Testing AI-Powered Applications: Forget the Turing Test, Welcome QA

With the AI apps gaining $2.5 billion in revenue in 2022, the competition in the field is beyond fierce. And you’d think there’d be abundant data on developing AI-powered products of high quality. Yet, surprisingly, there’s very little info on the subject. There’s even less info on the impact of software testing services on AI.

We might have a guess for why that is. But that’s not why you’re here. So, let’s dive into why you need remarkable testing for your AI app and how to make it such.

Why Testing AI-Powered Apps Isn’t the Talk of the Town

The biggest threat to AI-powered applications is settling for okay results.

Companies know that AI components have unique values. And they use them to drive their businesses forward. But it just as well can be a part of the marketing strategy that lets people know an organization is “trendy.”

Why are we bringing this up? Because using AI for the sake of attracting attention is too prevalent. And the consequences are harmful.

• Big Tech, in a way, owns AI.
• Companies that want to profit from artificial intelligence often reuse existing models.
• Nearly 70% of businesses struggle with implementing AI solutions for their goals.
• So, they make do with what they have (use AI as an add-on instead of turning it into an asset).
• Many smaller organizations bank on AI’s acquired status to gain investors and curious customers without actually developing algorithms for a purpose. They basically slap the “AI sticker” on their product without applying the tech for its potential.

And so, we have two issues on our hands:

1. Jumping on AI without knowing what to do with it.
2. And relying on the buzz around AI for profit only.

As a result, we got:

• AI algorithm recycling leading to the circulation of the same drawbacks (data inaccuracies, biases, unexplainable outputs, etc.).
• Deceleration of AI development.
• Decreased trust in AI-powered applications among users.
• Weariness around adopting AI for businesses.

The sci-fi-inspired fear of AI is not what it really is. The true danger of not using it properly is employing this tech without a solid plan or just for clout (kind of like what happened to Metaverse).

For your AI-powered solution to thrive, not simply exist, you ought to care for it. And first-rate QA services know how to do it properly.

Understanding AI-Based Software

It all begins with understanding your project. If your current QA team doesn’t have a grasp of AI fundamentals and lacks advanced knowledge of its principles, they won’t make your product shine (as we like to say). So, we shall begin with the essentials.

Defining AI-Powered Products

AI-based software refers to applications that utilize artificial intelligence techniques to perform tasks that typically require human intelligence. For example:

• Recommendation systems can analyze user preferences and behavior to recommend products, services, or content.
• Chatbots can understand and respond to human language.
• Predictive analytics can review historical data and forecast trends or patterns.

Basically, you can train a model to perform any task. You just need to know how to do it. Let’s review a simplified version of teaching an AI model to do something.

1. Data collection. AI models need data to learn from. This data could be anything from images, text, numbers, or even sound. For instance, if you want to build a model that can recognize cats in pictures, you would need a lot of pictures of cats.
2. Training. Once you have your data, you use it to train your model. This is like teaching the model what a cat looks like. You show it lots of pictures of cats and tell it, “This is a cat.” The model learns from this data and starts to understand what features make up a cat.
3. Testing. After training, you test your model to see how well it has learned. You show it new pictures of cats that it hasn’t seen before and ask it to tell you if there’s a cat in the picture. If it gets it right most of the time, you know your model is doing a good job.
4. Prediction. When your model is trained and tested, you can use it to make predictions on new data. For example, if you show it a new picture of a cat, it should be able to tell you, “Yes, that’s a cat!.”

So, to create a high-quality AI model, you should have an equally high-quality:

• Data.
• Training procedures.
• Testing processes.
• Success indicators.

With that said, you should also secure some of the core traits of AI-powered software in your product (as they signify that your AI is robust).

• AI can adapt through ongoing learning, allowing it to enhance its performance over time.
• It excels at tackling intricate problems by analyzing data and adjusting its internal parameters.
• AI models can perceive and interpret the world through sensors, cameras, and other input devices (integrations).
• They can automate simple and repetitive tasks, freeing up human workers for more complex and creative work.
• AI can analyze large amounts of data quickly and accurately, identifying patterns and trends that would be challenging for humans to detect.
• It can perform multiple tasks simultaneously, making it highly efficient for complex tasks.
• It can make decisions and take actions without human intervention, based on its analysis and internal programming.

