AI-based systems are non-determinism and provide probabilistic outputs as compared to fixed outputs in traditional software systems. Check how QASource uses the right AI QA strategy to check variance in results.
Rapid advancements in new-age cognitive technologies and artificial intelligence have dramatically increased the rate of adoption of AI-powered applications and systems across industries. This has also led to demand for quality engineering for artificial intelligence and machine learning-powered applications.
Some fundamental characteristics of AI-based systems like non-determinism, probabilistic outputs, and lack of transparency makes it difficult to assess and ensure quality of AI systems.
Due to these basic facts, as compared to the fixed outputs in traditional software systems, quality assurance for AI applications can’t be effectively achieved through traditional QA methods.
Unlike traditional software systems, which are rule-based and are a collection of logical units implementing the “if X, then Y” model, AI systems are probabilistic i.e. they are non-deterministic. AI systems can exhibit different behaviors for the same input.
Therefore, the testing of AI systems is fundamentally different from traditional QA which is focused on output verification while AI QA needs to be focused on accuracy-based testing methodologies.
Deterministic:
Non-deterministic:
In order to create the right testing strategy for AI systems, QA teams must understand the workflow of an AI system.
At QASource, we have extensive experience in delivering effective and efficient AI QA for machine/deep learning applications, NLP, computer vision, speech recognition, and robotics. Our dedicated team of QA experts is well versed in AI workflows, model evaluation, and testing techniques and has experience in delivering AI QA for different domains like computer vision, classification, speech analytics, and object recognition.
To know more about how using AI for QA testing can enhance your software reliability and quality, contact QASource now.
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