ISO/IEC TR 29119-11:2020

TECHNICAL ISO/IEC TR
REPORT 29119-11
First edition
2020-11
Software and systems engineering —
Software testing —
Part 11:
Guidelines on the testing of AI-based
systems
Reference number
©
ISO/IEC 2020
© ISO/IEC 2020
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Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions . 1
3.2 Abbreviated terms .10
4 Introduction to AI and testing .11
4.1 Overview of AI and testing .11
4.2 Artificial intelligence (AI) .11
4.2.1 Definition of ‘artificial intelligence’ .11
4.2.2 AI use cases .12
4.2.3 AI usage and market .12
4.2.4 AI technologies .13
4.2.5 AI hardware .15
4.2.6 AI development frameworks .16
4.2.7 Narrow vs general AI . .16
4.3 Testing of AI-based systems .16
4.3.1 The importance of testing for AI-based systems . .16
4.3.2 Safety-related AI-based systems .17
4.3.3 Standardization and AI .17
5 AI system characteristics .19
5.1 AI-specific characteristics .19
5.1.1 General.19
5.1.2 Flexibility and adaptability .20
5.1.3 Autonomy .20
5.1.4 Evolution .21
5.1.5 Bias .21
5.1.6 Complexity .21
5.1.7 Transparency, interpretability and explainability .22
5.1.8 Non-determinism .22
5.2 Aligning AI-based systems with human values .23
5.3 Side-effects .23
5.4 Reward hacking .24
5.5 Specifying ethical requirements for AI-based systems .24
6 Introduction to the testing of AI-based systems .25
6.1 Challenges in testing AI-based systems .25
6.1.1 Introduction to challenges testing AI-based systems .25
6.1.2 System specifications .25
6.1.3 Test input data .25
6.1.4 Self-learning systems .26
6.1.5 Flexibility and adaptability .26
6.1.6 Autonomy .26
6.1.7 Evolution .26
6.1.8 Bias .26
6.1.9 Transparency, interpretability and explainability .27
6.1.10 Complexity .27
6.1.11 Probabilistic and non-deterministic systems .27
6.1.12 The test oracle problem for AI-based systems .27
6.2 Testing AI-based systems across the life cycle .27
6.2.1 General.27
6.2.2 Unit/component testing .28
© ISO/IEC 2020 – All rights reserved iii

6.2.3 Integration testing .28
6.2.4 System testing .28
6.2.5 System integration testing .29
6.2.6 Acceptance testing . .29
6.2.7 Maintenance testing .29
7 Testing and QA of ML systems .29
7.1 Introduction to the testing and QA of ML systems .29
7.2 Review of ML workflow .29
7.3 Acceptance criteria .29
7.4 Framework, algorithm/model and hyperparameter selection .30
7.5 Training data quality .30
7.6 Test data quality .30
7.7 Model updates .30
7.8 Adversarial examples and testing .30
7.9 Benchmarks for machine learning .31
8 Black-box testing of AI-based systems .31
8.1 Combinatorial testing .31
8.2 Back-to-back testing .32
8.3 A/B testing .32
8.4 Metamorphic testing .33
8.5 Exploratory testing .34
9 White-box testing of neural networks.34
9.1 Structure of a neural network .34
9.2 Test coverage measures for neural networks .
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