{"id":12822,"date":"2018-09-03T18:48:31","date_gmt":"2018-09-03T13:18:31","guid":{"rendered":"https:\/\/cigniti.com\/blog\/?p=12822"},"modified":"2018-09-17T20:13:11","modified_gmt":"2018-09-17T14:43:11","slug":"quality-assurance-performance-with-ai-adoption","status":"publish","type":"post","link":"https:\/\/www.cigniti.com\/blog\/quality-assurance-performance-with-ai-adoption\/","title":{"rendered":"How is QA boosting its performance with AI adoption?"},"content":{"rendered":"

An interesting and futuristic piece on Artificial Intelligence (AI) has stated, \u2018because these AI systems don\u2019t actually comprehend the underlying logic of what they do, teaching them to do anything else, even if it\u2019s pretty similar \u2014 like, say, recognizing specific emotions\u00a0<\/strong>\u2014 means training them all over again from scratch.\u00a0Once an algorithm is trained, it\u2019s done, we can\u2019t update it anymore.\u2019 Scientists and researchers are working relentlessly to improvise the performance of AI and take it to the next level to facilitate various businesses.<\/p>\n

The article<\/a> further mentions, \u2018For years, scientists\u00a0have been trying to figure out how to work around\u00a0the problem. If they succeed, AI systems would be able to learn from a new set of training data without overwriting most of what they already knew in the process.\u2019 Along with multiple strides in other business zones, AI has been a major enabler in the software development cycle<\/a>. Especially, with software development and testing getting complicated and the delivery time getting shorter, AI is expected to bring substantial value for development teams.<\/p>\n

Foresight for Software Development and Testing with AI<\/h2>\n

Software applications and systems are being launched at breakneck speed to ensure that the brand and the business sustains in the competing market scenario. Hence, the need to develop faster and test smarter grows each day. Releases are expected every week and updates can happen even more frequently in such a scenario. Ultimately, a lot depends on how AI evolves to support Software Testing.<\/p>\n

Referring to the contributions by AI to the work process and within the working scenario, Svetlana Sicular, Research Vice President at Gartner states, \u201cMany significant innovations in the past have been associated with a transition period of temporary job loss, followed by recovery, then business transformation and AI will likely follow this route. AI will improve the productivity of many jobs, eliminating millions of middle- and low-level positions, but also creating millions more new positions of highly skilled, management and even the entry-level and low-skilled variety.\u201d<\/p>\n

The role and contribution of AI is still being defined and continues to emerge. Nevertheless, when it comes to testing, AI gives the added scope for testers to go beyond the traditional mode of testing and adopt AI-enabled automation platforms more rigorously and with much more ease.<\/p>\n

Mike Rollings, Research Vice President at Gartner, says, \u201cAI can take on repetitive and mundane tasks, freeing up humans for other activities, but the symbiosis of humans with AI will be more nuanced and will require reinvestment and reinvention instead of simply automating existing practices. Rather than have a machine replicating the steps that a human performs to reach a particular judgment, the entire decision process can be refactored to use the relative strengths and weaknesses of both machine and human to maximize value generation and redistribute decision making to increase agility.\u201d<\/p>\n

A practical approach will be when AI-powered continuous testing platforms are able to change controls as per the requirements and constantly update the algorithms with any miniscule indication or requirement. Especially, with automation testing, AI is being leveraged, where testers pre-train controls and create a technical map to identify the controls and label them. With identification of frequently used controls, testers can even create a hierarchy of the controls to accelerate and streamline the testing process.<\/p>\n

These are some expected results of implementing AI within the testing cycle, but what lies ahead is something that QA and Software Testing needs from AI implementation<\/a>.<\/p>\n

What QA expects from AI Adoption?<\/h2>\n

Practically, the apparent expectation would be to reduce the testing lifecycle, make it shorter, and smarter. As per a recent article<\/a>, \u2018Gartner believes strong growth (for AI) will appear in the customer experience sector while enterprise players experiment with AI and offshoot technology, such as deep learning, neural networking, and machine learning software.\u2019 It further mentions, \u2018AI-based agents account for roughly 46 percent of global AI-derived business value in 2018. However, this is expected to slide to 26 percent by 2022 as enterprise players invest further in more sophisticated solutions offered by AI.\u2019<\/p>\n

With these expectations and more, AI solutions and tools are being worked on to enable self-learning and self-induced evolution. It will ultimately result in better automation and seamless testing lifecycle.<\/p>\n