Modern QA challenges for AI-based solutions

Evaluate and improve the quality. Understanding the overall level of quality allows businesses to determine the future development strategy for their projects.

Find and eliminate flaws. During testing, engineers identify errors, inaccuracies, and illogicalities in operation. This makes it possible to understand what needs to be fixed for a more correct and efficient operation of the application.

Identify and minimize bias. This is a very common problem in AI applications, especially in NPL models. It is very important to detect and eliminate it for the efficient and problem-free operation of the software.

Recognize and get rid of ethical issues. Problems of this type can be a stumbling block to the success of a product. Their presence can lead to a significant decrease in customer satisfaction and even provoke some legal troubles. Therefore, it is very important to identify and eliminate them early on.

Ensure regulatory compliance. In each area, there are regulations, for example, concerning the collection, storage, and use of data. It is vital to comply with them to avoid problems with the law.

Enhance security. For most users, this criterion may be one of the most critical, as often the use of AI-based applications involves the transmission of sensitive data. Testing helps identify gaps in protective measures and vulnerabilities.

Increase performance. Imperfections in the code can limit the productivity of the application. Testers can check how it performs under a certain load. This can help to identify problems and get rid of them. As a result, the product will be able to work even more efficiently under higher loads.

Boost customer satisfaction. The presence of bugs and defects in operation can significantly worsen the user experience. After getting rid of them, the application can become faster, safer, more convenient, and more productive to make customers happier.

Intricate algorithms. This type of software operates according to complex mechanisms that involve the ability to “learn”, “decision-making” based on the information received, the autonomous performance of certain tasks, “prediction” based on the history of previous events or data learned, and so on.

Ongoing learning ability. For AI-driven solutions to work effectively, they must constantly replenish their “knowledge base”. This leads to development and metamorphosis in the processes of functioning.

High changeability. This software has very dynamic operating algorithms that improve constantly and may also be sensitive to changes in input data. As these systems are “learning” all the time, the outcomes of their work may get modified over time or vary from case to case.

Collecting industry-related information and verifying it against domain standards.

Distribution of data into clusters depending on the context and checking the correctness of this sorting.

Identifying and removing sample data sets.

Deletion of meaningless sections in the information array.

Adding labels to provide the right context for the AI system learning.

Test triangle. It is a comprehensive application quality assessment strategy that consists of unit, service, and UI testing.

Deployment testing. This is the check of the performance of the application in an environment close to production conditions. It helps to determine whether the code is ready for deployment by determining whether any errors or other problems are likely to occur during this process.

White box testing. This is a method of quality assessment where engineers are aware of the principles of the application operation and use them to develop test cases.

Black box testing. It's a kind of “blind test”. In this methodology, engineers only know what the result of the algorithms should be, but not how exactly they should perform the given functions.

Backtesting. It is an assessment of the quality of how the model makes predictions based on the information it receives.

Decision analysis. It is checking if the “decisions” the AI system makes are logical and correct.

Non-functional testing. This is an evaluation of software’s properties, such as stability, performance, efficiency, usability, and others, rather than features.

Check for human bias. This is testing to ensure that the AI model does not have cognitive bias typical for people.

smart assistants (such as Google Home);

robotic arms;

drones;

self-driving vehicles, etc.

Machine learning. Algorithms that can “learn” by “memorizing” and generalizing information.

Deep learning. A kind of AI model that “learns” according to principles that resemble the concept of how the human brain works.

Expert systems. Software that can “make decisions” based on “learned” information.

Natural language processing. These are models that process information based on patterns between the components of speech (letters, words, sentences, etc.).

Computer vision. These are algorithms that allow devices to identify and classify objects from graphical sources.

Image processing. These are models that analyze images by perceiving them as 2D signals and process them according to predefined algorithms.

Robotic process automation. This is the software that can automatically do routine work, such as copying, filing, transferring data, etc.

NLP models need especially careful testing for bias and ethical issues.

ML and deep learning systems should be thoroughly tested for correct learning and generalization of information.

Robotic process automation tools need rigorous checks for the correct execution of programmed functions.

Information-related problems, e.g. low-quality data with “noise” and bias.

Unpredictable application behavior during testing.

Difficulties in defining quality assessment criteria.

Lack of resources, such as necessary tools, qualified testers, etc.

Inadequate test coverage due to the complexity of the system.

Constant arising of new defects due to continuous updating and improvement of software.