Testing in the digital age

Foreword

1 Introduction
1.1 Reading guide
1.2 TMap Suite
1.3 AI makes the difference

2 Characteristics of the digital age
2.1 The digital age is the new industrial revolution
2.2 Digital explained
2.3 Digital help
2.4 Artificial intelligence era
2.5 AI terminology
2.6 The digital twin
2.7 Smart testing in the digital age

3 Set up your digital testing roadmap
3.1 A digital testing journey
3.2 Ultimately put “people first”
3.3 Digital test engineering: moving from functional to expertise mindset
3.4 Quality characteristics for conventional software testing
3.5 Why we require additional quality characteristics
3.6 New quality characteristics for intelligent machines
3.7 Existing quality characteristics applied to intelligent machines
3.8 Quality characteristics mapped to quality angles
3.9 Test intensity table
3.10 Test varieties
3.11 Five hops to digital testing

Hop 1 Automation and robots
4 Testing AI solutions
4.1 Six angles of quality for machine intelligence
4.2 What needs to be tested?
4.3 Test objects in AI Solutions
4.4 Structured learning
4.5 Examination
4.6 A/B testing
4.7 Metamorphic testing

5 Cobotics
5.1 Human robot-interface
5.2 Different reasons for robot solutions
5.3 Robotic process automation (RPA)
5.4 Robotic arms
5.5 Robots testing physical equipment
5.6 Cobotics in airplane testing

6 Robotesting
6.1 Testing of physical robots
6.2 Six angles of quality in robotesting
6.3 Testing of smart algorithms
6.4 Testing chatbots

Hop 2 Use the data
7 AI test data
7.1 Fitting data
7.2 Testing the input values
7.3 Feeding specific input data to see how the AI learns
7.4 Predefined test data sets
7.5 Monitoring the input of the AI
7.6 Underfitting data
7.7 Overfitting data

Hop 3 Go model-based
8 Virtual engineering
8.1 Test execution in a virtual engineering environment
8.2 Production and automation engineering
8.3 User centered product design
8.4 Mechanics of materials
8.5 Physics and mathematics
8.6 Checklist for virtual engineering testing

9 Quality in the product lifecycle
9.1 Digital archeology and testing
9.2 Product Lifecycle Management

10 Blockchain and testing
10.1 Blockchain explained
10.2 Blockchain QA and testing
10.3 Blockchain specific testing
10.4 Blockchain test strategy roadmap

Hop 4 Use Artificial Intelligence
11 Testing with AI
11.1 Testing with artificial intelligence using cognitive QA
11.2 The cognitive QA model
11.3 11.3 Self-learning and self-exploring testing tools take over thinking
11.4 Speed up with AI
11.5 Security issues regarding AI
11.6 Privacy, Big Data and AI

12 IoT testing
12.1 Three rules for IoT testing
12.2 Evolutionary algorithms help IoT testing
12.3 Example: Evolutionary algorithm to speed up selection of test environments

Hop 5 Test forecasting
13 Models help test forecasting
13.1 Systems of Systems
13.2 Models of Models
13.3 Multi-model predictions
13.4 Singularity meteorologists
13.5 Forecasting by an artificially intelligent elevator

14 Business and social impact
14.1 Business impact
14.2 Continuous everything
14.3 Change management
14.4 The impact of intelligent technology
14.5 Business impact example: additive manufacturing test strategy
14.6 Social impact
14.7 Testing in a cross-functional team
14.8 Digital test engineering skills

15 Interviews with leading companies on their digital vision
15.1 Interview with Werner Soeteman, Sr. IT Manager – Service Center Test at Air France - KLM Royal Dutch Airlines
15.2 Interview with Reindrich Geerman, Digital Architect – KPN Technium
15.3 Interview with Caroline Arkestein, QA and Support Manager – Wolters Kluwer
15.4 Interview with Peter Claassen, Delivery Manager at Rabobank
15.5 Interview with Willem-Jan van Tongeren, Director IT business support at PostNL
15.6 Interview with Paul Hesen, VP Custom Systems at TomTom
15.7 Interview with Martijn Tideman and Gwen van Vugt, Product Director and Director Mobility Center, at TASS International (a Siemens business)
15.8 Interview with Ton van Hamersveld, Director Engineering – Thales

Acknowledgements