Government agencies take advantage of this?


Vinay ShirkeDepending on their business needs, agencies have used testing as part of DME contracts or procured services via stand-alone testing or IV&V Contracts. However, agencies can encourage product managers to consider product testing an overarching theme and keep end-user expectations at the core. In addition, business owners can consider using AI/ML for automated defect detection and response, measurable over the product's entire lifecycle. AI/ML creates an opportunity to shift from low-value to high-value work.


  Can you share more details about ASSYST's offerings in this area?


Vinay ShirkeSure. We prepare our customers for an Integrated Digital Workplace and embrace the trends unfolding around the Future of Work. The ASSYST Green Accelerator Program's FutureOps serves as a foundation for every transformation initiative we take on. First, we understand the Agency's mission and, within a specific program, the scope and relevance of automation and the impact it can create. We then chart a strategic roadmap to adopt the automation program with tactical milestones. The FutureOps framework includes templates, use cases, an AI model library, a code library of common actions, user enablement tools, and metrics. In addition, the test solution framework takes advantage of Cloud services and can be infused into any application or data environment.


Vinay Shirke, CIO at ASSYST Inc. | LinkedIn



  ASSYST's Green Accelerator FutureOps program enables government agencies to adopt innovative solution capabilities through automation and prepare for the future of work. It includes a use case catalog, reusable components, common coding standards, talent profile toolkits, learning tools, a library of algorithms and automation, and a refactoring toolset to adopt the next generation of technology rapidly. Our implementation approach helps customers innovate and automate their business processes, reduce cost redundancy, decrease technology overhead, and deliver the highest customer experience.


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