National foreword

This Published Document is the UK implementation of ISO/IEC TR 29119‑11:2020.

The UK participation in its preparation was entrusted to Technical Committee IST/15, Software and systems engineering.

A list of organizations represented on this committee can be obtained on request to its committee manager.

This publication does not purport to include all the necessary provisions of a contract. Users are responsible for its correct application.

Compliance with a British Standard cannot confer immunity from legal obligations.

This Published Document was published under the authority of the Standards Policy and Strategy Committee on 31 December 2020.

Foreword

ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) form the specialized system for worldwide standardization. National bodies that are members of ISO or IEC participate in the development of International Standards through technical committees established by the respective organization to deal with particular fields of technical activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.

The procedures used to develop this document and those intended for its further maintenance are described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives).

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent rights. Details of any patent rights identified during the development of the document will be in the Introduction and/or on the ISO list of patent declarations received (see www.iso.org/patents) or the IEC list of patent declarations received (see patents.iec.ch).

Any trade name used in this document is information given for the convenience of users and does not constitute an endorsement.

For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions related to conformity assessment, as well as information about ISO's adherence to the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www.iso.org/iso/foreword.html.

This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology, Subcommittee SC 7, Software and systems engineering.

A list of all parts in the ISO/IEC/IEEE 29119 series can be found on the ISO website.

Any feedback or questions on this document should be directed to the user’s national standards body. A complete listing of these bodies can be found at www.iso.org/members.html.

Introduction

The testing of traditional systems is well-understood, but AI-based systems, which are becoming more prevalent and critical to our daily lives, introduce new challenges. This document has been created to introduce AI-based systems and provide guidelines on how they might be tested.

Annex A provides an introduction to machine learning.

This document is primarily provided for those testers who are new to AI-based systems, but it can also be useful for more experienced testers and other stakeholders working on the development and testing of AI-based systems.

As a Technical Report, this document contains data of a different kind from that normally published as an International Standard or Technical Specification, such as data on the “state of the art”.

1 Scope

This document provides an introduction to AI-based systems. These systems are typically complex (e.g. deep neural nets), are sometimes based on big data, can be poorly specified and can be non-deterministic, which creates new challenges and opportunities for testing them.

This document explains those characteristics which are specific to AI-based systems and explains the corresponding difficulties of specifying the acceptance criteria for such systems.

This document presents the challenges of testing AI-based systems, the main challenge being the test oracle problem, whereby testers find it difficult to determine expected results for testing and therefore whether tests have passed or failed. It covers testing of these systems across the life cycle and gives guidelines on how AI-based systems in general can be tested using black-box approaches and introduces white-box testing specifically for neural networks. It describes options for the test environments and test scenarios used for testing AI-based systems.

In this document an AI-based system is a system that includes at least one AI component.

2 Normative references

3 Terms, definitions and abbreviated terms

3.1 Terms and definitions

3.2 Abbreviated terms

4 Introduction to AI and testing

4.1 Overview of AI and testing

This clause introduces artificial intelligence (AI) and then explains testing in the context of AI-based systems.

Artificial intelligence is initially defined, typical AI uses cases are provided, and figures for the expanding market for AI-based systems are presented. The range of technologies used to implement AI-based systems are listed and options for the hardware and development frameworks used to implement these systems are provided. The implementation levels of narrow AI and general AI are then compared.

The importance of testing for AI-based systems is then introduced, and the use of such systems in safety-related domains is considered before the use of standards for AI-based systems is introduced.

4.2 Artificial intelligence (AI)

4.3 Testing of AI-based systems

5 AI system characteristics

5.1 AI-specific characteristics

5.2 Aligning AI-based systems with human values

5.3 Side-effects

5.4 Reward hacking

5.5 Specifying ethical requirements for AI-based systems

6 Introduction to the testing of AI-based systems

6.1 Challenges in testing AI-based systems

6.2 Testing AI-based systems across the life cycle

7 Testing and QA of ML systems

7.1 Introduction to the testing and QA of ML systems

7.2 Review of ML workflow

7.3 Acceptance criteria

7.4 Framework, algorithm/model and hyperparameter selection

7.5 Training data quality

7.6 Test data quality

7.7 Model updates

7.8 Adversarial examples and testing

7.9 Benchmarks for machine learning

8 Black-box testing of AI-based systems

8.1 Combinatorial testing

8.2 Back-to-back testing

8.3 A/B testing

8.4 Metamorphic testing

8.5 Exploratory testing

9 White-box testing of neural networks

9.1 Structure of a neural network

9.2 Test coverage measures for neural networks

9.3 Test effectiveness of the white-box measures

9.4 White-box testing tools for neural networks

10 Test environments for AI-based systems

10.1 Test environments for AI-based systems

10.2 Test scenario derivation

10.3 Regulatory test scenarios and test environments

Bibliography

PD ISO/IEC TR 29119-11:2020

Software and systems engineering. Software testing - Guidelines on the testing of AI-based systems

Current, Under Review

Published:

31 Dec 2020