Courtney Gray
Artificial intelligence has taken the world by storm, transforming industries and reshaping how businesses operate. It’s no longer the distant technology seen in films like Spike Jonze’s 2013 film Her: it’s here right now, and has transitioned from a buzzword to absolutely business-critical.
This is no different within the software testing space: AI is transforming how businesses use and test their applications.
In this article, we look at why organizations should consistently test their AI applications, and how they can utilize AI-augmented tools to do so.
Skip ahead to:
- Balancing the critical need for AI with its performance challenges
- What is testing of AI?
- Why is AI testing so important?
- How can organizations test their AI tools?
- Using AI automation to test AI
- How does Leapwork test AI with AI?
- What type of tests can be automated with Leapwork?
- How can you measure the success of your automated testing strategy?
- What are Leapwork's AI capabilities?
- Conclusion
Balancing the critical need for AI with its performance challenges
According to our AI and Software Quality: Trends and Executive Insights report, 85% of organizations have integrated AI applications into their tech stacks in the last year. From enhancing customer engagement to automating complex workflows, AI promises to unlock new levels of efficiency, innovation, and growth for any business.
However, as AI becomes more deeply embedded in these processes and vital to the running of many programs, its limitations and the challenges it introduces are becoming increasingly apparent. AI reliability and trust are being questioned.
In fact, 68% of organizations that have adopted AI are already encountering issues related to performance, accuracy, and reliability. This is an alarming amount of organizations, especially considering how vital AI is becoming for businesses.
What is the most common bug or issue you’ve encountered with your AI applications? Source: AI and Software Quality Report
The combination of these two data points paints a clear picture. The rise of AI adoption brings a critical need for testing AI applications and organizations must take a more cautious yet nuanced response to AI. While there is widespread AI adoption, there is also a core need for human validation to minimize risks, such as the amount of hallucinations that occur with generative AI.
What is testing of AI?
AI testing refers to the process of evaluating and validating AI software and applications to ensure they perform exactly as intended. This means assessing the functionality, reliability, and accuracy of AI, such as testing an AI-powered chatbot to ensure the given answers are as intended.
When this malfunctions, companies can expect to see issues with their customers that could even result in reputational damage, as seen when Air Canada’s chatbot promised a discount to a passenger, which later resulted in the airline losing a lawsuit.
Generative AI testing is one specific type of AI testing that many may be familiar with, especially due to the widespread adoption of generative AI platforms, such as Microsoft Copilot and ChatGPT. Many companies are also integrating their own generative AI systems and features such as chatbots. And given AI’s complexity and dynamic nature, the different types of AI testing can include:
- Functionality testing: This ensures the AI system is performing its core tasks correctly.
- Performance testing: This measures how well an AI application performs under different types of conditions and also assesses speed or response times.
- Accuracy testing: This validates how successful an AI operates by comparing AI-generated results against expected outcomes to identify errors or hallucinations.
- Ethics and bias testing: This examines whether the AI has an inherent bias or makes unfair decisions, providing you with methods of how to rectify these biases.
Why is AI testing so important?
AI testing is so crucial because these systems often operate within dynamic, data-intensive, and highly complex application environments, where even one small error could lead to significant consequences.
For example, a fraud detection AI must avoid false alarms that could disrupt user experiences, or a customer service chatbot must understand and respond accurately to a diverse range of user queries.
Red teaming, a type of testing that challenges AI systems to uncover vulnerabilities and flaws, is on the rise. This is especially vital for systems where the use of AI is handling highly sensitive data or operating in high-stakes situations such as diagnosing medical conditions or autonomous driving cars.
Adversarial testing is another style of testing where testers simulate attacks on an AI system to identify vulnerabilities and weaknesses.
Without this rigorous testing, AI systems can produce errors or show bias, limiting their effectiveness and trustworthiness. Proper oversight of AI testing identifies these issues and ensures that an organization’s systems continue to deliver consistent and high-quality results.
How can organizations test their AI tools?
Most AI tools, at least in their current form, are not plug-and-play solutions that operate flawlessly out of the box. Instead, they require continuous evaluation, validation, and fine-tuning to ensure they meet expectations and deliver on the technology’s vast potential.
It is a full-time job in itself to share knowledge about AI applications with key stakeholders while also staying on top of ongoing updates and changes in AI. Many organizations simply do not have these resources within their software testing teams.
And these organizations are not alone. In fact, according to our report, 24% of organizations do not have a dedicated team or individual responsible for testing AI apps, and 26% do not have a commercial testing platform.
Only 16% of organizations interviewed believe their current AI testing practices are efficient. There is clearly a disconnect between the business critical need for AI and the ability to ensure they actually function as intended.
The good news is that the way to mitigate these risks is within reach, and actually involves leaning further into AI. What do we mean by this?
