According to the World Quality Report, there is a lack of skills that you need to be an AI tester. “There is still some way to go in this regard,” the report said; around one-third of respondents admit to a skills gap. To become an AI tester, you will need to have a strong computer […]
According to the World Quality Report, there is a lack of skills that you need to be an AI tester. “There is still some way to go in this regard,” the report said; around one-third of respondents admit to a skills gap. To become an AI tester, you will need to have a strong computer and data science background. You will also need to be familiar with software testing methods. Also, test engineers have to understand some deep learning principles.
Here are seven major skills to be a skilled AI tester:
- Expert in AI Automation Tools.
- Knowledge of Cloud Computing: According to reports, the global cloud computing market will grow from $272.0 billion to $623.3 billion by 2023, as an expectation.
- Cloud computing provides some advantages in AI-based testing including speeding up testing, reducing costs and effort, and disaster recovery.
- Visual Testing is one of the most important topics in the domain of emerging technologies.
- Have a certification that helps you expand your skills in testing AI-based systems.
AI Testing Certificates
AI certificates guarantee highly concentrated, specialized knowledge in a specific field—something which a traditional bachelor’s degree doesn’t offer.
ISTQB Certified Tester AI Testing (ISTQB CT-AI):
This test introduces the key concepts of Artificial Intelligence (AI), how we decide acceptance criteria and how we test AI-based systems. With the ISTQB®Certified Tester AI Testing you will:
- Understand the current state and expected trends of AI.
- Understand the challenges associated with testing AI-based systems, such as their self-learning capabilities, bias, ethics, complexity, non-determinism, transparency, and explainability.
- Contribute to the test strategy for an AI-based system.
- Design and execute test cases for AI-based systems.
- Recognize the special requirements for the test infrastructure to support the testing of AI-based systems.
- Understand how to use AI to support software testing.
How does our skilled team work with AI testing?
For software to function efficiently and reach the market in a short time, there is a need for automation and artificial intelligence in software testing. This is where TestCrew comes in. At Testcrew, we provide AI-based software testing solutions that integrate quality into the software development process to prevent, detect, and remediate defects early in the SDLC. Below are some scenarios to test an AI application:
- For AI systems, data is extremely essential for any output or action. AI testers should have a huge collection of test data (text, speech, images, or videos) before they start testing. To be a successful system, the input data should be free of any errors. The input data needs to be scrubbed, cleaned, and validated. This is because any fault in the input data can lead to complications in the system’s interpretation of data leading to errors in the output.
- The next step will be a machine learning system or core algorithms. The system is trained to perform some actions using algorithms, these algorithms is responsible for processing data and generating insights. Testing AI algorithms involves using model validation, the effectiveness of the algorithm, successful learnability, and a core understanding of the mind. There are familiar examples of AI algorithms such as the ability to learn, voice recognition, real-world sensor detection, etc.
- The final stage checks the interaction of an AI with the real-time environment to provide the output, so it is important to see how it fits into such a system. Performance and security testing are needed. They ensure error-free performance that has incorporated security measures to protect the system from cyber-attacks. Testers also need to perform integration testing to ensure that all systems of AI interact as expected. Therefore, a holistic approach to testing is required. System integration testing tests the entire system on various parameters when it works with conflicting goals.