As AI applications continue to shape industries from healthcare to finance, ensuring that these systems operate fairly, efficiently, and securely is paramount. AI testing is not just about verifying functionality; it’s about evaluating its impact on users, data integrity, and overall system robustness.
With their reliance on complex algorithms and vast datasets, AI applications present unique challenges in areas like bias, performance, and security. Rigorous ai testing is essential for building trustworthy AI systems, from detecting biased decision-making to ensuring fast and reliable performance and safeguarding against potential security vulnerabilities.
In this blog, we’ll explore how to test AI applications to effectively address these critical concerns, providing actionable insights and best practices for achieving well-rounded, high-performing AI solutions.
Why Perform AI Testing?
AI testing can enhance your testing efficiency, reduce manual effort, improve test coverage, and accelerate release cycles while ensuring higher-quality software applications.
Below are the benefits of how AI enhances different aspects of your testing process:
- Test Planning: Analyzes historical test data to optimize test coverage and prioritize test cases based on risk and complexity.
- Test Data Generation: Automates the creation and management of test data, reducing manual input and ensuring more accurate testing.
- Test Case Creation: Helps quickly generate test cases by learning from user interactions and previous test executions.
- Test Authoring: Simplifies test authoring by offering suggestions and automatically writing tests based on application behavior.
- No-Code Test Automation: Enables testers to automate tests without coding by using visual interfaces and intuitive workflows.
- Auto-Healing: Detects changes in the application and automatically updates test scripts, minimizing the need for manual adjustments.
- Flaky Test Detection: Identifies and flags flaky tests by analyzing patterns in test execution, improving test reliability.
- Root-Cause Analysis (RCA): Helps identify issues by analyzing test results and tracing failures to their underlying causes.
You can leverage LambdaTest, an AI testing tool to simplify your testing process. It provides advanced features such as auto-healing, flaky test detection, RCA, and even more AI-infused features. This can help you boost software reliability and focus on delivering quality software faster.
How to Test AI Applications?
1. Define Clear Objectives: AI testing is not available as autonomous testing as of now. It can only be included as part of a phase that carries the heavier load and leaves the testers with minimum manual work. To achieve this, the team should be clear about the objectives they wish to achieve by introducing AI in their infrastructure.
For instance, some teams lack resources, and therefore, their main objective is to get the scripting work done through AI. Such clear objectives help the team decide which tool is best and which technology they wish to adopt (such as predictive analytics and NLP).
2. Leverage AI Technologies: The objectives defined in the previous step will carve the path to select AI tools to include them in the cycle and perform AI testing.
For instance, in the previous example, the team had to adopt AI due to a lack of resources, and their objective was to get help in writing test cases. In such a scenario, an ideal technology to adopt is natural language processing, through which the test cases can be written in English and be interpreted by NLP models.
3. Train Algorithms: Once the team has the technology, they need to train it on their organization’s data so that the algorithm understands the requirements and produces output only relative to this training data. It is an extremely important step and, if possible, should be done by an AI expert.
4. Measure the Efficiency and Accuracy: The trained AI model associated with the algorithm is not full-proof evidence that the model will work as expected. It is because the team has no proof of its accuracy and efficiency except in theory. Therefore, the algorithm must be tested through AI testing techniques before feeding.
For this, below are some AI testing techniques you can use to test an AI algorithm:
Model Interpretability Testing: It verifies the model’s output and decisions, especially when compared to the current software project in which it will be integrated. It helps make sure the outputs are correct and builds trust among the stakeholders about the adoption of the algorithm.
Bias and Fairness Testing: It ensures that the algorithm is not biased toward any parameter of the input and the algorithm provides fair and impartial output.
Data Quality and Validation Testing: It ensures that the data quality provided by the AI algorithm is high. Most of the time it means that the data generated is accurate and covers all the scenarios and corner cases for an enhanced test coverage.
Adversarial Testing: It ensures the negative inputs do not break the algorithm and make their way out as errors or incorrect inputs. This includes providing wrong and malicious inputs that are either not expected or do not fit with the input criteria of that particular algorithm.
Black-box testing: It tests the output by providing some input to the algorithm but does not consider its internal structure or how it works.
White-Box Testing: It tests the underlying code, the algorithm’s workings, and all the other complexities. The inputs provided to the algorithm in this testing are based on all these understandings to cover the hidden and complex cases.
Integrate With the Test Infrastructure: The tested AI model can then be integrated into the test infrastructure at specific places to conduct smoother AI testing.
KaneAI: How to Test AI Applications?
Many AI testing complexities force us to look for methods to minimize AI setup and tuning costs. In this situation, adopting a tool that provides tester-focused features with AI capabilities that can automate most of your tasks with artificial learning is best.
This is where test agents like KaneAI by LambdaTest help. It sets itself apart from traditional low-code/no-code solutions by addressing scalability challenges. KaneAI manages complex workflows and supports all major programming languages and frameworks, delivering seamless performance even for the most advanced ai testing tools and use cases.
Here are the steps to perform AI testing using KaneAI. In this demonstration, let’s check whether the video available on the cloud mobile phone page is working properly.
1. From the LambdaTest dashboard, click the KaneAI option.
2. Click on the Create a Web Test button. The browser will open with a side panel where you can write test cases.
3. In the Write a step text area, write “go to www.lambdatest.com” and press Enter to visit the website.
4. As soon as we press Enter, the test step is recorded, and the website is opened on the browser. We can repeat the same process now with new test steps.
These steps can be as follows:
- Scroll down
- Click on real device cloud
- Click on more about the real device
- Click on video
- Click X
Writing these steps one by one will execute them on KaneAI. These steps are recorded and can be reused later.
5. Click the Finish Test button at the top right to finish this testing session.
The next screen will ask to save the test case. You can notice how fields such as Test Name and Description are already populated by AI, therefore cutting down the time a tester has to spend on these things. Here, the tests can be saved in a folder and reused later when required.
Wrapping Up
Testing AI applications for bias, performance, and security is an essential step toward building responsible, reliable, and secure AI-driven systems.
By identifying and mitigating bias, ensuring optimal performance, and fortifying security, you can create AI solutions that are not only effective but also trustworthy and ethical. The process may be complex, but with the right testing methodologies and tools, it’s possible to address these challenges and deliver high-quality AI applications.
As AI technology continues to evolve, regular testing and continuous improvements will be crucial to ensuring these systems remain fair, secure, and performant in the long term. Implementing robust testing practices today sets the foundation for AI applications that inspire trust, boost user confidence, and stand up to the demands of the future.
