Top Trends in AI-Based Application Testing You Need To Know

Engineering managers understand better than most the relentless pace at which the world of AI is evolving. You're likely tasked with integrating this technology into your offerings and making sure it all functions seamlessly to advance your business.

Thankfully, with these AI advancements, new approaches to testing, automation, and quality assurance (QA) are also emerging, opening new doors to AI application testing.

How are engineering leaders testing AI and ML applications in 2023? Here are the top 10 AI application testing methods you need to know.

1. Data Quality Testing

Use benchmarks to assess the state of your data. While each company's objectives may vary, high-quality data generally means the data is:

• Free of errors: There are no typos or any issues with structure and format.
• Consolidated: The data is secured in one centralized system instead of scattered across multiple systems.
• Unique and one-of-a-kind: The data is not duplicated.
• Up-to-date: The information it presents is timely and relevant.
• Accurate: It offers precise information to help your business make informed decisions.

Testing data quality means identifying mislabeled, obsolete, or irrelevant data by comparing a business's information with established known truths. At this level of testing, it can be as simple as creating a data profile for a dataset, a process known as synthetic data generation. Using the defined validations of this dataset, companies can classify whether their data is valid or not and, therefore, measure its quality.

2. Testing for Bias

Another important test that's increasing in popularity is bias testing. The bias of an artificial intelligence system heavily depends on the data it collects.

For example, a 2016 report found Amazon biased toward male IT applicants. When the eCommerce giant trained its AI bots to find the best candidates for the job, it used current employees' resumes as databases, which were predominantly male. Based on this information, their AI surmised that only male candidates would make the best IT employees, which is not true.

To avoid making the same mistakes, you should conduct testing for bias when you push your algorithms online.

Back in 2016, testing for bias was only a matter of analyzing the requirements to establish the appropriate response to a set of inputs. Now, it's not as clear-cut. You need more variety and more options. You want to create multiple test cases to account for all possible variables instead of producing just one scenario using one dataset.

While the results may not always be perfect, they still provide a better, fairer, and more comprehensive approach to eliminating bias and developing more inclusive applications of AI.

3. AI Model Evaluation and Testing

AI model evaluation and testing help you predict results from analysis and evaluation. It involves three steps:

1. Splitting the dataset
2. Tuning the hyperparameters
3. Performing normalization on the batch

Splitting the Dataset

During the first phase of AI testing, the data collected is separated into training, validation, and test sets.

The training set includes up to 75% of the dataset and assigns model weights and biases.

The validation set consists of 15% to 20% of the data during training to evaluate initial accuracy and see how the model adapts, learns, and fine-tunes hyperparameters. At this stage, the model only considers the validation data but doesn't use it yet to learn the model's weights and biases.

The test set comprises five to 10% of the overall dataset. This is used for the final evaluation as a controlled set, free of biases.

Tuning Hyperparameters

The second phase of the testing process is tuning hyperparameters. At this stage, developers can control the behavior of the training algorithm and adjust the parameters based on the results of the first phase.

In the context of AI and deep learning, the possible hyperparameters can include:

• Learning rate
• Convolution kernel width
• Number of hidden units
• Regularization techniques

Performing Normalization

Finally, performing batch normalization involves two techniques- normalization and standardization — to transform data on the same scale during training preparations.

Once the AI model has been sufficiently trained, fine-tuned, and normalized, it's time to gauge its performance through the confusion matrix, AUC ROC, F1 Score, and other precision/accuracy metrics.

Going through this rigorous process is essential to see how effective and accurate your algorithms are performing.

4. Security Testing

Testing the security of your AI applications requires a combination of traditional security testing approaches and considerations specific to AI systems. Consider the following points to get started:

• Understand AI concepts: Familiarize yourself with the fundamental concepts and components of AI, such as machine learning algorithms, data pre-processing, and model training. This understanding will help you identify potential security risks and attack vectors specific to AI applications.
• Identify security goals and risks: Determine the security goals and potential risks associated with the AI application. Consider aspects like data privacy, model integrity, adversarial attacks, and robustness to input variations. This step will help shape your testing strategy.
• Data security: Evaluate the data security used for training, validation, and inference. Assess data privacy, storage, handling practices, and access controls. Ensure that sensitive data is properly protected and privacy regulations are followed.
• System architecture and infrastructure: Analyze the architecture and infrastructure of the AI application. Consider security aspects like authentication, authorization, and encryption. Verify that security best practices are followed in the design and implementation of the system.
• Input validation and sanitization: Pay attention to input validation and sanitization mechanisms. Verify that the application handles input data properly to prevent common vulnerabilities, such as injection attacks or buffer overflows.
• Third-party components: Evaluate the security of any third-party libraries, frameworks, or components used in the AI application. Ensure they are up-to-date, free of known vulnerabilities, and properly configured.
• Security testing tools: Utilize security testing tools specifically designed for AI applications, such as fuzzing or code analysis tools tailored to machine learning models.
• Documentation and reporting: Document your findings, recommendations, and test results. Create a comprehensive security testing report outlining the identified vulnerabilities, risks, and mitigations.

