AI Testing

AI is integrated into almost all apps these days to improve their effectiveness in delivering the services they promise. It enables intelligent automation and learning of many features, reducing user intervention to accomplish some tasks. By nature, it is learning on the go and for testers, the goalpost can keep shifting due to this non-determinate nature of AI technology.  

Since AI algorithms and models also vary from app to app, there is no single solution that can be used for AI app testing. Since AI is integrated with other components, testing must factor in how the performance of those components will constantly change when testing them.  

All these factors make it necessary to have a bespoke testing approach for every project since the constantly varying architecture makes it challenging to have a standard way to build and test AI apps. While some of the traditional testing methods are essential, they must be combined with specialised testing techniques to address the AI element in them.  

5 Key Traditional Testing Methodologies for AI-based Apps 

The following are traditional testing methodologies that have stood the test of time and are critical to identify and address bugs in software. These include: 

  • Functional Testing: This helps test the core functionality of the app, ensuring that the app’s AI algorithms and logic run along expected lines to produce the expected outcomes. 
  • Integration Testing: The app workflow must be smooth with software components integrating with databases and external APIs seamlessly. 
  • Usability Testing: The app must be intuitive and user friendly, making it easy to use without much training and as close to human behavior as possible, with natural language skills, smooth conversational flow, and efficient error handling. 
  • Performance Testing:  The model’s performance, throughput, response times, and other key performance indicators must be assessed to help optimise and improve the AI app. It must perform under different conditions and deliver the desired outcomes every time. 
  • Security Testing: AI models process a lot of data, making it critical to do security testing and prevent any potential data breach. 

Testing for AI Features 

Regular software testing usually has manual intervention even if an automated testing framework is used. In testing AI features, AI-powered testing tools are used to make testing more efficient and effective. AI algorithms are used to generate, execute and analyse test cases. AI models are also tested to ensure they are reliable, accurate, and mitigate biases. 

Some of the challenges of AI systems are that: 

  • They are non-deterministic or behave differently for the same input.  
  • They require vast amounts of training data for testing. Getting sufficient data that reflects real-world scenarios can be difficult, making it difficult to test the system comprehensively and accurately.  
  • They have bias as the available data can be skewed. 
  • They are hard to interpret or assign the specific causes for any errors. 
  • They are constantly learning, training, and adjusting to new data inputs.  

AI being a complex system, any minor defect can get amplified greatly, making it difficult to resolve problems. There is no single silver bullet for AI feature testing, each requiring a unique approach based on its features and desired outcomes. However, frameworks and libraries can be leveraged to test AI models for many AI-based apps. 

Some of the tools include TFX or TensorFlow Extended from TensorFlow: This can be used for – 

  • Data validation and processing 
  • Model analysis and training 
  • Model performance, among others 

It can be leveraged to develop recommendation engines to offer personalised recommendations using vast datasets and training recommendation models.  

Other frameworks and libraries include:

  • Scikit-learn 
  • PyTorch’s torch.testing module 
  • FairML for bias and fairness testing  
  • TensorFlow Model Analysis for evaluating models 

Best Practices in AI Features Testing 

The AI field is constantly evolving, as the AI models in specific apps. The use cases are expanding, and tool development is becoming quite complex. The AI testing landscape also needs to constantly evolve, which adds to the difficulty in testing AI models. This requires AI app testers to incorporate best practices to improve the efficiency and effectiveness of AI testing.  

At Merit, we implement the following best practices when testing AI models: 

Best Practice #1. Define Scope: We begin by defining the scope, objectives, aspects that must be tested and establishing metrics to measure success of the AI testing project. 

Best Practice #2 High-Quality Training Data: AI models need vast amounts of data. Merit takes special care in ensuring that the training data is diverse and reflects different real-life scenarios to ensure that the learning is unbiased and accurate. 

Best Practice #3 Establish Benchmarks: In addition to metrics for measuring AI testing success, it is also important to benchmark the AI features’ performance with global standards. 

Best Practice #4 Improve Efficiency of Testing: Choosing the right tool and framework that is apt for the app being tested plays a key role in ensuring the efficiency of testing. Merit leverages data-driven testing tools and AI-powered test automation frameworks to ensure the effectiveness of AI-based systems testing.  

AI Testing with Merit 

Merit has a long track record of providing QA and Test Automation services. The company also has a talented team of AI developers and testers, enabling integrating robust software and AI testing tools and frameworks to ensure efficiency and effectiveness. Our expertise in data further puts us in a unique position to generate clean and quality data for testing AI models to improve the accuracy of the findings.

Key Takeaways 

Customised Testing Approach: AI-based apps require bespoke testing methods due to their non-deterministic nature and constantly evolving architecture. 

Integration of Traditional Methods: While traditional testing methodologies like functional, integration, usability, performance, and security testing are crucial, they must be adapted to suit AI features. 

AI Feature Testing Challenges: Testing AI features presents challenges such as non-determinism, bias, interpretability, and the need for vast training data. 

Specialised Testing Tools: AI-powered testing tools, frameworks, and libraries like TensorFlow Extended, FairML, and TensorFlow Model Analysis help in executing and analysing test cases efficiently. 

Best Practices for AI Testing: Defining scope, ensuring high-quality training data, establishing benchmarks, and leveraging efficient testing tools are essential for effective AI testing. 

Continuous Adaptation and Improvement: The AI testing landscape is constantly evolving, necessitating continuous adaptation of best practices and incorporation of emerging tools and methodologies for optimal testing outcomes.

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