Testing When it comes to artificial intelligence (AI) projects, quality assurance is essential. However, there’s a difference between testing in AI projects and in other software projects.
Unlike traditional software, AI systems are continuously learning and evolving. Therefore, they have to continually be managed and tested. To ensure proper performance, your testing strategy may differ from the one you usually apply to non-AI systems.
Artificial Intelligence: definition, related terms, and types
Testing AI systems requires QA engineers to understand what artificial intelligence is and how it works. Let’s start by defining some basic terms.
Artificial intelligence, or AI, enables machines to perform functions associated with the human mind, such as:
- Image/audio/video recognition
- Problem solving
AI-driven systems include software, applications, tools, and other computer programs.
AI systems learn and gain experience with the help of machine learning, or ML — a branch of AI that’s concentrated on data analysis. The results of this analysis are used to automate the development of analytical models. In other words, ML allows machines to learn.
Another essential AI-related term is deep learning (DL), a subfield of ML concerned with processing data and identifying patterns for use in decision-making. Deep learning is based on artificial neural networks — sets of algorithms designed to recognize patterns and learn to perform tasks based on examples, without being programmed with task-specific rules.
AI implementation types
For testing AI solutions, it’s essential to understand the way AI works and to know the basic classification of AI systems.
While there are many ways to classify AI systems, we can determine four categories depending on how advanced they are. There are two existing types of AI systems: reactive machines, such as those applied in IBM’s Deep Blue, and limited memory machines, such as those used in self-driving cars.
What should you know before testing an AI system?
Despite various myths that AI can be 100% objective and work like the human brain, AI systems still can go wrong. Therefore, thorough testing is required.
AI systems have to be tested at each stage of development. All the usual approaches that QA engineers take to testing at different development phases are applied here. For instance, you need to analyze requirements to check for inaccuracies, achievability, and executability. Also, you have to develop a testing strategy according to the system’s architecture.
The first thing to do is define whether you’re testing an application that consumes AI-based outputs or is an actual machine learning system.
Applications that consume AI-based outputs usually don’t require a special approach to testing. You can apply black-box testing techniques, just like when testing regular deterministic systems. Focus on checking whether the application behaves correctly when presented with an output from the AI.
However, If you’re faced with machine learning systems, your testing strategy will be more complicated. While standard testing principles are applicable to AI testing, you can also use white-box testing techniques. Additionally, a QA specialist should have:
- experience working with AI algorithms
- knowledge of programming languages
- the ability to correctly pick data for testing
Make sure to get all information from developers about datasets used to train the network. Usually, this data is divided into three categories of datasets:
- Training dataset — data used to train the AI model
- Development dataset — also called a validation dataset, this is used by developers to check the system’s performance once it learns from the training dataset
- Testing dataset — used to evaluate the system’s performance
Knowing how these datasets play together to train a neural network will help you understand how to test an AI application.
Key challenges in testing AI systems
AI systems may be used for various purposes, have different architectures, and offer unique challenges to QA engineers. However, when testing any AI system, you’ll meet three major challenges.
1. Different outputs
The fact that a learning system will change its behavior over time brings some challenges.
For instance, implementing a test oracle in AI algorithms is impossible without human intervention. An oracle refers to a mechanism or another program used to determine whether a system is working correctly. However, if you take image recognition, for example, you need humans to check whether images are labeled correctly.
Testers have to check certain boundaries within which the output must fall. Predicting the output of AI systems may be an issue because they’re constantly learning, training, analyzing mistakes, and adjusting themselves to new information.
2. The issue of bias
Some myths claim that AI is free of bias, which unfortunately isn’t true.
Training datasets are provided by humans. Therefore, there’s always a risk that AI solutions will be biased. There are several ways to deal with the problem of bias in AI. For instance, you can deploy ML algorithm evaluation tools to check a system for possible unfairness.
Also, both developers and QA engineers have to think about using diversified datasets when training and testing an AI system. However, a lack of data poses its own challenges.
3. Lack of data
AI systems may be used to handle different tasks: analyzing users’ preferences to offer them similar products, processing images, videos, or voice records, etc. Data required for learning may be very specific and complicated to get.
Also, the performance of some AI systems can worsen as the accuracy of the output declines with time. This situation can be caused by changeable user behavior or dynamically changing data. One way to solve this issue is to regularly retrain the network with a new dataset, which can be costly or even impossible because of a lack of data.
We hope this article has helped you grasp how to test AI applications. Hopefully, our tips will help you form a mind map that you can use to cover the most critical parts of your system with tests. Thus, you can reduce the number of errors in your AI system and minimize the amount of time and resources spent fixing them.