SQAT-Lab

SQAT-LAB’s extensive experience in the use of AI, ML, Deep Learning, & analytics helps enterprises improve their automation frameworks & QC practices. SQCT-LAB provides AI/ML, Deep Learning-led testing and performance engineering services for your QC framework through implementation of its next gen IP.

SQAT-LAB’s AI Testing Specialty

Improved Test Coverage: SQAT-LAB’s AI algorithms help identify areas of the software that require more testing, leading to better test coverage and reducing the risk of bugs and defects.
Faster Testing: SQAT-LAB’s AI algorithms automate repetitive and time- consuming tasks, such as regression testing, reducing the time and effort required for testing.
Enhanced Accuracy: SQAT-LAB’s AI algorithms analyze large amounts of data to identify trends and patterns, improving the accuracy of test results.
Dynamic Testing: SQAT-LAB’s AI algorithms adapt to changing conditions and requirements, providing more flexible and dynamic testing solutions.

SQAT-LAB’s AI Testing Services

Model Testing:

Evaluate the accuracy and performance of the AI models used for testing.
Verify that the AI models are able to make correct predictions based on the data provided.
Test the ability of the models to handle exceptions and errors.

Integration testing:

Test the integration of the AI-led testing system with other systems, such as databases, cloud services, and APIs.
Verify that the data exchange between the AI-led testing system and other systems is functioning as intended.

Performance testing:

Evaluate the speed and performance of the AI-led testing system, including the time taken to complete testing tasks and the resource utilization.
Test the scalability of the system, to ensure that it can handle increasing volumes of data and tasks.

User experience testing:

Evaluate the ease of use and user interface of the AI-led testing system.
Assess user adoption and satisfaction with the system.

Security testing:

Ensure that the AI-led testing system is secure and protected against hacking, malware, and other security threats.
Verify that user data is being protected and that access to sensitive information is being controlled.

Data accuracy testing:

Validate the accuracy and consistency of the data used for training and testing the AI models.
Ensure that the AI-led testing system is generating accurate results.

Model robustness testing:

Test the robustness of the AI models, including their ability to handle noisy and missing data.
Verify that the models are able to make accurate predictions even when the data is corrupted or incomplete.

Artificial Intelligence Testing