The ability of the average person to yield the power of AI is no longer a concept relegated to works of fiction. It’s a present-day reality that’s beginning to have a real-world impact on industries across the globe. ChatGPT and similar topics like large language models (or LLMs) are quickly becoming household names, thanks to how they’re reshaping our work lives.

In the realm of software testing, AI has been in use for the past few years. Before the current AI boom, companies like Facebook and Google utilized artificial intelligence in some of their systems to assist with QA and other testing purposes. But with the wide accessibility of these tools, only now are we realizing how they can help improve the efficiency of developers and testers and increase the quality of the applications we build and test.

The Benefits of AI in Software Testing

Traditional software testing methods often rely on extensive, time-consuming activities. Manual testing requires plenty of resources in the form of people and time to go through scripted and exploratory tests. Automated testing relieves some of the burden that manual testers go through but still needs a lot of effort to build and maintain stable test suites. Managing test cases, logging bugs and other actions add to the labor necessary to validate the functionality of an application and ensure that everything’s working as expected and reaches quality expectations.

AI can bridge the gap with the issues organizations currently face with software testing. Existing testing methods like exploratory and automated testing will continue to be a vital part of quality assurance in software. However, adding AI into the mix will provide benefits that will help organizations and software testers build high-quality software at a pace never seen before.

Some of the immediate improvements we can expect to see AI introduce into software testing are:

Increased Efficiency by Taking Care of the “Boring” Work

One of the benefits of automated testing is eliminating repetitive tasks to free up testers and allow them to perform other high-value tasks. AI-driven tools and services will further free up our time by automating more of the redundant parts of our work (and, in many cases, the monotonous tasks we don’t like doing). It will allow teams to improve their existing processes, fix shortcomings quicker and deploy faster than ever since they won’t have to sweat the small stuff.

Improved Accuracy with Code and Test Scenario Generation

Despite our best intentions in creating bug-free test cases and code that always performs amazingly well, we’re still human, and we still make the occasional mistake in our work. While AI isn’t perfect either, it can help reduce the element of human error by generating consistent code and tests that follow best practices, spotting potential issues earlier and supporting coverage in areas that the team isn’t familiar with.

One of the advantages of AI is its ability to gather historical data, quickly analyze past performance and detect trends before most of us notice. We can leverage these benefits to measure efficiency and foresee potential trouble spots. In turn, we can use this information to help us improve our testing strategies moving forward and implement preventive measures early when it’s quicker and cheaper to resolve.

These advancements are just the tip of the iceberg, and we can expect even better results as AI tools and services mature. Combining the strengths of AI with existing testing practices, such as manual and automated testing, will yield the best possible results and potentially transform the way we test software.

The Current State of AI in Software Testing

With all the benefits AI can bring to software testing, how is it currently utilized in today’s software development environment? As mentioned earlier, AI is becoming a standard tool in almost every worker’s toolkit and expanding rapidly by integrating into typical workflows in organizations worldwide. However, the adoption of AI in software testing is still in its infancy, and the availability of tools and services that integrate AI with existing practices is still limited in its potential to serve us.

However, that doesn’t mean we must wait a couple of years to begin taking advantage of AI in our software testing processes. You can already see real-world usage of artificial intelligence helping developers and testers. Here are a few examples:

Automated Test Case Generation

Creating and maintaining automated test scenarios for our applications is one of the most tedious testing tasks teams face. Specifying what test cases we need to automate and keeping them up to date takes time and effort. AI will help reduce the time it takes us to build and support automated tests by automatically generating test scenarios based on the application under test. Facebook has a tool called Sapienz that is helping their large engineering teams squash bugs quicker by automatically creating tests for their mobile applications.

Improved Visual Regression Testing

Visual testing verifies that an application’s user interface looks as intended across various scenarios, such as different devices, web browsers and screen resolutions. Typically, this process can create inconsistent results due to minor changes like an element being off by 1 or 2 pixels, triggering false alerts. Some platforms use AI algorithms to improve the analysis of an application’s content. These systems collect information that, over time, save tons of time by detecting significant differences while ignoring minor changes that other tools might flag as a change.

Eliminate the Need to Write Boilerplate Code

Whenever we begin a new test automation project, teams require a substantial amount of up-front time and effort for the initial setup. Depending on what’s needed to get the project up and running, it can take plenty of time, meaning less time testing for the rest of the team. Instead of wasting our energy writing boilerplate code to kick-start a project, we can let AI do that for us. GitHub Copilot is an excellent tool demonstrating how we can use AI to handle some of the grunt work to get to the fun stuff quicker.

Rapidly Adjust to Changes in the Application Under Test

The pace of modern software development means that the codebase changes in the blink of an eye. Often, these changes require adjusting existing test scenarios, interrupting the QA and development process. It also leads to testers spending more time fixing broken tests instead of using their time more wisely. With the help of AI, we might consider this tiresome part of software testing a thing of the past. Testing tools like Progress Telerik Test Studio take advantage of AI processes to help you fix recorded test scenarios by detecting test failure reasons and providing solutions, shrinking the effort needed to redo existing work.