In the context of the above seven traits, if your AI product doesn’t cover them, you might want to consider working on it some more.

Grasping Subfields of AI

We’ve grown rather accustomed to AI. And given that the IT sector is infamous for blurry definitions, let’s keep everything neat.

AI is an umbrella term. It is a denominator for technologies that rely on the concept of artificial intelligence to execute tasks.

• Machine learning (ML) learns from data and makes predictions or decisions based on that data. Companies like Amazon, Netflix, and Spotify use ML to analyze user behavior and preferences to recommend products, movies, and music.
• Natural language processing (NLP) can understand and generate human language. Chatbots and virtual assistants like Siri and Alexa use NLP to understand and respond to user queries and commands.
• Computer vision can interpret and understand visual information. Facial recognition systems like Apple’s Face ID and Facebook’s DeepFace use it to identify and authenticate individuals based on their facial features.
• Robotics is a subfield of AI that focuses on the development of robots and autonomous systems that can perform tasks in the physical world. Industrial automation systems like ABB’s RobotStudio and Fanuc’s Roboguide use algorithms to control and operate industrial robots in manufacturing and production environments.
• Expert systems mimic the decision-making capabilities of human experts in a specific domain (medicine, finance, engineering, etc.). Medical diagnosis systems like IBM Watson Health and Infermedica use them to analyze patient symptoms and medical history to assist in diagnosing diseases.

These are the most preferred AI subfields for businesses. There’s also fuzzy logic, neural networks, deep learning, and many more. Each variant has its individual aims and architecture. So, when we talk about testing AI, not only does it differ from validating, say, mobile apps, but it calls for distinct approaches for each “type” of AI.

What Makes Testing AI-Based Apps Different

Discussing how to test AI for each category would take a couple hundred pages. Hence, we’ll focus on four core distinctions.

#1 Data-Driven Testing

AI-based applications rely heavily on data to make predictions or decisions. Ergo, testing must ensure the software can handle a wide range of inputs and that it performs well in different situations. For example, a recommendation system may need to be tested with diverse types of user data to ensure that it provides accurate and relevant recommendations.

#2 Dynamic Behavior

AI systems can adapt and learn from new data. This means that their behavior can change over time. So, it’s not enough to test the app once and assume that it will continue to perform well in the future. Testing must be ongoing and iterative to ensure the product’s accuracy and effectiveness as it evolves.

#3 Black-Box Testing

Such software is often complex and difficult to understand. Just consider deep learning models with millions of parameters. This makes it challenging to test the application based on its internal workings. Instead, testing must focus on the application’s behavior, or “black box,” and ensure that it performs as expected in distinct scenarios.

#4 AI Ethics

Testing AI-powered products must consider ethical implications like bias and fairness. For example, a facial recognition system may need to be tested to ensure that it performs equally well for individuals from different demographics and does not exhibit bias. Additionally, testing must consider the potential impact of the application on privacy and security.

These elements also introduce quite a few Gordian Knots that the QA team should be aware of and know how to untangle. And especially since there are no standardized processes for testing AI-powered applications, when you work with or hire QA engineers, make sure they:

• Know about testing artificial intelligence specifics.
• Have practical experience with testing AI.

If they have no theoretical knowledge – don’t bother with the second point. And remember, artificial intelligence QA can set your AI development back a few years or create a visionary product. Experts make all the difference.

Unique Challenges Associated with Testing AI Software

Now come the Gordian Knots – the hardships of testing AI-powered apps. So, get ready. But don’t get nervous. All is possible with a good QA team.

Lack of Ground Truth

In traditional software testing, there are often clear, objective criteria for determining whether the software is functioning correctly. For example, if a calculator app is supposed to add two numbers and return the correct sum, it’s easy to determine whether the app is working as expected.

However, in AI-powered applications, the “correct” output is not always clear-cut. In a recommendation system, for instance, there may be multiple valid recommendations for a given user. And it’s not always clear which one is the “right” option. This lack of a clear, objective “ground truth” makes it challenging to evaluate the performance of AI models.

Data Quality & Bias

AI models are trained on data. And the quality and representativeness of this data can significantly impact the performance of the model. Additionally, data can contain biases that can lead to unfair or discriminatory outcomes. Poor-quality or biased training data can result in inaccurate or unfair outcomes, and identifying and mitigating these issues requires careful data analysis and preprocessing.

