ai software testing

AI software testing refers to the process of evaluating and verifying the quality, functionality, and performance of artificial intelligence (AI) systems or applications. As AI technologies become more complex and integrated into various domains, ensuring their reliability and accuracy becomes crucial. AI software testing involves various methodologies and techniques that focus on assessing the behavior and performance of AI algorithms, models, and systems.

What's coming

Technology is gradually absorbing more and more areas of activity, and software testing is next in line. We are all so spoiled with ubiquitous automation that, given the right tools, we would gladly outsource most of the test design and validation of tests to artificial intelligence (AI). Instead of manually setting up automated testing, machines will develop and execute tests themselves, constantly improving as they interact with people. This mechanization of test coverage means that every development team will soon have access to a virtual team of testers with more intelligence, speed, and coverage than even the highest-paid development teams can get today.

Here are some key aspects of AI software testing:

Data Quality and Preprocessing: Data is at the core of AI models. Ensuring the quality, diversity, and relevance of training data is essential for accurate AI predictions. Testing might involve examining the input data and verifying that it covers a wide range of scenarios.

Model Testing: This involves testing the AI model's behavior in various situations. Test cases are designed to evaluate the model's accuracy, robustness, and generalization capabilities. It might also include testing edge cases and corner cases that the model might encounter in real-world scenarios.

Functional Testing: This includes verifying that the AI application or system functions as intended. For instance, in a chatbot, functional testing might involve ensuring that the chatbot responds appropriately to different user inputs.

Performance Testing: AI systems often need to process a large amount of data or make quick decisions. Performance testing evaluates the system's response time, scalability, and resource utilization under different workloads.

Robustness Testing: AI models should be able to handle unexpected inputs or noisy data without crashing or providing incorrect outputs. Robustness testing aims to expose vulnerabilities and weaknesses in the AI's decision-making process.

Ethical and Bias Testing: AI systems can inadvertently perpetuate biases present in training data. Testing for ethical considerations and bias helps identify potential discriminatory outcomes.

Regression Testing: As AI models evolve, changes and updates can impact their behavior. Regression testing ensures that new versions of AI software do not introduce new bugs or issues.

Exploratory Testing: This involves exploring the AI system in an unstructured manner to discover unexpected behaviors or issues that might not be covered by standard test cases.

Security Testing: AI systems might be vulnerable to security threats like adversarial attacks or data poisoning. Security testing assesses the system's susceptibility to such attacks.

Interoperability Testing: If the AI system interacts with other software or APIs, interoperability testing ensures that the integration works seamlessly.

Usability Testing: If the AI system has a user interface, usability testing evaluates how easy it is for users to interact with the system.

Feedback Loop: AI software testing often involves a feedback loop where testing results are used to fine-tune the AI model, improve training data, and enhance overall system performance.

Due to the unique challenges posed by AI systems, testing might require innovative approaches, including using AI itself to generate test cases or simulate different scenarios. Testing AI software is an ongoing process, as AI systems continuously learn and adapt based on new data and interactions.

Manual AI Software testing

Manual functional testing is expensive, both in time and money. The most advanced application development teams and vendors code thousands of lines of tests for their applications, line by line typing the same "click here" and "test that". This approach has many disadvantages, including in terms of funding the testing team. Creating such tests diverts the attention of developers from the main goal they are creating - the product itself. In addition, these scripts either require the management of many machines to run or the involvement of serious human resources to execute manually. All this eats up precious time. The full completion of the tests can take several days, and sometimes several weeks. This is out of line with today's development teams, who strive to build and deploy their applications on a daily or even continuous basis.

If we consider the classical approach to test automation, then test support is a factor that additionally increases its cost: the more tests, the more labor-intensive and expensive their support becomes. Further, when the application changes, the test code most often also needs to be updated by someone. And it often happens that most of the automation efforts quickly turn into pure maintenance, with little change for additional coverage. AI bots evolve even after code changes. Since bots are not hard coded, they adapt and learn to find new features of the app on their own. When the AI ​​finds changes, it automatically evaluates them to determine whether they are new features or defects in the new release. In this way,

What happens when the application becomes more complex

Manual testing does not scale because tests are created one at a time. Adding tests is a linear activity. Adding product features can increase complexity exponentially as new features and states interact with older features. At the beginning of a project, testing can usually go hand in hand with building functionality, but the more complex an application becomes, the more difficult it becomes to ensure that it is fully covered by tests. What's most frustrating about the current state of manual testing is that it only tests the specific cases you choose - and nothing else. If a new feature is added, the previously manually created autotest will still succeed, even if the new feature does not work. Only exploratory testing (by humans) will be able to detect these changes,

Why AI is better

The AI ​​approach to quality assurance wins in cases that cause so many problems in manual testing. If we had a simple AI that knew how to bypass application functionality like an end user or tester, recorded performance metrics and kept track of where each button or text field was located, then it could generate and execute tens of thousands of test cases over a period of time. a few minutes. But what if you give the machine thousands of examples of errors, as well as examples of correct functioning. The bots could suggest where the team should focus their efforts. And if these bots test thousands of other applications while learning in parallel, they will gain a huge amount of experience that can help the test team make deployment decisions. In this way,

AI will notice every little thing that is added or removed from the application. Where manual automation misses changes in the latest build, AI bots will automatically click every new button added to the app and pay attention to every picture missing from the app. The bots will evaluate all changes and rank them according to how important they are based on the collective wisdom of the current testing team, as well as all other teams that have flagged similar changes as a "feature" or "bug".

And now it's time to consider the objections from the skeptics:

Objection 1: “My application is special, AI will not help here”

In reality, your application looks just like many others. If you break it down like a chef carves a fish, you will notice that the app has buttons, text fields, images, etc., the same ingredients as any other. If the AI ​​can analyze some other application, then it is very likely that it will work well enough on yours.

Objection 2: “Hey, I will always be smarter than a bot!”

Doctors, credit and financial advisers once also thought they could not be replaced by AI and automated. And the truth is that you only know what you know - your application and test cases, a book or two, and maybe what you learned during a conversation at a conference about testing. AI is like a Terminator: it doesn’t know fatigue, it doesn’t forget anything, it inexorably moves towards its goal without fear and pain. Are you really smarter and able to do more than 100, 1000 or 10000 bots analyzing your application? Even if you're smarter, wouldn't you rather have bots do all the boring work for you so you can instead focus on creative or more complex issues?

Objection 3: “Of course, but how does the AI ​​know what test data is suitable for the application?”

If you think about it, most applications accept the same data. Names, email addresses, phone numbers, product searches, camera profile photos, etc. Almost the entire flow of input data into your application is typical data that can be classified and structured. A small data set will be enough to build an impressive testing AI. At least this will be enough to become your assistant.

Objection 4: “Okay, but how do these bots know if the application is working properly?”

Good question. Indeed, how do you know that the application is functioning properly? You never do. You have some tests, maybe 100 manual or automated test scripts, and they pass, but they only test a small piece of the possible state space of your application. You also know that the most valuable information is in the form of feedback, errors from real users, and their systematic correction. What you really want to know about testing is: "Does it work the same as it did yesterday?". If not, what are the differences - is it good or bad? This is indeed what is the result of most tests. AI bots are great for searching thousands of points in your app, checking a lot of things to make sure everything works the way it did yesterday.

Will AI bots appear anytime soon? Now the AI ​​testing bots have already begun their training, testing hundreds of the biggest applications on the market.and