These real-world examples show how existing companies apply AI to their products and how we can use them to enhance our existing software testing practices. The current state of AI in software testing is still evolving, and we can expect to see this space grow as these tools mature and organizations begin finding innovative ways to integrate AI into their current processes.

The Future of AI in Software Testing

Even with the progress that organizations have made with introducing AI into tools and services to assist with software testing, it’s still in its early phases and not fully developed yet. We’re only scratching the surface and discovering how to use these technologies best moving forward. Expect the field to continue expanding into different paths as we learn how to use these tools and services to our benefit.

Teams will still need help integrating AI into their organizations, requiring expertise and knowledge that might be in scarce supply at this early stage. Trust is another factor in adopting AI-driven processes, with some worries about the reliability of automatically generated tests and testing methods. There are also privacy concerns to consider, as these services store and use your data to train itself and increase the potential exposure of sensitive information.

That being said, the future of AI in testing looks very promising. We’ve already explored tools that let us take advantage of AI right now, and the situation seems to improve daily. We’re also seeing many emerging developments taking place across the business world as companies begin to recognize the potential of AI in their organization. Let’s explore some of the trends that look to be leading the way in this area:

Better Integration with Existing Processes

As mentioned above, integrating these services is one of the primary challenges of introducing AI into any organization. After all, AI isn’t a replacement for how we work but instead will help us augment our current processes. Some companies like GitLab are beginning to explore how to use artificial intelligence and machine learning for their DevOps and security platform. Having a way to incorporate AI into existing software testing practices safely will become an essential component for widespread adoption.

Help Software Become More Inclusive

Accessibility is becoming a vital part of software development so that anyone can have complete access to a website’s or application’s functionality regardless of their physical abilities. In some cases, such as with government websites in the United States and other countries, accessibility compliance is a legal requirement. Deque Systems, the company behind the Axe accessibility engine, has been applying AI into their tools to help make software accessible to all. As accessibility continues to expand in importance, AI will play a significant role in assisting teams to make their applications fully accessible.

Hands-off Monitoring and Observability

Keeping software systems running optimally after deployment is part of a high-quality customer experience. We have plenty of tools and services that allow engineers to provide insights into a system’s health and alert us when something isn’t quite right. Most of these still require manual intervention to adjust metrics or rules when needed. However, we’re beginning to see how some companies use AI to help customers discover and alter their monitoring settings in real-time, helping site reliability engineers keep the systems they monitor running at peak condition.

These three examples are just a handful of some of the AI trends that are beginning to emerge around software testing. You can find other services and organizations focused on software security, automated test creation and performance tracking already integrating AI into their products to boost our efficiency as developers and testers and become more commonplace.

The Future of Developers and Testers in an AI World

This article mainly discusses tools and services and how AI transforms how we use them for software testing. But what about the people using these tools and services? Not a week goes by without some news outlet talking about whether AI will replace human workers. Many developers and testers are becoming increasingly worried about whether their job is secure due to the influx of AI-driven processes taking over the landscape. It’s a valid question to ask since some organizations have begun to lay off workers due to AI.

Does that mean anyone involved in software, whether through QA or engineering, must worry about their long-term employment status? In its current state, AI won’t replace testers or developers, at least if a company cares about quality. The tools and services discussed in this article help some of the day-to-day processes for these fields, but only if you know how to leverage them properly. For instance, ChatGPT is notorious for sounding like it gave you the correct solution to a query when, in reality, it didn’t. One recent experiment found that ChatGPT gave incorrect responses to coding questions slightly over half the time. If an organization lets some employees go because they rely on AI, they will likely run into many problems sooner rather than later.

Experience and reasoning are some areas where developers and testers excel over machines, and that’s not changing any time soon. That’s not to say AI won’t improve to a point where they’ll need to be concerned about their jobs. These tools and services are continuously improving—mainly because we’re constantly providing data that trains the systems every time we use them—so it might reach a point where AI gets close to human efficiency in the world of software testing. We’re likely still some time away from that happening if it ever does, so it’s not something most testers or developers will need to worry about now.

Final Thoughts: Embracing AI Will Help Improve Software Testing

Artificial intelligence is already shifting the way we approach not only software testing but also almost every software-related task in our day-to-day work. However, AI isn’t about replacing the work that we do—it’s all about enhancing our current skills and responsibilities. The current crop of AI-driven tools and services is still in its infancy. Still, we can already see the possibilities to help us develop, test and deliver high-quality software quicker than ever.

Even with these early glimpses of the potential of AI, integrating it into existing processes still faces its challenges. Privacy concerns, trust in automated output, and incorporating new systems are just some of the hurdles organizations must overcome if they want to take advantage of AI’s benefits. We also must overcome the worries that AI is coming for our jobs since these tools and services aren’t a substitute for the creativity and critical thinking required to produce stable and reliable software. That’s where developers and testers will always shine and have an advantage over automated systems.

Artificial intelligence and software testing have a bright future ahead. As these systems improve, the relationship between AI and people will likely create more efficient ways to work on software and help organizations unleash their full potential to deliver the best possible product to their customers.

This article was originally published on the Telerik blog.