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Test Design for AI Systems

Test Design for AI Systems

Murad Mamedov
AI Researcher, Exactpro

“Machine Learning (ML) has achieved remarkable progress over the past decade and has been widely applied to many industry domains, including safety-critical ones. With the expansion of ML, the risks related to correctness and robustness are also evolving. Businesses and governments are mitigating the risks with regulatory activities. Since software testing is an important aspect of monitoring and control processes, its applications in ML-based systems (MLS) are also evolving.”

AI Testing Talks – Test Design for AI Systems. 13 May 2022

https://exactpro.com/events/external/ai-testing-talks-test-design-ai-systems?utm_source=speakerdeck&utm_medium=Refferer&utm_campaign=test-design

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May 13, 2022
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  1. 1 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks BUILD

    SOFTWARE TO TEST SOFTWARE exactpro.com Test Design for AI Systems Murad Mamedov AI Researcher, Exactpro 13 MAY | 3 PM SLST
  2. 2 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks Table

    of Contents - neural network architecture - ML development process overview - current QA activities in industry - why test-design is important - black-box testing - white-box testing - data-box testing - conclusion
  3. 5 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks Current

    Activities in Industry The emerging risk makes governments and businesses respond with quality assurance activities. The regulatory activities are also leveraging monitoring and control of AI development. - USA Data and Trust Alliance - EU AI Regulation Draft - ISO/IEC TR 29119-11:2020 Guidelines on the testing of AI-based systems
  4. 8 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks Black

    Box Strategy: Mutational Approach It can be applied to: - to an algorithm itself - train data - test data Original Program Mutant Program Output Compare the results of both programs
  5. 9 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks Black

    Box Strategy: Combinatorial Approach The picture represents a decision map application to the Boldness and Discontinuity features, in order to generate use cases from high-level scenarios
  6. 10 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks Black

    Box Strategy: Business Logic Approach The approaches based on the business logic are closer to validation-level ones, since they are going directly to the question of whether a system meets the stakeholders’ expectations or not. An example of merging approaches: Model-based exploration of the frontier of behaviours for deep learning system testing Input Database
  7. 11 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks Black

    Box Strategy: Business Logic Approach The approaches based on the business logic are closer to validation-level ones, since they are going directly to the question of whether a system meets the stakeholders’ expectations or not. An example of merging approaches: Model-based exploration of the frontier of behaviours for deep learning system testing Input Database Literature Features Libelled Inputs Open Coding Metric Identification Candidate Metrics Design Metrics Validation and Correlation Metrics Initial Labelling Consensus Meeting Final Labelling Feature name Score [5pt] Candidate Metrics
  8. 12 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks White

    Box Strategy: Activation Testing What can be tested: - if a neuron is activated - which value it’s activated with - how the neurons interact - how the layers interact
  9. 14 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks White

    Box Strategy: What’s Missing? Activation testing focuses mostly on neurons/layers behaviour, and pays less attention to the predictions
  10. 15 BUILD SOFTWARE TO TEST SOFTWARE AI Testing Talks Data

    Box Strategy Combinatorial EDA helps to represent the data from a use cases perspective and enhances the further ML testing activities such as oracle education, test generation, etc.