Why AI is the only answer to legacy manual software testing

By Adil Mohammed, CEO and Founder, Virtuoso
Until recently not feasible, the increasing availability of AI, ML and NLP is not only making test automation smarter, but also more accessible

Every business is caught between a demand to deliver things quickly and make sure they meet quality expectations. It doesn’t matter whether they make furniture, cars, food or software, companies have to meet the demands of customers that want fast access to great products.

This puts a squeeze on development. First mover advantage counts for a lot, and many companies look for ways to accelerate their go-to-market. That can mean certain areas, such as quality assurance and testing, are cut back to the bare minimum: what’s required by law, for example, or what’s achievable by stretched teams within a curtailed timeframe.

The inherent tension of software development

Nowhere is this more apparent than in software development. There is an inherent tension in software development, as products need to be shipped as quickly as possible, with as few bugs as possible. Traditionally, that means testing is a necessary evil: the solution needs to work, but good QA takes time. A team can only work on one part per person at a time. Therefore, a complicated piece of software, or a large website, might take a while to test properly. Add in that updates might be required every couple of weeks, and businesses are left with either having a huge, and costly, QA team, or only testing the bare minimum.

With testers (and indeed much software-related talent) in such high demand, it is unsurprising that many feel they have to opt for the latter.

But is manual testing really the only way to ensure you ship quality software?

Automating tests to scale speed and quality

Not necessarily. Many businesses combine automation with manual testing to speed up the process where possible. This isn’t a new phenomenon: tools like Selenium have been around for a while. Designed to empower testers to keep up with demand, these types of coded automation were supposed to provide test scripts in common coding languages, running on a variety of systems and browsers.

In theory, this is ideal: much of the testing can be covered by coded automation, with humans around to review the outputs and provide a final QA overview. Yet as with most theories, reality was a different matter. Each test environment needed to be manually set up, which required resources at the onboarding stage. Then, as the automated testing was itself coded, if the tests met dynamic or unusual data problems could occur, requiring more manual input to fix problems and get them running.

Finally, there was a limit to how many tests coded automation could run, with the number dropping even more when it had to operate cross-browser. So while there might be some scale, the amount of manual support required often negated any benefits.

From coded to codeless

Is there another alternative? Yes – codeless test automation. Until recently not feasible, the increasing availability of artificial intelligence, machine learning and natural language programming is not only making test automation smarter, but also it more accessible to a wide variety of organisations of all sizes.

By using AI, ML and NLP, codeless test automation platforms provide anyone with the ability to generate tests. For instance, NLP allows simple commands like “click ‘add to bag’” and “hover over ‘my account’” to be translated by RPA so the testing software knows exactly what it needs to do. AI and ML keep tests from breaking when they encounter dynamic data, and tests can continuously self-heal so there’s significantly less test maintenance.

QA teams can save an incredible amount of time as they no longer need to have coding knowledge. That time that they previously spent meticulously coding can now be dedicated to more important tasks. Test re-usability is easier, as team members do not need to write new tests for different scenarios. They can simply copy and adjust previous steps with a few words of plain English. The number of tests an individual can oversee grows significantly, as teams are able to truly oversee tests and check overall QA, rather than dive into every test every time there is non-standard data.

Test quality has also gone up with codeless automation thanks to advancements in AI and ML. Tests are no longer as brittle due to the way AI helps them to self-heal and how selectors can be intelligently identified. Codeless test automation tools are also more capable of scaling, as many can run tests in parallel and across a multitude of browsers and devices with cross-browser testing.

Allowing organisations of all sizes to ship faster and better

This means that these tools can easily be used at an enterprise level without any strain on performance. It also results in smaller organisations being able to develop, test and scale more complex solutions, without worrying about a resulting drop in quality.

By using AI, ML and NLP-powered codeless test automation, software developers can achieve that holy grail of rapid delivery and high quality. At a time when first-to-market has a huge advantage of the competition, being able to deliver both can be a significant contributor to broader business success.


Featured Articles

Virgin Atlantic accelerates AI transformation with Amperity

Leading enterprise customer data platform will help Virgin Atlantic leverage a data-driven strategy to deliver highly personalised customer experiences

Sustainability LIVE: Event for AI leaders

Featuring experts from companies including Microsoft, Google, AWS, Meta and Tech Mahindra, Sustainability LIVE offers a number of sessions for tech leaders

VMware and NVIDIA AI Foundation unlocks business potential

VMware and NVIDIA have partnered in a private AI foundation with a wide range of offerings, to aid businesses to better adopt and customise AI models

TimeAI Summit Oct 2023 to unite tech giants and visionaries


MIT suggest generative AI is democratising AI access

Machine Learning

How AI could help airlines mitigate contrail climate impact

Machine Learning