Is generative AI the end for software testers?

You may be proud of your organization’s test automation progress, and rightfully so! It is a major leap forward.

However, it's only half the story. The real spark of automation shines when executing test scripts. Beyond this shiny exterior lies a tedious, repetitive world that’s still extremely dependent on manual labor. For example, analyzing requirements can feel like an endless loop of reading document after document after document, and still failing to reveal what needs to be tested.

This is followed by another monotonous task of defining and writing test cases, which calls for an almost mechanical repetition of similar patterns. Writing test scripts is a detail-oriented task that often boils down to reusing and adjusting similar pieces of code over and over again. And after the automation scripts have been run, the exciting part ends. It's back to the grindstone with the analysis of test results, a task that entails combing through lines of logs, in search for the needle in the haystack. So, while test automation might take the spotlight in the execution phase, the backstage crew of manual tasks might not be as glamorous.

Say hello to the era of generative AI

But wait, don't be discouraged just yet! Here's where the new fascinating world of generative AI enters the stage, promising to turn the tedious into the terrific. Imagine AI technology that learns from existing requirement documents. Picture AI-based tools that take care of the mundane task of defining and writing test cases based on the insights learned. Now, envision generative AI assisting in writing test scripts. And even the post-execution analysis could be jazzed up with AI that helps identify patterns and anomalies in the test results. Like what you see? So, while the traditional manual tasks might seem like a dull encore, with generative AI, we're looking at a potential game changer that could take the center stage of test processes.

An evolution, not extinction

Now, the introduction of generative AI might also make some wonder (and nervous) if this spells the end for software testers. The answer is a resounding "No!" Instead of eliminating the role of software testers, generative AI is set to revolutionize it, evolving the monotonous into the monumental. The advent of AI technology in testing means that software testers will be able to focus less on repetitive and mundane tasks, and more on the higher-level, creative, and complex aspects of testing. So, rather than signaling an end, generative AI simply indicates a new chapter in the world of software testing. In this transformative era, the role of a software tester is not being diminished, but in fact, it's becoming more dynamic, essential, and impactful. Glad to see you smiling!

A close look at the AI revolution

Having shed light on the transformative potential of generative AI in the world of software testing, it's clear that exciting times lie ahead. But to fully appreciate the complete depth and breadth of these changes, it's crucial to delve deeper. So, strap in as we take a closer look into the future of software testing!

Conquering the paper mountain

The shift-left approach in testing requires early and consistent review of requirements, which often involves painstakingly going through extensive documentation, understanding each nuance, and intention. For example, you may find yourself repeatedly reading hundreds of pages of product specifications, business rules, user stories, or system requirements. It's not just time-consuming but also mentally exhausting, as it requires maintaining the same level of attention to detail from the first page to the last.

Here's where the magic of generative AI comes into play. Envision an AI that's capable of digesting these extensive documents, understanding the context, and extracting the key points. Imagine this AI can even predict possible use-cases and edge-cases based on historical data and patterns. It's like having an intelligent assistant that does the monotonous work of reading and comprehending extensive requirement documents for you, leaving you with a concise, insightful summary, which helps you to save time and focus on taking the key, strategic decisions.

Elevating test steps

While defining test cases and test steps is a fundamental aspect of software testing, it can be quite laborious and repetitive. Consider the common scenario where you need to validate multiple user flows for an e-commerce website. You would need to define test cases for every possible journey a user could take - logging in, browsing products, adding items to a cart, all the way to checkout. For each of these stages, you'd need to outline specific test steps, inputs, and expected outcomes. And it doesn't stop there. You'd also need to account for different scenarios, like varying payment methods, applying discounts, or handling out-of-stock items. It's a constant cycle of creating similar but slightly different test cases and steps.

This is another area where generative AI can lend a helping hand. Imagine having an intelligent assistant that takes the laborious task of defining test cases and steps off your plate. Trained to understand user flows and scenarios, this assistant is capable of generating test steps based on predefined templates or patterns. Picture a scenario where, instead of you spending hours mapping out all possible user journeys, your AI assistant swiftly does it for you. You can easily skip all those tedious tasks as it crafts variations for negative test cases, edge cases and test data for you. This way, AI can shoulder the load of the repetitive groundwork, freeing you to focus on the strategic aspects of test case design, making the job not just easier but also more rewarding.

Crafting scripts

Writing test scripts is yet another critical but often tiresome part of the testing process. It involves turning the previously defined test cases into executable scripts, with each of them testing a specific functionality or scenario in the system. This often translates into countless hours spent writing lines of code, where a substantial part of the task can be repetitive, as similar functions and features tend to have similar code structures. Test engineers need to craft these scripts carefully, considering different variations and conditions, which can often feel like they're stuck in a loop of coding, debugging, and adjusting.

This is another classic instance where generative AI can become an efficiency beast. By understanding and learning from pre-existing scripts, the AI can generate new scripts for analogous functionalities. It's as if you have a co-developer capable of doing the initial heavy lifting, creating the fundamental groundwork of the scripts at an impressive speed. Rather than scripting from scratch, testers can now shift their attention to reviewing these AI-generated drafts, tweaking them to perfection, and ensuring they're fit for execution. The result? Less time spent on routine tasks, and more time dedicated to tackling the more complex, challenging, and ultimately rewarding aspects of scripting.

Unraveling the unknown

And finally, after executing those automated tests, it's time for the meticulous work of analyzing the test results. This is where you transform data into actionable insights, finding bugs, identifying failures, and highlighting areas for improvement. This process often feels like a complex puzzle, as you read through log files, test outputs, and error messages, trying to make sense out of them. Understanding and analyzing information requires a sharp focus, detailed attention, and often a lot of patience as you work through a vast amount of data to find what is truly useful.

With AI, this process can become significantly more manageable. AI tools can read and understand the output data, highlighting and categorizing the results for easy analysis. They can even learn from previous tests to predict potential issues and help identify the root cause of failures. You're no longer trying to understand this large volume of data on your own. Instead, you have a reliable co-pilot that can guide you through the data, highlight important aspects, and help you reach your goal faster and more accurately. This not only makes the process of analyzing test results less laborious but also helps to uncover deeper insights, leading to more efficient debugging and improved software quality. Isn’t it cool?

The seamless blend of AI and human expertise

Even after the test results have been analyzed and interpreted, the testing process doesn't end there. Instead, it sets the stage for the next round of development and testing. This continuous loop of testing, analyzing, and improving forms the heart of software quality assurance. The introduction of generative AI in this cycle doesn't disrupt this loop but instead, makes it more efficient and meaningful.

While test automation has made significant strides in the execution phase, the advent of generative AI is poised to revolutionize the entire testing process, making it less repetitive, more efficient, and more exciting.

But we need to remember that AI models are not perfect, and that human oversight and decision making will stay for the time being. AI is not replacing the human role in testing but instead streamlining and enhancing it. By taking over mundane tasks, it allows testers to focus on the complex and insightful aspects of their role. In this new era, the role of a software tester is not just surviving but thriving, as it continues to evolve and adapt to technological advancements.

AI brings efficiency and speed to the table, while human testers contribute their creativity, strategic thinking, and intuition. This seamless blend of AI capabilities and human expertise is not only a future prospect. It's what’s currently unraveling and will drive the world of software quality from now on. So, buckle up, as the ride of AI-supported software testing is taking off!

Join our #bitesize webinar to learn more on how you can utilize the power of generative AI to make testing more efficient, effective, and fun!