10 minutes to read
Best QA strategies to test your AI solution
Anastasiia Sokolinska
Chief Operating Officer
AI is saturating all areas of life and transforming business and technology. Over 314 million people are using AI-based applications. 57933 AI companies are currently operating in the world, and the global artificial intelligence market is expected to grow to $1.85 trillion by 2030.
It's important to know how to test AI solutions properly and effectively to avoid being lost among competitors and lead your product to success. Below, you can find a comprehensive artificial intelligence testing tutorial with best practices and useful recommendations.
5 reasons why AI-based software needs thorough testing
All companies are striving to implement and improve techniques for testing AI systems. Why is it so crucial? Here are 5 main reasons.
The need to control the quality of information
The outcomes of AI-driven applications’ work directly depend on the quality of data they were trained on. Artificial intelligence “remembers” and analyzes information in a significantly different way than the human brain does. Therefore, the information that is suitable for human learning may not always be correctly processed by AI models. As a consequence, it is very important to correctly select, collect, and adapt data sets for training AI-based systems.
Innovate boldly. Test fearlessly. Launch confidently
Poor data quality can lead to numerous problems in functioning that negatively affect business performance and customer satisfaction. This applies to all AI-driven applications and systems, regardless of application type. For example:
Inaccurate work of AI algorithms that automate routine business processes can lead to incorrectly performed work and reporting errors.
Imprecise operation of healthcare applications can provoke misdiagnosing or inappropriate treatment.
Incorrect functioning of AI algorithms of robotic production leads to defects in final goods, and so on.
The data must be accurate, unambiguous, complete, and representative. The creation and maintenance of AI systems require expanding and adding new data blocks for continuous improvement. Therefore, it is crucial to continuously test, monitor, and improve the its quality.
Eliminating bias issues in AI
Bias concerns are very common when it comes to AI-powered applications. There can be a lot of inconveniences and problems because of bias, for instance:
Healthcare AI apps may misdiagnose due to incorrect assessments of certain demographic groups.
Fintech applications' security algorithms may work incorrectly, causing unreasonable recognition of actions as suspicious or fraudulent.
HR apps may show discrimination against certain groups of candidates.
Natural language processing models may give incorrect or repetitive answers.
To avoid such inaccuracies, it is important to thoroughly test the AI system for bias and eliminate it as much as possible by providing the AI model with more diverse datasets based on assorted demographic groups.
Avoiding ethical concerns
Bias issues may entail some ethical concerns related to discrimination. Also, poor selection of training information can provoke inappropriate or harmful responses from AI. To minimize such risk, information for training AI models should be carefully selected with ethical considerations in mind.
Enhancing target audience trust and customer satisfaction
Product popularity and success depend on the satisfaction of existing customers, as well as the developing trust of potential ones. To improve the apps' credibility and usability, it is essential to test it rigorously and fix all possible defects.
Guaranteeing regulatory compliance
To avoid legal problems and penalties, when developing and maintaining AI-based applications, it is vital to make sure that they comply with such laws as:
EU artificial intelligence act, Europe's most comprehensive and major law concerning AI systems.
General data protection regulation (GDPR), a European law that regulates the collection, storage, and processing of users' personal data.
Ethical regulations like The Institute of Electrical and Electronics Engineers (IEEE) AI Ethics Framework, The European Union (EU) Ethics Guidelines for Trustworthy AI, and the Organisation for Economic Co-operation and Development (OECD) AI Principles.
Anti-discrimination laws.
Health insurance portability and accountability act (HIPAA), the US law that may regulate healthcare applications based on artificial intelligence.
3 main challenges of testing AI applications and how to overcome them
Testing AI models is vital, however, quite challenging. What difficulties await you during quality assurance of the AI-powered applications? Here are the top 3 most widespread challenges in AI quality assurance.
Variability of performance results
Traditional applications produce the expected programmed results. Meanwhile, the outcomes of the work of AI systems can be quite unpredictable due to constant learning, development, and changes. Due to unforeseeable performance, it can be difficult to select and implement appropriate test cases, as well as evaluate the testing results.
How to test AI, and overcome this challenge? Here are a few tips:
Use multiple performance metrics instead of one. Some of the main ones are accuracy (the ratio of true results to the total number of them), precision (the rate that shows the accuracy of positive predictions by calculating the number of true positive results to all positive ones), recall (the ratio of true positives to the sum of true and false positives), and F1 score (the harmonic mean of precision and recall).
Test the model on different data sets. This will help you to ensure that the AI system works correctly with information of different specifics.
Use k-fold cross-validation for AI/ML testing. This is a statistical method that evaluates the skills of an ML model on new data.
Conduct real-life testing. Involve real users to make sure the system produces correct results based on their information.
Unforeseen changes
AI/ML models are capable of self-learning, so they constantly evolve and change. This can be a challenge for QA specialists. Here are a few ways of how it is possible to overcome it:
Test explainability. Examine the information about the reasons and evidence of the AI model’s behavior and outcomes. Assess how well the decisions of artificial intelligence can be explained.
