Artificial intelligence (AI) has revolutionized the way we do quality assurance (QA). The immense value that AI brings to optimizing testing processes and enhancing efficiency simply can’t be ignored. Organizations that know how to incorporate AI into their testing will gain a strong competitive edge against their competitors. This article explores in-depth the power of AI in quality assurance and how QA teams can shift from unscalable manual testing to cutting-edge autonomous testing technologies.
AI can perform much more advanced tasks that previously required human cognitive powers, most notably:
Incorporating AI into the QA process takes us one step closer toward the autonomous testing future.
The software testing industry has gradually shifted from Test Automation to Autonomous Software Testing (AUST). This shift was accelerated by the introduction of many groundbreaking AI technologies like ChatGPT. By “autonomous”, we envision a future where tests are completely created, driven, and managed by AI/ML, eliminating the need for human intervention.
Read More: Autonomous Testing: A Complete Guide
Taking a look at the Autonomous Software Testing Model (ASTM) above, you will see that Manual Testing ranks at the lowest position in terms of autonomy, simply because it is 100% driven by humans. Further up the model, we see Automation Testing slowly offloading the repetitive tasks, freeing up the bandwidth of testers so that they can focus on more strategic tasks and high-level planning. Autonomous Testing is the final stage, where computers have full control over the testing process.
Shifting from manual testing to automated testing, then from automated testing to autonomous testing bring a wide array of benefits:
Read More: How to Go from Manual Testing to Automation Testing
According to the State of Software Quality Report 2024, test case generation is the most common application of AI for both manual testing and automation testing, followed by test data generation. You can download the report for the latest insights in the industry.
More than just the implementation of AI into the QA process, AI-led QA is when you leverage AI to automate, optimize, and even guide the testing activities. AI-enabled test techniques can be incorporated into any stage of the Software Testing Life Cycle (STLC), from test case creation to defect detection and analysis. This approach is also known as AI testing.
ChatGPT, a groundbreaking AI recently released by OpenAI, possesses remarkable language capabilities, and has an impressive IQ of 155. QA teams have leveraged its power to achieve “effortless test authoring,” where they simply give the chatbot a detailed instruction on the test case they want to generate and it gives them exactly that, in the programming language they choose. Although there is still room for improvement, chatbots can boost the speed of test authoring in many ways, such as:
Read More: The Potential of AI in Regression Testing
2. Improved Consistency
With human testers, there is always the risk of human error. Either due to fatigue, distraction, perception errors, or even cognitive bias, testers may venture off the standardized path at some point. This is a particularly common issue in manual testing where testers execute test cases and interpret test results in their own way, leading to inconsistency in the process.
With AI in quality assurance, teams can establish a more standardized process. For example, you can have an AI system that automatically scans through the code to identify inconsistencies in your design. AI can also be trained to follow standardized formats and coding best practices so that test script generation becomes more rule-based.
Another interesting application of AI in quality assurance is documentation. You can instruct AI to examine the code, then extract essential details like function and variable names, descriptions, and usage examples. With this information, it can produce comprehensive reports that are user-friendly, saving valuable time and effort that would otherwise be spent on manually constructing the required documentation.
3. AI as a Helpful Assistant
Instead of spending hours scrolling through resources on the internet, testers can now jump straight to ChatGPT to find the answers for any questions that they have. Although concerns have been raised about the validity of these answers, they do offer a fairly decent starting point to further understand the topic at hand. It can save testers a lot of time spent scouring Stack Overflow.
The goal of implementing AI in quality assurance is to overcome the bottlenecks in manual testing. Manual testing is a time-consuming process that is prone to human error. It requires significant effort to write, manage, execute test cases, document the outcomes, and verify results. As software complexity grows, scaling manual testing becomes much more challenging, leading to prolonged testing periods.
Moreover, manual testing can cause delays in identifying and resolving bugs, especially during major software updates or releases. When performed manually, regression testing, which is essential for software maintenance, becomes tedious and counterproductive. The hidden costs associated with training new testers and managing testing environments are also often overlooked.
Automation testing accelerates the process by streamlining repetitive and resource-intensive tasks. As we move closer to autonomous testing, even the common challenges of automation testing can also be addressed.
Read More: Why Now Is the Perfect Time to Transition to Automation Testing
AI is a crucial part of autonomous testing because we want the system to make decisions and evolve on its own, with minimal (and ideally zero) instructions from human testers. A truly autonomous system is a self-governing system that can test other systems intelligently. This intelligence is what separates autonomous testing from automation testing.
Let’s explore the concept of “intelligence” more in-depth. Humans are considered “intelligent” because we can make non-deterministic decisions based on existing information and experience, taking into account a large number of complex underlying factors. Similarly, AI can recognize patterns from billions of data points that they have been trained on to arrive at a final decision.
Without the power of AI, we can only perform automation testing at best. Automation testing generates deterministic results (i.e., the outcome is entirely predictable with the same initial inputs). When we execute a test script for the scenario “Typing valid credentials in the Login feature,” we know exactly the possible results this script will return. In this scenario, we don’t really test, we can only check the system.
With the power of AI, we can expect the system to perform more non-deterministic activities (i.e., the outcome is not entirely predictable with the same initial inputs). For example, we can ask the AI to generate a CSV file consisting of 100 data points with username, password, and email fields that we can use to test the Login feature. The system is now capable of doing much more complex tasks to arrive at results not entirely under our control.
Currently, QA teams have already leveraged AI in their quality assurance process to achieve many exciting feats:
The software testing industry is heading towards autonomy, gradually pushing automation testing into the past. Automation testing used to be the innovative approach, but autonomous testing is what we should truly look towards.
Currently, AI-driven autonomous testing is still only in its infancy, but we can expect its development to follow a continuous upward trend in the near future. AI technology requires time to be fully trained and integrated into an organization’s existing system, so it will be a while before companies can fully enjoy the benefits of AI-driven quality assurance. Several interesting potential developments include:
The rapidly evolving landscape of the QA industry demands a learning mindset. The methods and tools we rely on today may swiftly become outdated, rendering our expertise obsolete. Testers must do more than just testing — now they must know how to leverage cutting-edge technologies to transform their entire testing process. The result would be a significant competitive advantage in an increasingly crowded marketplace.
Katalon is a modern, AI-augmented test automation and quality management platform for web, mobile, API, and desktop applications. It provides a unified platform for teams to plan, design, execute, and manage automated testing efforts. Most importantly, Katalon is the pioneer in the AI testing space with a lot of exciting features that transform the way we imagine testing:
As one of the pioneers in the use of AI in software testing, Katalon continues to add more exciting AI-powered features to their product portfolio, empowering QA teams around the world to test with unparalleled accuracy and efficiency.