People are fascinated by AI and ChatGPT and are looking for better ways to use it. In the tech space, some people are discussing how these tools aretransforming software and testing, but many overlook a key point: AI in software testing is not new. Long before ChatGPT, teams were already using AI to improve automated testing, increase accuracy, and reduce manual effort.
Artificial intelligence (AI) has been portrayed in pop culture as a technological threat for decades. From HAL in 2001: A Space Odyssey to Skynet in The Terminator and VIKKI in I, Robot, AI often appears as the villain in our imagined future.
These portrayals reflect a deeper fear: that technology will replace humans. AI-powered systems are already taking over tasks once handled by factory workers and call centers, and increasingly by knowledge workers, including those in software development and testing.
Still, for much of my career, many people listed me as an anti-automation person. I find this interesting because I use various automation tools to assist me in my testing work, even if I don’t use trending name-brand tools. The reason is simple – those tools often didn't do what I needed without a lot of extra work.
The Rise of AI in Software Testing
I am wary of the new, buzzword-laden solution people hail as the next great thing to improve the software world. Early automation tools that did record-and-playback and nothing else were similarly acclaimed. The industry also praised the next several test automation tools that fixed or avoided the problems of earlier tools.
Before I go all-in on a revolutionary tool or approach, I want to see real, repeatable evidence that it works as described. I have seen too many people, teams, and companies burned by trusting initial advertising.
The cool, new, attention-grabbing thing is ChatGPT and AI in software testing. So, let’s talk about it.
Understanding AI Software Testing and ChatGPT
AI is powered by three key elements: large volumes of data, significant computing power, and an underlying model determining how the AI learns. This model drives the learning algorithms and processes the data, generating results.
Most software professionals already interact with AI through everyday tools. For example, search engines may not feel like artificial intelligence, but their ability to refine results based on repeated queries is a common form of AI in software testing workflows. The more often users search using similar terms and click on specific results, the smarter the tool becomes at anticipating intent.
ChatGPT, a generative AI tool, takes things a step further. Instead of simply retrieving results based on search keywords, it generates original responses to user prompts. This allows software teams to:
- Research unfamiliar topics to build technical understanding
- Outline blog content ideas or marketing drafts
- Debugging and explaining code for internal projects
- Brainstorm solutions or approaches to complex problems
However, generative AI tools like ChatGPT have limitations. The results may be reasonably accurate or completely incorrect, depending on the quality of the training data and the phrasing of the prompt.
What does that tell us? We should never use ChatGPT responses without proper subject-matter experts weighing in on their relevance and accuracy. The results of any machine learning tool will only be as helpful as the data we feed it. Any tool can give valuable or worthless results based on how it learns, what data it consumes, and the requests people ask.
Coding and technical answers may give you starting points to consider. As a large language model, ChatGPT can combine pieces from various sources into a very confident – but occasionally very wrong – response. It is risky to rely on such a guide without other sources to verify code or confirm the response.
How AI Improves Software Testing
Artificial intelligence brings measurable value to software testing when used with clear intent and a smart application. By analyzing code and evaluating possible scenarios, AI can help generate test cases and optimize test coverage. It can also identify redundant or unnecessary tests and remove them to streamline the testing process.
AI also supports automated test execution by creating scripts that simulate user behavior. These scripts interact with the system under test, reducing manual effort and increasing the speed and accuracy of test cycles. This step reduces manual effort and increases testing speed and accuracy. It can then monitor and analyze test results to identify patterns and trends that help improve the test suite and look for potential problem areas that might arise with different data or conditions.
In addition, AI tools can monitor and analyze test results, identifying patterns and potential problem areas. This helps teams address issues early, improve their test suites, and catch defects more effectively.
AI can also support automated test execution by generating scripts that simulate real user behavior across different scenarios. This helps teams improve test accuracy and efficiency while reducing manual effort. With clearer patterns and insights, developers can act faster, resulting in fewer escaped defects and stronger overall software quality.
Evolving AI Tools in Automated Software Testing
A growing number of AI tools now use machine learning and natural language processing to support better software testing. These solutions improve everything from test creation to maintenance, making testing faster, smarter, and less error-prone.
