The software testing landscape is rapidly evolving, with artificial intelligence (AI) and machine learning (ML) technologies playing a pivotal role.
One of the most common questions I’m asked lately is, “Will AI replace testers?”
But I do believe that testers being assisted by artificial intelligence will become the new norm.
That’s where my recent podcast interview with Mush Honda comes in.
Mush Honda, a respected AI and ML expert with global experience, recently shed light on how these technologies are revolutionizing the world of software testing.
So, in this article, you’ll discover three ways to apply AI to your testing efforts to save you time.
Do you think AI in testing is hype?
AI and ML in Software Testing: Beyond the Hype
Mush admits that the buzz around AI and ML may sound like hype.
However, he emphasizes that they're more than just buzzwords; they are valuable tools that can accelerate and assist software testers, much like Iron Man's Jarvis
He likens the use of AI in software testing to test automation tools that boosted efficiency a decade and a half ago. As Mush and many other past guests have suggested, AI should not replace human testers but instead optimize and streamline their efforts.
So, what is an example of how AI can help testers become more efficient?
Let's start with the first time-saving AI feature–a GPT-powered, manual test generator.
Katalon's AI-Powered Jira Plugin
One innovative solution Mush mentioned is a Jira plugin developed by Katalon.
This plugin employs Open AI to generate manual test cases, scenarios, and steps for validating user stories. It offers an excellent baseline for understanding and documenting scenarios, accelerating software delivery.
Why is this cool?
The Power of GPT in Manual Test Generation
This feature allows Jira users to generate test steps from a single button click within Jira issues.
The manual test generator parses and analyzes the Jira ticket's description, extracts relevant information about software testing requirements, and outputs a set of comprehensive manual test cases tailored to the described test scenario.
This enables teams to create and collaborate on testing efforts early in the development pipeline, enhancing efficiency and productivity.
From what I'm being told, this is just the beginning.
Katalon also plans to release many other features incorporating GPT to enhance the following:
- The authoring experience with suggestions for code options
- Recommended documentation
- Generating API tests
- Further, extended support for manual testing
You've probably heard a lot about GPT and testing, so how is this different or unique from all the other solutions being released in this area?
How is Katalon GPT Different?
While many companies are incorporating GPT into their products, Katalon has engineered customized GPT prompts to meet the specific needs of manual testing.
This allows Katalon to deliver tuned responses that generate manual tests that meet the expectations of QA professionals and software teams.
Besides the GPT feature, what's another AI feature that could help the tester?
The second AI feature Honda talked about was the new Studio Assist.
What is StudioAssist?
This unique tool employs AI models as pair programming partners, enabling users to input test scenarios and generate the corresponding code for test automation scripts.
This is a significant advancement from traditional recording and playback methods.
What's more, StudioAssist can facilitate the creation of automated tests using comments and can convert simple user instructions into automation test scripts.
Honda highlights how this feature can save considerable time, reducing a task that took 30-40 minutes to just 5 minutes!
How does StudioAssist Work?
Harnessing the power of Generative Pretrained Transformer (GPT) and Katalon Studio, Studio Assist offers context-based code suggestions and detailed descriptions of existing code, enhancing efficiency and collaboration among test developers.
This tool bridges the gap between the easy-to-learn record and playback capabilities and the early steps to customizing tests using code.
With features like code generation based on descriptions and generating descriptions of existing code, StudioAssist enables quality engineers to rapidly develop new Studio code tailored to their needs and understand unfamiliar existing code.
The “Explain Code” feature allows experienced developers to include inline comments, guiding junior developers.
Think about all the user cases this can be used for.
One example is helping to onboard new automation engineers by going through the automation code base and using the Explain Code feature to understand the framework better.
The third feature is Autonomous Test Generation.
Before discussing this feature, let's talk about what autonomous testing is.
What is Autonomous Testing?
Autonomous testing harnesses AI and ML technologies to automatically identify and execute relevant test cases, improving quality, efficiency, and scalability.
How does Katalon use this?
A beta feature called Autonomous Test Generation (ATG) creates automated tests based on the actual system's use in production.
AI algorithms enable ATG to provide strategic, data-centric test scenarios, adapting to evolving software requirements and optimizing testing processes based on historical data.
Compared to traditional methods, ATG significantly enhances test coverage, efficiently identifies potential bugs and errors, and seamlessly adapts to changing user patterns.
Benefits include time and cost efficiency, comprehensive test coverage, continuous testing integration, and increased software quality. ATG's adaptability and scalability help maintain test cases' relevance, ensuring higher quality standards.
Fusing ATG with other testing techniques can result in a comprehensive and effective testing strategy.
For example, it allows for deeper code insights, leverages domain knowledge, reduces test suite maintenance effort, and refines overall testing quality.
Therefore, ATG, though not a standalone solution, forms an integral part of a robust testing strategy for software developers and industry practitioners, promising a revolution in the software testing landscape.
Mush suggests the key takeaway is that regarding “autonomous testing,” Katalon's philosophy is to focus on accelerating the testing process, not eliminating human input or replacing testers.
So don’t be afraid.
Every AI expert I've spoken to mentioned that AI will not replace testers but assist them.
So as a tester, how do you learn to see AI as a friend rather than a foe?
Embracing Change and Leveraging AI
Mush advises testers to embrace change and view it as an opportunity for continuous learning. He encourages testers to utilize AI to streamline their work and handle testing pressures.
As a community, we need to realize that the future of software testing is here, and AI is driving it.
Technologies such as Katalon Studio are reshaping how we approach software testing, making the process more efficient and streamlined. The insights shared by experts on my podcast, like Mush Honda, provide a valuable understanding of how AI and ML can enhance the human element in software testing rather than replacing it.
Embracing AI technology and leveraging tools like Studio Assist and Autonomous Test Generation can be game changers for software testers. They help testers focus more on new feature development and less on the mundaneness of repetitive tasks.
As we move forward, integrating AI into software testing tools like Katalon Studio will continue redefining the boundaries of what's possible in software testing.
So, as Mush advises, let's embrace change, leverage AI, and continue to learn and adapt in this ever-evolving technological landscape. If you want to be at the forefront of this revolution, Katalon Studio's new features are undoubtedly worth exploring.