Artificial Intelligence (AI) is one of the most popular technology in driving digital transformation. Testing AI Systems are very critical to achieve successful application, testing an AI application requires an agile model process and the ability to compare scenarios and identify the changes based on business needs.
Quality assurance of AI applications has increased tremendously. Artificial intelligence applications should meet the 3 main aspects such as performance, safety, and security. These 3 aspects are internally linked and very important, if you find any error in one category it can trigger a failure in another aspect.
Challenges of Testing Artificial intelligence applications
Non-Deterministic: AI systems are non-deterministic; they give different outcomes for the same input data in a different environment.
Large and accurate data: Artificial intelligence models depend on a large amount of accurate data. This takes 80% of data scientist’s time.
Bias: The distribution of training dataset may introduce unwanted bias in the AI systems.
Explainability: exacting and explaining a specific attribute to the AI system is very difficult.
Sustained testing: AI systems will learn, train themselves constantly with new data input. Sustainability testing can ensure AI systems produce results based on human intelligence.
How do you test AI applications?
Quality Assurance (QA) is a key part of any product or technology and business delivery that it is one of the most important components of any software development cycle.
Key aspects of testing AI applications
- Data validation: For any successful AI application data, validation is a must. Input data should be free of errors. In AI systems input data needs to be cleaned and validated to accomplish expected outcomes. If input data is not validated, it may lead to complications in an application. For instance, if you are developing driverless automobiles like Cars, Trucks, invalid navigation may cause the wrong destination and even lead to accidents.
- Core algorithm: Algorithms are very important in Artificial intelligence applications Since Algorithms are the heart of the AI technology which processes the data and generate results. There are few key factors at this stage such as learnability, Model validation, and calculation of the algorithm efficiency.
- Security and performance testing: Security and performance testing will perform to check how an AI system performs in heavy load conditions and it measures quality aspects of the application such as reliability, resource usage.
- Integration Testing: AI systems are built to operate larger context of other applications to provide specific solutions for a particular task. When multiple AI systems involve then it requires system integration testing to eliminate conflicts in the production.
Testing AI applications are very different than traditional software testing. You cannot just deploy the AI or Machine learning models to the production, because AI systems need to test very proper way which should satisfy quality characteristics such as performance, robustness, reliability, security, and usability, besides demonstrating ethical behavior.
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