Testing AI: What Are You Missing During the POC Phase?

TESTING AI IS A NON-TRIVIAL TASK THAT REQUIRES UNIQUE TESTING TOOLS AND METHODS. SEE WHAT CHALLENGES YOU MAY FACE AND WHAT YOU CAN DO TO SOLVE THEM.

New technologies bring new challenges. Although artificial intelligence (AI) has been around for a long time, it is still a new phenomenon in software development and testing. A key AI feature is its ability to learn and evolve. Therefore, the test results you get today may not be relevant tomorrow. Such changing conditions require a specific approach to testing AI software which may differ from testing other products.

So, how to organize QA for AI and be always up to date about the system state? This post answers the above question and gives useful tips for QA teams that are tasked with testing AI.

WHAT SHOULD YOU KNOW ABOUT TESTING AI IN 2021?

Is your testing object purely AI-based software, or does it use some data produced by smart algorithms? If the latter is the case, you don’t have to come up with something special. Just use the testing methods you were using before for any other piece of software. The main task here is to make sure the program works as it should when using the AI’s output.

If the application under test can gain new knowledge and make decisions, you are dealing with a machine learning system. In this case, the expected result will not be static, so you need a different testing scheme.

First of all, you have to get a complete list of datasets used to educate the program:

  • A training dataset contains multiple examples used by an AI system to learn and train.
  • A validation dataset is used to configure the architecture of the classifier.
  • A test dataset helps estimate program productivity.

Understanding how the above datasets interact for training an AI model will help you create an efficient technique for testing AI software.

FIVE CHALLENGES TO EXPECT WHILE TESTING AI

1. VARIABLE OUTPUT

Let’s assume a car “sees” a stone on the road. What should it do? Stop, go around, or keep driving? The correct option depends on many factors: traffic intensity, stone size, vehicle speed, etc. AI software testers can evaluate the decision within acceptable boundaries. To set the exact boundaries, you need to limit the input data to only the essential information.

2. THE POSSIBILITY OF BIAS

3. CHANGING ENVIRONMENT

4. ON-GOING TRAINING

5. DATA SECURITY

TESTING POC AND MVP

PoC aims to confirm the project is technically viable and economically beneficial for the company. It involves the collaboration of developers, testers, and business analysts. In a good scenario, its accuracy reaches 80–85%.

For a QA engineer, testing the PoC stage means checking the most complex part or feature from a technical point of view. If you prove it works, there is a big chance of implementing the project as a whole.

The next step is launching an MVP. It is a prototype with a minimum set of functions that can cover customers’ basic needs. If it is in demand and makes a profit, consider it a strong reason to develop a full-fledged product.

To effectively conduct PoC, MVP, and subsequent AI testing phases, QA engineers need a number of special skills. Here are the five essentials for AI automation testers:

1. Expertise in AI frameworks and tools. The most demanded ones are Testim and Appvance. You can use them to run AI-generated tests for your ever-changing product.

2. Machine Learning basics. It is vital to understand the algorithms by which the program is trained to anticipate its future decisions.

3. Cloud computing. Apps are more and more migrating to the cloud, and AI is no exception. Testers should be equally comfortable in both server and serverless environments to ensure software stability.

4. Data science fundamentals. AI uses large amounts of data to learn and evolve. Data science helps testers understand methods of data analyzing and extracting valuable information.

5. Visual testing. It allows comparing the visible output with a baseline image. Visual tests can be run on individual components or a whole app by integrating with automated testing frameworks like Selenium, Cypress, and WebDriverIO.

CONCLUSION

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