A complete guide to testing AI and ML applications
Standard vehicle testing software already includes artificial intelligence and machine learning (AI/ML), resulting in increased automation and more life-like results. To achieve increased automation, faster adaptability, and efficient performance, development and testing teams are using AI/ML technology more and more. Similarly, Artificial intelligence and machine learning algorithms are implemented in various applications and industries. And that is why AI-assisted application testing is a critical strategy for improving operational efficiency, product iterations, and development cycles.
Imperatives for AI systems testing
It is said that the global market of AI is expected to be almost $60 billion by 2025 when in 2016, it was just at $1.4 billion. AI is everywhere, and it is ruling the world.
AI was envisioned as a technical idea that could emulate human intelligence. Research on AI used to be confined to large technology companies. Instead, today, AI has become the new electricity for every organization, thanks to significant breakthroughs in data collecting, processing, and computation power.
Challenges of AI and ML testing
- Non-deterministic: AI and ML systems are non-deterministic. This means that they tend to show different behaviors for the same input.
- Adequate and accurate training data: The ML model depends on labeled data. 80% of a scientist's time is spent preparing the training dataset.
- Bias: When the training dataset is distributed, it can lead to bias.
- Explainability: There is a massive level of difficulty when extracting specific attributes. For example, finding what caused a system to recognize an image of a coupe as a sedan wrongly may not be possible.
- Sustained testing: After testing and validating traditional systems, we do not need to retest them until we modify the software. On the other hand, it is imperative to know that AI/ML systems constantly learn, train, and adjust with new data and input.
Massive sensor data pose storage and analytics issues and result in noisy datasets. Here are some common obstacles faced while testing AI systems/applications:
- Data obtained during unplanned events is exceedingly challenging to aggregate, offering training problems to AI systems.
- Human bias is typically present in training and testing datasets. It should be identified and removed in AI model test situations.
- AI works well with sophisticated input models. If the inputs are not up to the mark, the defects are more complex, and to resolve them, a lot of time and effort is consumed.
- AI is an already complex system, and even minor defects in the system are greatly amplified. The difficulty in this problem resolution also increases.
Testing machine learning applications are also difficult and pose several self-assessment challenges:
- The data, code, curricula, and frameworks that support ML development must be thoroughly tested.
- Traditional testing methods, like test coverage, are ineffective.
- The behavior of your ML model may change each time the data training is updated.
- As domain-specific information is necessary, creating a test or test (e.g., labeling data) costs time and money.
- Since it's so challenging to identify trustworthy carpenters, ML testing frequently indicates false positives in defect reports.
Key factors to consider when testing AI-based solutions
For AI-based solutions, data is the new code. So, to have a well-functioning system, these solutions must be tested for any change in input data. This is similar to the classic testing approach, in which any changes in the code cause the improved code to be tested. When evaluating AI-based solutions, there are a few things to keep in mind:
- Creating semi-automated curated training data sets: Semi-automated curated training data sets include input data and intended output. Static data dependency analysis is necessary to annotate data sources and features, a key component for migration and deletion.
- Creating test data sets: Test data sets are logically constructed to test all conceivable permutations and combinations to determine the efficacy of trained models. As the number of iterations and data richness increase, the model is refined throughout training.
- Creating system validation test suites: Algorithms and test data sets are used to create system validation test suites. For example, test scenarios for a system designed to predict patient outcomes based on pathology or diagnostic data must include risk profiling of patients for the disease in question, patient demography, patient therapy, and other similar test scenarios.
- Reporting test findings: Because ML-based algorithm validation yields range-based accuracy (confidence scores) rather than expected outcomes, test results must be written in statistical terms. For each development, testers must determine and specify confidence criteria within a given range.
Fundamental biases are essential to take note of during AI/ML testing
For modern enterprises, developing unbiased systems has become critical. Supervised learning techniques, encompassing more than 70% of AI use cases today, have labeled data prone to human judgment and biases. This creates a double-edged sword for measuring the bias-free quotient of the input training data sets. We miss out on experiential information if we don't factor the human experience into labeled data. And even if we do, data biases are likely to emerge.
- Data skewness: The data we use to train the model is frequently skewed. Sentiment analysis is the most common example: most data sets do not have an equal (or sufficient) amount of data points for different sentiments.
- Prediction bias: In a well-functioning system, the distribution of predicted labels should match the distribution of observed tags. While this isn't a complete exam, it is an instrumental diagnostic step. Changes in data like this are frequently symptomatic of a problem that needs to be addressed. This strategy, for example, can be used to discover circumstances where the system's behavior changes abruptly. In such instances, training distributions based on historical data are no longer accurate representations of the current situation.
- Relational bias: Users are often constrained and biased in their understanding of how to solve a data pattern or problem set, depending on their knowledge of the relational mapping of which solution would have worked for a specific type of problem set. This, on the other hand, may tilt the solution toward what a user is familiar with, avoiding more complex or unfamiliar alternatives.
Critical aspects of AI systems testing
Data Curation & Validation
An AI system’s efficiency is based on the quality of training data, including aspects like bias and variety. Car navigation systems and phone voice assistants find it quite troublesome to understand different accents. This entails that data training is critical for AI systems to get the correct input.