Complexity & Non-Determinism

AI models can be highly complex and non-deterministic. Specifically, it can be difficult to predict their behavior and design comprehensive test cases. For example, a deep learning model may have millions of parameters, and it’s not always clear how these parameters interact to produce a given output.

Interpretability & Explainability

The lack of interpretability in AI models makes it difficult to understand why they make certain predictions or decisions, which can complicate the process of identifying and addressing errors. For example, a deep learning model may be able to accurately classify images of cats and dogs, but it’s not always clear why it makes a particular classification.

Scalability & Performance

AI models can be resource-intensive and may require significant computational resources to test at scale. The resource-intensive nature of AI models can make it challenging to test them at scale, and performance issues can arise when testing large datasets or complex models.

Regulatory & Ethical Considerations

AI models can have significant societal impacts, and there may be regulatory and ethical considerations that need to be taken into account when testing them. The societal impacts of AI models, as well as regulatory and ethical considerations, can complicate the testing process by introducing additional constraints and requirements.

On top of these, there are also a few, shall we say, technical hardships with testing AI-powered apps.

• Because the development of AI-based systems typically commences with a dataset, the objective is to ascertain the predictions that can be derived from that dataset. In other words, AI-based products often lack clear requirements. SRS are present as strategic business objectives, not precise criteria.
• An AI system’s accuracy is typically only known after testing. So, again, no definitive measurements are available.
• AI products that reproduce user behavior instead of centering on functionality need, well, precise behavioral patterns. And, yet again, this is only possible after some work is done.
• AI-specific qualities, like adaptability and flexibility, can be difficult to define in requirements. Since the app changes with time, SRS may serve as kind of a point of reference, not explicit rules.

So, the correct answer to the question “How to test artificial intelligence” is with perseverance, skilled experts, and your mindset on a better product.

How to Test AI Applications Effectively

Your QA team shouldn’t perceive testing AI-powered apps as a standard project. Because it’s simply not true. AI products differ drastically from other software. And your QA engineers ought to know how to go about this divergence.

Input Data Testing

Information you feed into AI will be the backbone of its processing powers. It’s like explaining to a kid what a black hole is – you start with simple words and concepts so that the child grasps the basics. Then, you move to more complicated stuff and details to offer a fuller explanation of singularity and such.

That’s how input data testing works. It ensures that your AI has all the necessary info to connect the dots and come to its own conclusions. If you’re wondering what exactly it means to “test data,” it’s basically refining it to the point that makes sense to the AI model.

1. Collect data from various sources, such as databases, APIs, and external sources. Ensure that the data is relevant to the application and that it covers a wide range of scenarios.
2. Preprocess the data to clean it and prepare it for analysis. This may involve removing duplicate records, correcting errors, and transforming the data into a suitable format.
3. Label the data to provide context and meaning to the AI model. You may categorize the data into different classes or assign numerical values to it.
4. Augment the data to increase its diversity and improve the performance of the AI model. For example, you can add noise to the data, generate synthetic info, or apply transformations to it.
5. Validate the data to ensure that it is accurate and complete. Check the data against predefined rules or constraints, identify errors, and correct inconsistencies.
6. Profile the data to identify patterns, trends, and anomalies. You can use statistical techniques for data analysis or visualization to identify trends.
7. Assess the overall quality of the data, including its accuracy, completeness, and consistency.

Overall, testing data for an AI-powered application involves a combination of manual testing services and automated testing services. How to mix the two for your product is highly individualized. And that you ought to settle with your QA team.

Ambi Testing – The Perfect Blend of Manual and Automated Testing

Simulating Real-World Conditions

Testing AI applications effectively requires more than just running them through a series of predefined scenarios. Your validation strategy must include real-world conditions. They verify the model works as intended and can deal with deviant cases (like an autonomous vehicle registering a jaywalking pedestrian).

Simulating real-world conditions involves creating test environments that mimic the complexities of reality (and its surprises). For that, you should consider a few aspects.

