Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems use algorithms, statistical models, and computational power to analyze data and make predictions or decisions based on that data.
Machine Learning (ML) is a subfield of AI that involves teaching machines to learn from data and improve their performance without being explicitly programmed. Machine learning algorithms can analyze large datasets, identify patterns, and make predictions or decisions based on that data.
Artificial intelligence and machine learning (AI/ML) have become increasingly important in testing software, resulting in increased automation and more lifelike results. AI/ML testing is essential to ensure that AI/ML models produce accurate and reliable results.
AI/ML models are trained using large datasets, and it can be challenging to ensure that they are working correctly and producing the expected results in different scenarios. The testing process helps identify errors, biases, and other issues in the model, making it more reliable and effective. Additionally, AI/ML testing helps improve the model's transparency and interpretability, making it more trustworthy and easier to use.
Imperatives for AI system testing
The global AI market is expected to reach nearly $126 billion by 2025, up from just $10.1 billion in 2016. With AI becoming more prevalent, it's crucial to ensure that these systems are tested thoroughly. Originally, AI was conceived as a technological concept to replicate human intelligence. It was mostly researched and developed within the confines of large technology companies.
However, in recent times, AI is transformed into an indispensable resource for every type of organization, thanks to significant advancements in data collection, processing, and computing power. In essence, AI has become the "new electricity" for businesses of all kinds.
Challenges of AI and ML testing
AI and ML systems are non-deterministic. This means that they tend to show different behaviors for the same input.
Adequate and Cccurate Training Data
One of the biggest challenges of AI and ML testing is the lack of test data. ML algorithms rely on vast amounts of data to learn, but it can be challenging to obtain sufficient data that accurately represents the real-world scenarios that the system will encounter. This can make it tough to test the system thoroughly and accurately.
Testing for bias can be challenging, as it requires a thorough understanding of the training data and the potential sources of bias.
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.
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 to new data and input.
Common obstacles in AI application testing
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 a lot of time and effort is consumed to resolve them.
- AI is a complex system, and even minor defects are greatly amplified. The difficulty in this problem resolution also increases.
Challenges in testing ML applications
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, such as test coverage, are often ineffective when testing machine learning applications.
- 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.
- As it is challenging to identify trustworthy carpenters, ML testing frequently indicates false positives in defect reports.
Key factors to consider while testing AI-based solutions
When evaluating AI-based solutions, it's important to remember that data is the new code. For 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.
Several steps must be taken to create effective and accurate machine learning models, such as:
Semi-Automated Curated Training Data Sets
For this step, the input data and intended output are essential. We need to analyze data dependencies statically to annotate data sources and features. This analysis is crucial for migration and deletion.
Test Data Sets
Test data sets are created to determine the efficacy of trained models. These data sets are logically constructed to test all possible combinations and permutations. The model gets refined during training as the number of iterations and data richness increase.
System Validation Test Suites
Algorithms and test data sets are used to create system validation test suites. These test suites must include various test scenarios, such as risk profiling of patients for the disease in question, patient demography, and patient therapy, for a system designed to predict patient outcomes based on pathology or diagnostic data.
Reporting Test Findings
Test results must be presented in statistics since machine learning algorithm validation yields range-based accuracy or confidence scores rather than expected outcomes. For each development, testers must specify confidence criteria within a given range.
Taking note of fundamental biases
Fundamental biases are essential to take note of during AI/ML testing. For modern enterprises, developing unbiased systems has become critical. Supervised learning techniques, which make up more than 70% of AI use cases today, often rely on labeled data prone to human judgment and biases.
It 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 is a common problem in machine learning, especially in sentiment analysis. Most data sets do not have equal or sufficient data points for different sentiments, leading to skewed training data.
In a well-functioning system, the distribution of predicted labels should match the distribution of observed tags. This diagnostic step is crucial to detect problems, such as sudden changes in behavior. If the training distributions based on historical data are no longer accurate, it can lead to prediction bias.
Users' understanding of how to solve a data pattern or problem set is often constrained and biased by their knowledge of relational mapping, leading them to favor more familiar or simple solutions. This bias can result in a solution that avoids complex or unfamiliar alternatives.
Critical aspects of AI systems testing
Data Curation & Validation
The efficiency of AI systems 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. It proves 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.
- Natural Language Processing
- Image Processing
- Machine Learning
- Deep Learning
Performance and Security Testing
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
The potential of AI
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 multiple industries. The widespread adoption of AI is expected to help uncover its full potential and increase efficiencies in various industries in the coming years.
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. 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 was 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 the annotation of data sources and features, a key aspect 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 ML models:
Model Performance Testing
It 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 was previously constructed and placed into production.
It 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 know 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 one 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.
Data fed into the ML models are designed to verify all feature activations using guided fuzzing. Test data sets that result in the activation of each of the neural network's neurons/nodes, for example, are required for a model produced with neural networks.
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 with 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, recent transactions, and the ability to transfer money across accounts. Chatbots can be influenced into providing business-sensitive information without proper testing.
AI/M-based tools for 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 three 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 just like 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 Sauce Labs. It supports a wide range of browsers, operating systems, mobile emulators, simulators, and mobile devices. It 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 available.
As AI and ML become more prevalent in our lives, it's crucial to ensure these systems are thoroughly tested to work as intended. 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.