Here are a few notable tools currently used to assist in AI-powered software testing:
UiPath
UiPath originally built automation libraries. T
hey expanded to a desktop tool-building test automation and expanded into robotic process automation (RPA), including an end-to-end RPA platform.
Their business automation platform offering combines process and task mining using pattern recognition to build out recommendations and suggestions based on user inputs. It also has a natural language capability native to the tool.
Tricentis Testim
Testim began as a mobile testing tool provider using a “low code” model to test mobile applications. It records the user flows entered, and the more a user works with it, the more it recognizes repeated patterns and offers suggestions. It has a visual editor to use for test creation, focusing primarily on mobile apps, but you can use it reasonably well with web projects. Tricentis acquired Testim in 2022.
askUI
Started in 2021, askUI positions itself as a platform capable of automating everything from web interfaces to native desktop apps. It can simulate human actions by identifying any visible element on the screen without relying on object IDs. askUI also supports cross-device automation, such as workflows involving two-factor authentication steps.
Mabl
Mabl is another low-code automation tool that offers features from API testing to auto-healing. It also uses smart element locators you can use in different frameworks, which can lead to more powerful tests.
Virtuoso
Virtuoso uses a natural language format to describe tests. It has an AI-powered self-healing feature intended to reduce flakiness in tests. For example, if a class or classpath has been changed, it will change the code to accommodate the change. It uses a low-code approach, so onboarding is easy. There are loads of technical integrations and support for scripts.
The Future of AI-Driven Software Testing
Several tools on the market already offer AI-driven testing capabilities, including others not covered here, such as Applitools and Qyrus. The examples in this blog represent just a few of the growing number of platforms designed to support smarter, more efficient software testing.
At a glance, some of these technologies may still feel early in development. But the pattern recognition and decision-making features many of them offer are already fairly mature and continuing to evolve rapidly. The real challenge often lies not in the technology itself, but in knowing how to apply it effectively within your environment.
For organizations with a lower risk tolerance, it may make sense to wait until AI testing solutions become more standardized. Others may benefit from hands-on experimentation or even developing their own machine learning frameworks for test automation.
Patience and pragmatic analysis are good starting points. Learn and understand what you want to do and why. Learn how to apply the tools to that end. Be patient and learn what you want and how to do it. You will still have issues when you, your environment, and your toolset are ready. There may be problems in production. However, as the issues dwindle after the first few encounters, it becomes mundane, and the glossy newness fades. We use it because it works every time, without question.
Common Questions and Answers for AI in Software Testing
What is the role of AI in software testing?
AI supports software testing by generating test cases, optimizing test coverage, detecting patterns in test failures, and automating repetitive tasks. It helps QA teams test more efficiently and identify issues earlier.
How does AI improve test automation?
AI enhances test automation by analyzing user behavior, identifying high-risk areas in the application, and dynamically adapting test scripts to changes in the codebase. This leads to faster, more reliable releases.
What are some common AI-powered testing tools?
Popular tools include UiPath, Testim, askUI, mabl, and Virtuoso. Each offers features like low-code test creation, visual recognition, and natural language scripting.
Is AI in testing mature enough for enterprise adoption?
Yes, many AI testing tools have mature core capabilities such as pattern recognition and predictive analytics. However, success depends on how well they’re implemented and aligned with your organization’s needs.
Should we build or buy an AI testing tool?
If your team has specific needs or operates in a highly regulated environment, building may offer more control. Otherwise, commercial tools can accelerate adoption with lower upfront investment.
[cta bg="dark-blue" button="View Webinar" link="https://centricconsulting.com/chatgpt_and_ai_revolution_wbnr/?source=website"] Need help determining how ChatGPT and Artificial Intelligence should fit in your organization? Make sure you aren’t left behind as companies across the globe continue to embrace these productivity enhancing technologies. In this on-demand webinar, our Artificial Intelligence expert provides an executive’s guide to what leaders need to know about adopting ChatGPT and AI in the workplace. [/cta]
