• Nikhil Menon, QA Engineer at Baton Systems


• 11.01.2024 12:15 pm
#AI #AItools #transformation #fintech

Anybody who has worked in software development can faithfully attest to the fact that quality assurance (QA) testing, and the functional testing cycles that are required until the desired quality standards are consistently met, can prove to be extremely time-consuming. As an organisation that is trusted by some of the world’s largest banks to securely process and settle billions of dollars of cash and securities everyday, we are starkly aware of the responsibility that sits squarely on our shoulders to ensure the solutions we provide meet the rigorous standards required.

This quest for quality has got the industry looking to ways in which it can increase the productivity and efficiency of functional testing processes. One potential area – the current belle of the ball of course – is generative artificial intelligence (AI)! For all the bluster around this exciting technology that has been talked about extensively in the press, we are witnessing advancements which could prove advantageous in providing an automated means of writing robust and efficient test scripts.

There are solutions available, such as Postman’s recently introduced generative AI assistant, Postbot, which are designed to fit into existing architecture and workflows – something that cannot be underestimated when looking to apply new technology to established processes. As an example, it is capable of generating test scripts from simple, yet precise queries in English. This is not meant to be an advertisement for Postman (though we have enjoyed working with the firm!), but a recognition that there are functional examples already of generative AI being used to assist vital processes.

Take what we do as an example, a large proportion of the functional tests executed for our solutions are for an extensive set of basic test cases. Here we might want to check that a collateral movement instruction will be executed on a set date, in this instance we might want to give Postbot a query to check that the system knows that the trade date must not be later than the value date.

Generating basic test scripts was previously a relatively manual step, however by using Postbot  we’ve been able to automate the generation of test scripts and then run the test at the click of a button. Freeing up more time to focus on identifying and working on more complex test cases, the team is now able to be more innovative and productive. In fact, using Postbot has actually increased the test coverage we can achieve.

This is a direct example of how using AI is not about replacing the human touch, but simply arming people with the tools to work more effectively and achieve more at an accelerated rate. All of which ultimately benefits clients because they get an even more rigorously tested solution deployed in an expedited time frame.

One of the key benefits in early adoption when working with generative AI tools in the beta stage is that it gives firms the ability to provide feedback to the developers, which will hopefully assist them in improving and tailoring solutions to fit the industry’s needs.

The use of generative AI across industries is still in its early stages, but we’re already experiencing the transformative power that comes from being able to use these tools to accelerate existing workflows. With time, perhaps the early improvements that have been seen in the QA testing process from the application of AI will not only get stronger, but also be extended to other areas of the business and across a broad range of industries as generative AI continues to mature.