Algorithms, which process data and provide insights, are at the heart of AI systems. This approach's primary advantages include model validation, learnability, algorithm efficiency, and empathy.
- Image processing
- Machine learning
- Deep learning
Performance and security testing are non-functional
AI systems require extensive performance and security testing. Aspects such as regulatory compliance are also included.
Smart interaction testing
- Devices (Siri, Alexa, etc.)
- Driverless cars
Integration testing of systems
Artificial Intelligence (AI) research used to be confined to large technology companies, and it was envisioned as a technical idea that could emulate human intelligence. On the other hand, AI has become the new electricity for every organization, thanks to significant breakthroughs in data collecting, processing, and computation power. The AI sector has exploded in the last several years, with applications covering a wide range of industries. The widespread adoption of AI is expected to help uncover its true potential and increase efficiencies in various industries in the coming years.
Integration testing of systems
AI systems are created to work with other systems and tackle specific challenges. This necessitates a comprehensive analysis of AI systems. Integration testing is critical when numerous AI systems with competing agendas are deployed together.
AI systems are created to work with other systems and tackle specific challenges. This necessitates a comprehensive analysis of AI systems. Integration testing is critical when numerous AI systems with competing agendas are deployed together. According to Gartner, the worldwide business value of AI was expected to exceed $1.2 trillion in 2018, up 70% from 2017. By 2022, this market is estimated to reach $3.9 trillion. With more and more systems incorporating AI features, they must be adequately evaluated.
Semi-automated curated training data sets include input data and intended output. Static data dependency analysis is necessary to enable annotation of data sources and features, a key feature for migration and deletion.
Black Box and White Box testing
Like traditional test methods, black box and white box testing are used for ML models. Obtaining training data sets that are large and thorough enough to suit the objectives of ML testing is a significant difficulty.
Data scientists test the model's performance during the development phase by comparing the model outputs (predicted values) to the actual values. The following are some of the strategies used to do black-box testing on machine learning models:
- Model performance testing: entails comparing the model's performance in terms of precision-recall, F-score, and confusion matrix (False and True positives, False and True negatives) to that of a predetermined accuracy with which the model has previously been constructed and placed into production.
- Metamorphic testing: attempts to solve the problem of the test oracle. A test oracle is a method that allows a tester to assess whether a system is functioning correctly. It is challenging to determine the expected outcomes of selected test cases or determine if the actual output matches the expected results.
- Dual coding/Algorithm Ensemble: Given the same input data set, multiple models utilizing various algorithms are created, and predictions from each of them are compared. Numerous methods, such as Random Forest or a neural network like LSTM, could design a typical model to address classification difficulties. However, the model that gives the most expected outcomes is ultimately chosen as the default.
- Coverage data fed into the ML models are designed to verify all feature activations using guided fuzzing. Test data sets that could result in the activation of each of the neural network's neurons/nodes, for example, are required for a model produced with neural networks.
Backtesting of the model
A predictive model based on historical data is known as backtesting. This method is widely used in the financial sector to estimate the performance of previous models, particularly in trading, investment, fraud detection, and credit risk evaluations.
Testing for Non-Functional Requirements (NFR)
A representative sample view of things and the deployment approach must be considered while evaluating ML Models, apart from performance and security testing. AI systems require extensive performance and security testing. Aspects such as regulatory compliance are also included. HSBC's Voice Recognition System was recently hacked by a customer's non-identical twin, who gained access to balances and recent transactions, as well as the ability to transfer money across accounts. Chatbots can be influenced into providing business-sensitive information without proper testing.
Tools for AI/ML testing
There are many AI-based QA products on the market, each with its own set of features. Here's a quick rundown of the four most commonly used AI tools in software quality assurance.
Applitools is a visual UI testing and monitoring program powered by artificial intelligence. It is a visual AI-powered end-to-end software testing platform that can be used by engineers and manual QA, as well as test automation, DevOps, and Digital Transformation, teams. Furthermore, the AI and ML algorithm is completely adaptive—it scans and analyses the app displays in the same way that a human eye and brain would, but with the power of a computer.
It's an AI and ML-based automated functional testing platform that speeds up automated tests' creation, execution, and management. Chrome, Firefox, Edge, IE, Safari, and Android are among the browsers and operating systems that can use the tool. This AI-powered software testing platform lets customers develop robust end-to-end tests that can be programmed or left codeless or both. Testim's original cycle model is responsible for its success and popularity.
Another popular cloud-based test automation solution that uses ML and AI is this one. Sauce Labs supports a wide range of browsers, operating systems, mobile emulators, simulators, and mobile devices. It also works at the pace that its consumers require. It also claims to be the world's largest continuous testing cloud, with over 800 browser and operating system combinations, 200 mobile emulators and simulators, and hundreds of genuine devices to choose from.
Finally, the number of factors that must be considered when deploying an AI/ML model into production differs significantly from typical software testing methods. The regularity with which the AI model is tested for accuracy affects the previous 'test once and deploy forever' strategy. As businesses increasingly use AI to construct systems and applications, testing approaches and procedures will evolve and improve over the next few years, eventually approaching the maturity and standardization of traditional testing methods.
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