Well, using AI-augmented testing platforms to help automate and streamline the process will assist organizations in validating their AI systems and maintaining high performance as they continue to evolve.
Using AI automation to test AI
So there you go: our advice to ensure your AI is working correctly is to utilize AI-augmented systems that help your software testing teams correctly validate.
Organizations that utilize robust testing practices, both for their AI applications and the larger systems in which they operate, can really unlock the true power of AI without the worry of unreliable performance.
By adopting AI-augmented testing tools, companies can proactively identify and resolve potential issues, ensuring that their existing AI applications deliver consistent, high-quality results across the customer journey.
Understand though, that AI-augmented tools are not simply adding more layers to an AI framework. They can be used to address the many shortcomings of AI systems by providing rigorous, unbiased assessments of their performance and accuracy.
AI-powered testing platforms, such as Leapwork, will assist in driving and delivering continuous, end-to-end quality across your business. Leapwork provides a visual, unified, and journey-centric workflow that uses generative AI capabilities, ensuring everyone from your engineers to business users can build and maintain tests.
How does Leapwork test AI with AI?
By now, you’re probably wondering how testing of AI with AI works in practice, especially within an AI-powered testing platform such as Leapwork.
Let’s look at an example of how to use Leapwork’s AI-powered test automation to test Microsoft Copilot.
Leapwork validates responses using automation. It runs predefined test scenarios and compares unstructured data outputs with expected results. You can also develop texts that consider the context of unstructured data to avoid any false or hallucinated responses.
Leapwork uses knowledge articles, documents, and questions as the input data to regularly test Copilot's responses against these predefined texts, helping you to identify accuracy drift as documents become out of date.
What types of tests can be automated with Leapwork?
You are able to automate several tests including functional tests, regression tests, performance tests, and response accuracy. Leapwork lets you create custom test scenarios that can be tailored to your specific needs. And
How can you measure the success of your automated testing strategy?
Success can be measured by using several metrics including test coverage, defect detection rate, test execution time, and the accuracy of Copilot’s responses. Make sure you regularly review these metrics to assess the performance of your automated testing strategy and make adjustments as required.
We recommend running the testing pack daily, for example, as a pre-start of the day check-up, to ensure that Copilot responses are not already outdated.
What are Leapwork’s AI capabilities?
Let’s take a look at Leapwork’s AI-powered testing capabilities for businesses, which are designed to assess how well generative AI handles specific tasks and responds to user-defined prompts.
To do so, Leapwork compares these AI-generated responses against predefined, human-crafted expectations, ensuring the outputs of your AI applications are consistent, accurate, and aligned with your intended results, and are not generating incorrect responses. We call this capability, AI Validate, within the Leapwork platform.
As an example, let’s take an AI-powered chatbot on an e-commerce website. A customer asks “Do you offer international shipping?” This question can also be phrased in many different ways such as “Do you deliver overseas?.” AI Validate ensures that every response aligns, despite the differing ways of asking the question.
By automating and comparing these outputs against your independent data, Leapwork can help by acting like a second pair of eyes, verifying the AI’s responses without the biases the original AI might introduce. This also helps to simplify the testing process, allowing for faster and more accurate results.
Find out more about how Leapwork is approaching AI →
Alongside AI Validate, Leapwork has several other AI capabilities to assist with and inform your testing.
AI Transform:
- This function is incredibly powerful as it can take any input and transform it into anything else, reducing test creation efforts and costs by standardizing your input data formats. For example, you can ask for the number 741 to be translated into German, enabling your testing to become multilingual. By doing so, Leapwork allows for quick content translation, simplifying the testing process with efficient text manipulation.
AI Extract:
- This ability uses automation to extract structured data from otherwise unstructured or semi-structured sources, such as emails or PDF invoices, and present it as structured text. By doing so, you can improve data accuracy in systems like your CRM thanks to more streamlined data handling.
AI Generate:
- AI Generate creates realistic and varied datasets based on prompts and can generate any kind of test data. This allows for comprehensive testing that accurately reflects the environments your AI systems will operate in and ensures they perform reliably under diverse conditions. For example, you could generate test data by asking for British postal addresses, generating any output you need to determine (such as street name, city, and postal code). Another example is generating French-sounding names required for a data test. This pillar is a powerful way to generate test data for complex processes.
Conclusion: AI will continue to expand the quality assurance of software test automation
AI holds incredible promise for businesses, but its success hinges on rigorous testing and validation. As AI becomes an integral part of more and more businesses, the companies that invest in thorough automated testing will be the ones that reap the full benefits of AI by turning risks into rewards.
Related: The Future of QA with AI and Human Collaboration →
In our next blogs in this series, we will be exploring the themes of our AI report, including why AI-driven quality assurance won’t replace human oversight and why the C-suite, in particular, is eyeing AI-augmented testing tools to support business growth.