5. Performance and Scalability Testing

To conduct performance testing of an AI application, it is crucial to have a comprehensive understanding of the application's architecture, components, and data flow. Volume testing, endurance testing, and stress testing are the most crucial performance testing types that must be performed on AI applications to assess their performance and scalability. This can be achieved with different test data, including large and small test data sets, since extensive test data takes up more computing resources.

You can measure scalability by running performance tests for increasing requests and an extended duration. Also, parallel monitoring of hardware resources helps set up the correct configuration to support anticipated user requests for AI applications.

6. Metamorphic Testing

It involves generating test cases and verifying test results by using metamorphic relations. These relations help validate an algorithm's response to various inputs and their expected outputs. It also includes testing metamorphic relations and generic relations, which capture relationships between inputs and outputs.

Metamorphic AI testing primarily aims to assess various AI models' behavior after any changes in input data (also known as perturbations).

7. Computer Vision Applications Testing

The goal of computer vision application testing, especially when it comes to ML and deep learning, is to decipher and analyze visual data (including images, videos, and graphics) through a human lens.

It does so by analyzing visual data in three formats: data annotation, data labeling, and data ingestion.

• Data Annotation: This provides vital information and insight concerning the image or video by highlighting (or annotating) key elements.
• Data Labeling: Data labeling adds more informative and meaningful labels to the visual reference to establish a more comprehensive context.
• Data Ingestion: Data ingestion is the process of organizing these details and storing them in the corresponding databases for people to use.

Through computer vision application testing, systems can derive valuable information from visual data and take appropriate actions by providing relevant recommendations based on the data collected, annotated, labeled, and ingested.

8. NLP Applications Testing

Evaluating speech and natural language processing (NLP) models involves testing their recognition and prediction capabilities. This testing often relies on metrics such as word error rate (WER) and text similarity measures like cosine similarity and Levenshtein distance. These metrics help assess the accuracy and performance of NLP models in tasks such as speech recognition and text prediction.

9. Chatbot Testing

With chatbots gaining popularity in the application of AI, it's essential to ensure that the information these bots offer users is accurate. If your business makes use of chatbot features, you must test the chatbot's functional and nonfunctional components.

• Domain testing: Chatbots are designed to handle specific domains or topics. Domain testing involves thoroughly testing the chatbot on optimistic scenarios related to its designated domain. This ensures the chatbot understands and responds accurately to queries within its intended scope.
• Limit testing: Limit testing assesses how a chatbot handles inappropriate or unexpected user inputs. This involves testing the chatbot's response to invalid or nonsensical questions and identifying the outcome when the chatbot encounters failure or errors. Limit testing helps uncover potential vulnerabilities and improves error handling and user experience.
• Conversational factors: Chatbots rely on conversation flows to provide meaningful and engaging interactions. Validating different conversation flows is crucial to evaluating the chatbot's responses across various scenarios. This includes assessing the chatbot's ability to understand user intent, handle multiple turns in a conversation, and provide relevant and coherent responses. Evaluating conversational factors helps optimize the chatbot's conversational skills and enhances the user experience.

10. Robotics Testing

Robotics testing simulates real-world scenarios and evaluates the behavior of systems or algorithms in these scenarios. Simulation-based behavior testing includes algorithm debugging, object detection, response testing, and validating defined goals.

To ensure comprehensive testing, you should employ both low-fidelity 2D simulations and high-fidelity 3D simulations. Use the former for module-level behavior testing and the latter for system-level behavior testing. This allows you to examine different levels of complexity and accuracy in the simulations.

The process also tests hardware availability scenarios and hardware unavailability scenarios. These scenarios evaluate the behavior and performance of the system or algorithm under varying hardware conditions, ensuring robustness and adaptability in different environments.

Prioritize Your AI Application Testing  

The rapidly evolving landscape of software applications calls for innovative approaches to AI application testing. If your business uses or offers AI solutions, you must prioritize comprehensive testing methodologies to ensure accuracy, security, and inclusivity.