• Real-world data is often noisy and unpredictable. Testing AI applications with a wide range of data inputs helps ensure robustness and reliability.
• Many AI applications operate in dynamic environments where conditions change over time. Simulating these changes can reveal how well the AI adapts and responds.
• AI systems must be able to handle unexpected situations. Testing with edge cases and unusual inputs helps identify vulnerabilities.
• If the AI interacts with humans, simulating human behavior and responses is essential. This can include testing for cultural differences, language nuances, and emotional states.

Allowing your AI to work with realistic patterns is like letting an animal out of the cage. If it doesn’t know anything beyond the laboratory – it won’t do well in the wild. But when we take time and effort to acclimate to the outside, it will surely have higher chances of success.

Rigorous Model Validation

Model validation is sort of like the final exam: did the AI learn something, or was it just guessing and getting lucky? It involves thoroughly assessing two primary elements:

• How well the AI model is able to achieve its intended task.
• Whether it consistently produces accurate results across different datasets and under different conditions.

To determine the AI’s productiveness, you need to evaluate its performance against a set of predefined criteria:

• Accuracy. How well does the model predict outcomes compared to ground truth data?
• Precision and recall. How well does the model balance between minimizing false positives and false negatives?
• Robustness. How well does the model perform under different conditions and with varying inputs?
• Generalization. How well does the model perform on unseen data?
• Bias and fairness. How does the model perform across different demographic groups, and is it free from biases?

If you encounter issues at this stage, so to speak, you might want to come back to data. As most AI performance issues stem from bad information. Yet, there can be weak points in the validation process, too. That’s why working with an expert QA company is so meaningful – they might as well help you improve your quality-related procedures.

Testing for Automation Bias

Automation bias refers to users relying on AI outputs without critically evaluating the logic behind them. This can happen when users trust the AI system to make decisions without fully understanding how it came to the verdict.

Consider this simple example:

• You ask an AI model to recommend you a movie.
• It does so; you watch the film and enjoy it.
• Does it mean the AI works perfectly?
• Or perhaps it was a coincidence?
• Would a few such lucky guesses be enough to declare the AI fully operational?

In short, automation bias is believing in AI more than your own knowledge and skill. And to avoid it, you could consider the following:

• Make sure the AI system is transparent and explainable so users understand how it works and what its limitations are. This can help users make more informed decisions and avoid blindly trusting the AI system.
• Incorporate human oversight into the AI system so there is always a person in the loop to review and validate the AI system’s output. This can help catch errors and biases in the AI system and ensure that it is used appropriately.
• Implement continuous monitoring and evaluation of the AI system’s performance so any issues or biases can be identified and addressed quickly. This can help ensure that the AI system remains accurate and reliable over time.

You should also have precise error-handling methods the AI can use. For example, if it’s not sure about the answer or just doesn’t know it, make sure it informs you of this instead of presenting the most “fitting” output.

Ethical Considerations in Testing AI

Most people are concerned about companies using AI. And rightfully so. A faulty model can produce erroneous results, share private info, spread misinformation, etc. The AI art debacle is still ongoing, by the way. And artists continue to sue AI companies for using their works as training material.

So, when we talk about AI ethics, we better be serious about it. And you must ensure that your AI-powered products are developed and deployed responsibly and that they do not cause harm or perpetuate biases. Hence, you should test for:

• Bias and fairness by evaluating the AI system’s performance across different demographic groups and ensuring that it does not perpetuate existing biases.
• Privacy and data protection to ensure the AI application complies with relevant privacy laws and regulations and that user data is handled securely.
• Transparency and explainability to make sure the system’s decisions are transparent and understandable and that users can easily grasp how the system works.
• Accountability to secure the app’s responsibility and the presence of mechanisms that address any negative impacts (e.g, not revealing sensitive info under any circumstances).

There are also AI regulations present. While they are in their nascent stages, dismissing them wouldn’t be wise. Plus, by getting to know the laws, you can anticipate where they will go and polish your product in advance.

Testing for Edge Cases

Edge cases are difficult to estimate as there’s no limit to human imagination or oddness. Still, it needs to be done. It advances your AI’s robustness, reliability, and UX. That’s where a QA specialist’s perspective is irreplaceable. To come up with weird scenarios your product might deal with is an art form.

• How would an autonomous vehicle react to a mattress falling out of a truck?
• Would a medical AI system come up with a new condition or flag odd symptoms for potential Munchausen syndrome?
• Could AI recognize someone tricking it? Like a person talking an AI chatbot into selling them a car for $1.

