Q&A with ZAPTEST on how AI is changing the world of software testing and RPA
Software Testing Automation and Robotic Process Automation (RPA) have evolved considerably over the last decade. Both disciplines boost productivity and save time and money. However, thanks to advances in AI, these advantages are being pushed to new levels.
AI is making waves again, primarily because of interest in Generative AI tools like ChatGPT, Claude, and GitHub Copilot. However, that is just one area of AI that has improved Test Automation and RPA.
We sat down with Alex “Zap” Chernyak, the founder of ZAPTEST, a best-in-class enterprise-level software test automation and Robotic Process Automation tool. In this Q&A, Alex shares his thoughts on how Artificial Intelligence has altered the world of software testing and RPA forever.
How has AI affected Test Automation and RPA?
To answer that question, we need to go back a few years to an era when software testing was a manual process. This was before DevOps and Agile methodologies had taken off, and software development life cycles were much slower and less efficient.
In many quarters, manual testing was viewed as something of a necessary evil. Worse still, manual testers were some of the least satisfied employees in software development. The work was tedious and repetitive, and many workers felt invisible unless there was a major problem in production. In other words, this was an ideal use case for the power of automation.
There were lots of similar scenarios in other fields that RPA addressed. Companies were spending resources on repetitive, high-volume tasks that were business-critical but weren’t always the best use of their human capital.
Test Automation and RPA tools moved into these spaces and started delivering results, both in terms of job satisfaction and return on investment. However, both these tools had limitations.
What sort of limitations are you talking about?
Well, the obvious limitations with automation come from the fact that it needs clearly defined, step-by-step instructions. It’s perfect for predictable processes but less suitable for situations that require human cognition and decision-making. Of course, one of the most well-known limitations of RPA is its inability to deal with unstructured data.
However, there were various AI tools that were able to bridge these gaps. So, over the years, vendors gradually added these tools to their products.
Which AI tools are best for RPA?
Again, we need to start by underlining the fact that AI is an umbrella term for a diverse mix of different technologies. Generative AI is grabbing all the headlines lately, but it’s just one narrow application of this set of technologies.
From a Robotic Process Automation perspective, there are a number of tools that have helped the discipline overcome its limitations in terms of unstructured data processing and decision-making. For starters, Optical Character Recognition (OCR) helps with Intelligent Document Processing by interpreting and reading documents and converting them into data that RPA tools can process.
Similarly, Cognitive RPA or Intelligent Automation (AI) means that RPA tools are capable of decision-making. To a large extent, Machine Learning (ML) has made this possible and now allows software testing to solve far more automation testing requirements.
And which AI tools are best for Test Automation?
Just like AI, Test Automation is an umbrella term for a wide variety of different approaches to validating software. So, determining which AI tools are best for the job depends on what job you need to do. So, if we are talking about a comprehensive program of tests, there are a number of points where AI can help. The best way to answer this question is by exploring which testing process AI can solve.
What types of Test Automation can AI help with?
One of the biggest areas where AI helps is by generating test cases. Historically, this has been a very time-consuming part of testing. However, thanks to advances in ML, AI tools can generate comprehensive test cases. The big advantage here is that test cases can be created via Generative AI or codeless tools, which reduces the workload on developers or opens up the world of testing to nontechnical teams.
AI can also analyze code. Again, this is something that Generative AI or LLMs are capable of. The major upside here is speed and accuracy. These methods can help teams detect codes based on algorithmic patterns and uncover bugs that would otherwise go unnoticed by human testers.
What about performance testing?
Performance testing is an essential part of building a robust and reliable product. While an app might work well while under production, it’s hard to tell how it will react when put under the stress of multiple users. Once again, AI can simulate the effect of thousands of users accessing the app at the same time. Verifying in this way can give teams incredibly useful insights into their app’s stability and give them confidence about going live
Are there other important areas that AI helps with?
We can’t talk about AI test automation without referencing CI/CD pipelines. Continuous integration and continuous development are a big part of modern code development. In the past, software used to be released and updated once or twice a year. But these days, consumers demand much more and want bugs fixed ASAP.
AI helps teams take a CI/CD approach by supporting automated testing and code reviews. What’s more, it can automatically fix bugs and provide ML-driven predictive analysis to identify historically problematic areas for programmers to focus on.
Overall, CI/CD means faster and more reliable releases without putting excess pressure on developers.
What advantages can teams unlock by using AI for Test Automation?
AI has extended many of the benefits of AI and RPA and opened up a few new benefits in the process. A few of the most important advantages for developers include:
AI-assisted testing means that projects can be completed very quickly but without compromises on quality. That means faster time to market or quicker updates, which is essential for competing in a crowded market.
Codeless test automation opens up testing to nontechnical employees. The democratization of testing can have a huge impact on entrepreneurship and citizen development because it allows them to build software applications at a lower cost. For development teams, it saves time that can be rotated into other areas.
Reducing costs is a huge benefit. Developer’s salaries have shot up due to market demands. AI code generators and test automation are a genuine antidote to these problems, especially for less well-capitalized teams.
Maintenance is another perennial budgetary concern for developers. Most teams try to keep them to around 20% of the overall development costs. However, quite often, they can dramatically exceed this benchmark. AI-powered test automation lowers these costs with self-healing code.
Finally, AI test automation overcomes the problem of human error and leads to better releases. Of course, these are just some of the more popular benefits.
How do you see the future of Test Automation?
There is no getting away from it: AI-powered code copilots are ground-breaking innovations that are changing the world of Test Automation and software development in general. These AI tools can produce code 100 times quicker than developers, which will cause seismic shifts within the industry.
Any tool that can code, rewrite, and debug code offers immense commercial advantages. However, this technology is so disruptive that it will change how many teams think about software test automation. For vendors, this means a shift away from their traditional value proposition of helping development teams reduce costs. Finding a way to stay relevant is no mean task.
Many software automation vendors also act in a consulting capacity for their clients. A USP of reducing development costs is not as impactful when copilots can generate test automation far quicker than any coder.
Of course, these technologies won’t spell the end for test automation vendors with consulting models. The real question is how can test automation consulting vendors survive in these new conditions.
How can Test Automation vendors survive the advance of AI copilots?
AI will have an impact on many industries, not just Test Automation. The story of the next few years will be about how organizations grapple with these changes. There are a lot of people who are very worried that their job or industry will be destroyed by AI. However, people also made these claims about Robotic Process Automation over the years.
So, while some jobs will be uprooted, it won’t be the end of work. People will retrain and pivot into other fields, while technology will help support many professionals in existing industries. In many ways, this is the same scenario that will face consulting vendors.
The best bet for these teams is to adopt AI-based tools that help them stay competitive while changing their offering to more advanced types of test automation, including software that works across platforms, operation systems, and devices.
The companies who survive are the ones who get out ahead of these technologies and offer them as part of their service.
About ZAPTEST
ZAPTEST is a full-stack Software Automation testing suite that offers cross-platform, cross-device, and cross-application software testing automation and RPA. With a range of innovative features from 1Script Automation, AI and Computer Vision, and WebDriver Integration, ZAPTEST is the perfect tool for any Test Automation use case. What’s more, Enterprise clients get a dedicated full-time ZAP Expert to ensure implementation and ROI are as straightforward as possible.
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