How is QA boosting its performance with AI adoption?

An interesting and futuristic piece on Artificial Intelligence (AI) has stated, ‘because these AI systems don’t actually comprehend the underlying logic of what they do, teaching them to do anything else, even if it’s pretty similar — like, say, recognizing specific emotions — means training them all over again from scratch. Once an algorithm is trained, it’s done, we can’t update it anymore.’ Scientists and researchers are working relentlessly to improvise the performance of AI and take it to the next level to facilitate various businesses.

The article further mentions, ‘For years, scientists have been trying to figure out how to work around the 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.’ Along with multiple strides in other business zones, AI has been a major enabler in the software development cycle. Especially, with software development and testing getting complicated and the delivery time getting shorter, AI is expected to bring substantial value for development teams.

Foresight for Software Development and Testing with AI

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.

Referring to the contributions by AI to the work process and within the working scenario, Svetlana Sicular, Research Vice President at Gartner states, “Many 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.”

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.

Mike Rollings, Research Vice President at Gartner, says, “AI 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.”

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.

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.

What QA expects from AI Adoption?

Practically, the apparent expectation would be to reduce the testing lifecycle, make it shorter, and smarter. As per a recent article, ‘Gartner 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.’ It further mentions, ‘AI-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.’

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.

• AI is expected to boost Accuracy

Similar to automation, probably notches above, when it comes to accuracy, the expectations from AI are paramount. It is mostly the key reason for enterprises who take the strategic decision to leverage AI platforms and further invest in the initiatives. Testing can be done effectively when accurate information is captured and the test data is further leveraged to automate software tests. AI platforms are expected to scrupulously generate accurate data that is referable and resourceful.

• AI is expected to maximize overall Test Coverage

AI platforms are expected to expand the overall length and scope of the testing for business applications, and in the process enhance quality of the software. The process has to look back into the data files and data tables to understand the application’s behaviour and accordingly plan the test cases. Ultimately, it will help to maximize the depth of the testing activity. This will definitely be an enabling factor for developers and testers, by boosting confidence levels to get the product within the consumer zone.

• AI must ensure faster turnaround

Resolving issues with an application post the launch can incur efforts as well as costs, which will also kill time and extend the time span for getting the application to the users. With AI-enabled automation tools, testers and developers would be notified well in advance about the flaws and glitches. This will not only save costs, but also pace up the time-to-market. Ultimately, every business needs speed, but ensuring quality is something that they strive to achieve.

Cigniti’s Quality Engineering specialists and thought leaders, leveraging the collective years of experience of serving and solving the test engineering problems of global businesses, after extensive research, have come up with BlueSwan.

Cigniti’s BlueSwan™ is a next generation Proprietary Testing platform. Aligning with the needs of the market in the digital era, BlueSwan also is Cigniti’s enabler towards being the leader in independent software testing services. It also fortifies our IP led software testing services approach along with the Cigniti 4.0 services and Domain centric approach for offering world class testing services to business across the different verticals. BlueSwan™ helps accelerate Quality Engineering for the Digital Enterprises.