How well you test your AI-powered app for edge cases is mostly up to the experience of your QA team (so assemble it wisely). And they ought to test scenarios that are outside the norm or that push the boundaries of the app, like:

• Inputting unusual or unexpected requests. For example, testing a language translation AI with a rare or obscure language.
• Testing the AI application under extreme conditions that it may not encounter frequently. For instance, assessing a self-driving car in extreme weather conditions or during crab migration in Christmas Islands.
• Checking the AI’s ability to handle rare events that may not occur frequently but can have a significant impact. For example, testing a fraud detection AI with rare or unusual fraud patterns.

Yet, it’s not worth your while to think about every possible occurrence. Know what your product can do, study your audiences, and identify a finite number of deviations. And teach your AI to respond to extremely odd requests honestly while, perhaps, redirecting the user to a human assistant.

Best Practices for AI-Based App Testing

After over ten years of hands-on experience with testing AI-powered apps, we’ve developed our own expert insight bank. The following practices are something we’ve found most valuable for our clients’ products. So, file this data for future reference.

Integration Testing

AI-powered applications often rely on multiple models and algorithms working together. And there are numerous other components an app might use (APIs, data ingestion modules, etc.). Integration testing focuses on verifying that individual elements of the AI system work together as expected.

For example, if your AI application uses a machine learning model, integration testing would involve ensuring that it can be properly loaded, trained, and used by the application.

System Testing

Testing AI is one thing. Testing how well your product interacts with it is something else. So, while you’re determined to optimize your model’s operations, don’t make the entire system an afterthought. At any rate, one won’t work without the other.

That’s where system testing comes in. It involves evaluating the AI-based application as a whole rather than centering on its pieces. This includes checking the UI, the AI algorithms, and any other parts that make up the system.

Performance Testing

ChatGPT gets about 10 million requests a day. How do you make sure your AI-powered app doesn’t disintegrate with too many users? Performance testing. It assesses how your product operates under different conditions, such as varying levels of load or stress.

Yet, you should not only consider the number of customers but the toll it takes on the system. Hence, you ought to check its ability to handle large amounts of data, multiple concurrent users, and overall system speed.

Security Testing

As we’ve already established, AI is quite smart. But it can be a fool at times. And someone might want to take advantage of this. So, investing in high-quality security testing is imperative.

It helps identify and address potential vulnerabilities in the AI system. This can include testing for common security issues such as SQL injection, cross-site scripting, and authentication vulnerabilities. But don’t forget about hackers’ creativity. Consider advanced techniques a person might use on your product:

• Adversarial attacks.
• Data poisoning.
• Model inversion, etc.

Acceptance Testing

Acceptance testing is checking the AI-based application against the requirements and user expectations. Simply put, it’s about making sure your product offers real value to clients. And since AI apps are complicated in nature, it’s prudent to consider “the other side.”

Developers may say your system is the best they have ever made. QA teams may find all bugs and perfect the product. Stakeholders may be beyond content with the work done. But, will users have the same “that’s great” feeling from your project?

Acceptance testing, in a way, helps relate the SDLC to people who will ultimately use your app. It’s a much-needed reality check, so to speak.

Building a Culture of Quality Through Acceptance Testing

Continuous Testing

Continuous testing (CT) is just what it sounds like – testing the app until the end (rather than at the end). You run tests automatically and consistently throughout the development process. This can help to identify and address issues early on before they become more serious problems.

Apart from better quality, CT has more perks to offer:

• Bringing developers and QA teams together, advancing results.
• Reducing costs with prompt issue resolution.
• Accelerating your project’s SDLC.
• Making scalability a bliss, and more.

Plus, as your product grows and evolves, with continuous testing, you ensure that each change is quick and meaningful.

How Continuous Testing Can Become a Game-Changer for Your Business: E-Commerce Example

To Sum Up

“Testing AI is like cooking a meal. You need to follow the recipe and use the right ingredients to get the desired outcome.” That’s what AI said when we asked it to explain what it’s like to test AI-powered apps. While it’s an overly simplified analogy, it’s right about one thing. For a high-quality product, you need high-quality teams.

So, gather skilled experts (developers and QA) and secure productive collaboration between them and domain specialists. Then, you will have yourself a meal product that others gaze upon with awe.