For many organizations, testing services remain a secondary concern when setting strategic IT goals or embarking on transformation projects. However, as more organizations accelerate their journeys toward the As-a-Service Economy, it will be more important than ever to advance the sophistication of testing capabilities. Thus, these discussions on the future of testing services have to move beyond simply advocating Agile and DevOps methodologies and be brought into line with cloud-based engagement models and the arrival of Intelligent Automation. HfS had the opportunity to sit down with Accenture’s testing leadership team at a recent client event and discuss how the notion of Intelligent Automation will shape the future.
Accenture’s key message was twofold: First, organizations should not just look at test automation in isolation but embed it into their overarching strategy on Intelligent Automation that is increasingly cutting across all towers. Second, organizations should explore cognitive computing as a lever to move beyond traditional ways of engagement. At the heart of driving automation across organizational boundaries is Accenture’s artificial intelligence engine, which provides an architecture abstraction layer for interacting with various automation approaches such as natural language processing (NLP) and machine learning. Thus, the underlying components can be swapped out according to client preferences or as new solutions become available, leveraging a broad automation ecosystem. Extending this approach, Accenture advocates thinking beyond just cost to embrace an open innovation mindset. However, the reality of the current adoption of this new paradigm became tangible when the audience was polled about their progress with test automation and whether they had started to explore cognitive and predictive capabilities: while some had started with test automation, none had yet explored either cognitive or predictive tools as part of their testing strategy.
Against this background, Accenture executives demonstrated how cognitive computing could help organizations align their testing capabilities with the fast changing requirements of moving toward the As-a-Service Economy. For instance, how Big Data based log operational analysis can help to prevent common and repetitive errors at source. Similarly, another demonstration showed how a virtual ticket-solving agent is leveraging NLP and machine learning to discover and resolve similar yet co-related incidents. Lastly, a knowledge search agent was demonstrated to guide testers through application specific domain information and data by creating a knowledge graph with a view to accelerate guided problem resolution and support.
In order to advance testing services toward a vision of continuous delivery it is important to link test automation with the broader notion of Intelligent Automation in order to maximize the opportunity for innovation. However, the market’s limited progress to date with test automation is a reminder that this is not a journey that will be completed overnight. Today’s demonstrations of the power of cognitive computing to support the effectiveness of testing is a good start but it will take securing access to new types and depths of data to bring this into the mainstream and to help deliver the new business models associated with the As-a-Service Economy.
HfS will assess the progress on this journey with Blueprints on Testing-as-a-Service as well as Intelligent Automation in the second half of 2016.
Service Provider : Accenture