Implement continuous testing. It is not enough to test an AI system once to make sure it works correctly now and will in the future. It is necessary to conduct regular tests to track changes in all the nuances of operation.
Use automation to the maximum. Constantly running tests manually may require an excessive amount of human, time, and financial resources. Therefore, automate processes as much as possible by using specialized tools.
Difficulty of full test coverage
AI systems are so complex and have so much potential input space that covering them completely with tests can be an impossible mission. This can be too time-consuming and costly. However, here are some recommendations to overcome this challenge:
Give due consideration to prioritization. This will help primarily cover the most crucial areas with tests. These may be the most critical features and properties, as well as the zones with the highest risk of problems.
Automate the process of writing test cases and setting up the test environment. This will help to significantly speed up and cheapen the process of increasing coverage by tests.
AI testing tutorial: 3 best techniques
It is vital to continuously ensure the quality of AI systems’ operating, their performance, fairness, accuracy, and unbiasedness. What are the best methods of how to test AI models? Here are the 3 most effective ways.
Adversarial testing
This is an essential method of testing generative AI. It evaluates how the AI-based application behaves when it receives harmful or malicious input. This type of testing helps to determine how vulnerable a system is to attempts to intentionally or unintentionally make it give an unacceptable response that violates some safety policies or ethical rules.
To properly execute this method, it is necessary to develop many test cases with a variety of data sets. It is essential to ensure that information blocks are diverse and are different in:
Lexicon. Develop queries that vary in length, wording, synonyms, slang, regional dialects, question types (direct and indirect), etc.
Semantics. Prepare cases that cover as many topics as possible, including sensitive and ethical ones. For example, those relating to gender, race, nationality, and so on.
Policy. Think through requests for violations of various policies. For example, different groups of inputs “provoke” AI for harmful advice, profanity, discriminatory language, and so on.
Pairwise testing
This is a method that provides for testing the performance of possible combinations of different pairs of input parameters. This approach is very relevant for AI applications, which are complex systems with a great number of parameters that need to be checked.
This technique helps to reduce the number of test cases required to fully check functionality and maximize coverage without the loss in QA quality. This, in turn, entails many benefits associated with saving:
Time. The fewer test cases it requires to test the software to the full, the less time it takes. This is a great advantage if you have tight deadlines. It also can allow you to shorten the time to market and beat competitors.
Human resources. The fewer test cases are necessary and the faster it is possible to conduct the full QA cycle, the fewer qualified specialists you will need for this.
Budget. The fewer QA engineers are involved in the process and the fewer working hours they spend, the less financial expenses the business needs to allocate. Also, getting the test cycle done faster will allow the company to avoid lost revenue due to delayed time to market. The sooner a product is released, the sooner it starts generating income.
Experience-based testing
AI software, unlike the traditional one, can have unclear specifications. Unpredictable results of the AI model’s functioning, as well as difficulties in assessing the quality of its work, can make testing an intricate task. In such a case, using an experience-based approach can help. It may include the following types:
Error guessing. It predicts the maximum possible defects based on previous experience.
Exploratory testing. Within this method, a QA engineer learns the software, creates tests, and runs them simultaneously. The specialist creates each new group of tests based on the results of executing the previous ones.
Checklist-based testing. Within this technique, a professional studies the product and its specifications in detail. Then, a QA specialist creates a multilevel to-do list and follows it while creating and executing tests.
Attack testing. This method identifies possible software vulnerabilities. It is vital for all AI systems, and especially for those applications that work with sensitive customer data, for example, in the fintech or healthcare niche.
Conclusion: Why and how to test artificial intelligence
Quality assurance is a crucial step in creating and maintaining an AI-based solution. Testing AI applications thoroughly and properly helps to prevent a lot of problems, such as:
Poor or incorrect results of the AI system’s work due to insufficient quality of information it was trained on.
Bias issues that can lead to inappropriate outputs.
Ethical problems that may occur due to poor-quality training data or the presence of bias.
Lack of trust from potential users or low customer satisfaction rate.
Trouble with the law due to not complying with regulations.
AI models are very complex and variable, which makes it quite challenging to test them. It may be difficult to forecast functioning changes and performance fluctuations, as well as reach the complete test coverage of such an intricate system.
However, it is possible to overcome them with the most appropriate techniques, such as adversarial, pairwise, and experience-based testing. Also, the process will become easier and more effective if you follow some useful tips like:
Using a large number of diverse data sets.
Applying several performance metrics like accuracy, precision, and recall for ongoing evaluation of the model’s work.
Implementing such techniques as real-life testing and k-fold cross-validation.
Automate as many processes and tasks as possible to save time, financial and human resources.
The DeviQA team of 250+ engineers has deep experience in AI/ML testing. With the best practices and the most relevant tools, our dedicated software testing services provide the maximum coverage for AI-based applications testing.
Does your AI solution need testing? Contact DeviQA for a free professional consultation!
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