Markets are evolving at a faster pace than they used to do a decade ago. The pace accelerates everyday driven by customer needs and competition in the industry. The game is fierce and the winner is the one who can deliver the product not just timely but also excels in the quality of the application. The complexity of the software on the other hand plays a significant role in slowing down the delivery process therefore failing to achieve any competitive advantage in the market. QA processes and automation had concentrated for a decade to increase this pace of delivery whilst maintaining the quality. However, industry needs to evolve from automation - with the lessons learned and the challenges of the future it should look beyond just automation.
With the normally slow pace of delivery and ever-increasing complexity in applications and increased demand of the market, many in the industry had come to a harsh realisation that QA has become bottleneck to triumph. If at all agile has contributed – it has changed the attitudes and behaviours of the industry and taught the industry to respond to the change of the market that is to be customer centric and business focussed. The automation still had been time- intensive, never ending expensive affair.
Automation Approach & AI
Automation from its inception has been considered as a budget saviour as it reduces the manual efforts. This approach doesn’t fit to current demands of the market as applications warrant frequent changes and so does the automation tests- increasing the cost and reducing the overall Return on investment. If that is not the case some companies view it as a nice to have substance. In this case they are likely to face difficulties in growth and expansion. Either of these approaches had hindered the evolution of automation into intelligent automation and beyond that an artificially intelligent automation.
Artificial Intelligence driven software quality assurance can be leveraged to solve the problem and accelerate manual testing. Bots could be developed such that they learn the events occurring in your application and its end to end flow. When they have learned enough they could be configured to trigger errors on any unknown circumstances which we would normally call as a defect (an un-expected behaviour or result). More the AI agents learn and develop themselves throughout the testing process, the stability and reliability of an application increases by the day. Which is exactly opposite to the usual automation.
Role of QA
An AI agent works round the clock throughout the week and most importantly, all of this can take place in real time, in the background, quickly any reported unexpected behaviours could be consolidated on live dashboards. QA Engineers then see the full picture and subsequently prioritize further investigations and corrections based on feature relevance to the customer. They will be able to analyse results faster and communicate results to stakeholders more efficiently. The results: instant resolution on issues- contributing to satisfied customer base.
Yes, actual people need to monitor the AI bots and its codebase to ensure its own successful functionality, but overall, the AI bots have ultimately full autonomy to do their job. Currently, they actively test some of the most prestigious apps available on the market. In several areas, they even outperform world class QA teams and leading testing platforms: the use of AI bots could produce many positive results, including a decrease in overall costs, early detection of high-risk areas for regression test, quicker time to market, increased customer satisfaction and increased profitability.
