Artificial Intelligence Testing Software the future of Johan Steyn
Who works for a bank?
Who used AI today?
Testing
Testing Functionality
Testing Functionality Quality Engineering
Testing Customer Experience Functionality Quality Engineering
Who is your customer? Have you met him/her?
The ATM story
1840
Industrial Revolution 5.0
NATURAL LANGUAGE PROCESSINGARTIFICIAL EMPATHYINTONATION & PACINGTEXT-TO-SPEECH / SPEECH-TO-TEXT CONTENT MODERATIONVOICE DATA UPLOADEDMACHINE VISIONHUMAN/MACHINE MODERATION LEGO LIFE APP MACHINE LEARNINGANONYMISED, NON-PERSONAL DATAANTI-TRAFFICINGPORNOGRAPHY AR Kit 2 (APPLE)AUGMENTED REALITYCONVERSATIONAL AICHATBOTS LEGO MINDSTORMS &LEGO BOOSTPROGRAMABLE ROBOTSVISION SENSORSINFRARED DISTANCE DETECTION
Picture credit: Franck V. on Unsplash What is your view ofArtificial Intelligence
Hype Hysteria
2030
2018-2020 dominated by •global high-speed mobile internet•artificial intelligence•widespread adoption of big data analytics•cloud technology
Accelerated technology adoption: 2020•85% of companies adopt big data analytics•IoT, Cloud computing•Machine learning•Augmented and virtual reality
Trends in robotization: 2018–2022 •Stationary robots•Non-humanoid land robots•Fully automated aerial drones
Changing employment types: 2022•50% of companies expect that automation will lead to reduction in their full-time workforce•38% of businesses expect to extend their workforce to new productivity-enhancing roles•More than a quarter expect automation to lead to the creation of new roles in their enterprise
New human-machine frontier within existing tasks•2018: 71% humans, 29% machines•2022: 58% humans, 42% machines
Emerging in-demand roles: 2022•AI and Machine Learning Specialists•Big Data Specialists•Process Automation Experts•Human-Machine Interaction Designers•Robotics Engineers
A reskilling imperative: 2022•54% of all employees will require significant re- and upskilling.•35% are expected to require additional training of up to six months•9% will require reskilling lasting six to 12 months•while 10% will require additional skills training of more than a year
How will all this impact Software Quality & Testing? How will all this impact YOU?
Source: https://www.tricentis.com/artificial-intelligence-software-testing/
Key recommendation: Invest in intelligent self-learning QA and Testing platforms for all areas of the application landscape."Intelligence-driven QA" - invest and experiment with tools that willanalyse the root cause of defects, analyse coverage and efficiency of test sets; analyse utilisation of resources and environments; predict test estimation based on requirements; predict risk areas and risk levels in projects; plan the priority of test cases.Robotics will bring down the headcount in the QA and testing function as these machines take on the more repetitive analysis and execution tasks and routine jobs currently undertaken by humans.
The convergence of physical with the cyber has added another layer of complexity to the testing activity. Product strategy is shifting from building discrete products to building connected eco-systems.In the digital age, we need to test for experience rather than functions or features. As we enter the realm of early AI testing it is critical to building knowledge based on artefacts we already collect like defect log data, life cycle information, fields defects, and production events to improve effectiveness.The book concludes with an interesting perspective on the digital quality engineering skills that an AI quality engineer needs. This can be used as a ready reckoner when setting up cross-functional testing teams armed with the right digital test engineering skills.
Robotic Process Automation An emerging form of business process automation technology based on the notion of software robots. Natural Language Processing When an AI is trained to interpret human communication. This is useful for chat bots and translation services, but it’s also represented by AI assistants like Alexa and Siri. Machine Learning Statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. A way of achieving AI. Artificial General Intelligence The intelligence of a machine that could successfully perform any intellectual task that a human being can. Artificial Superintelligence A hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. An intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest. Artificial Narrow Intelligence AI that specializes in one narrow task like coming up with driving routes or playing chess. Terminology
Andrew Williams - IBM 4 challenges to finding the right data for AI ingestion•Requirements (Opportunity)•Defects (Strong opportunity) •Test Cases (Opportunity)•Test Results (Difficult)TOP three ways you can predict and protect your future in test•Defect classification (Reduce defect turnaround)•Defect prediction (Improve resource pipeline)•Quality transformation (Improve quality)
AI: Test Practitioners to follow Andrew Williams Global Test Executive, IBM IGNITE Quality & Test Rik Marselis Management Consultant Digital Assurance & Testing at Sogeti Tom van de Ven Senior Test Consultant High Tech at Sogeti Jason Arbon CEO @ test.ai Jeremias Rößler, Ph.D Founder at ReTest Geoff Meyer Test Architect at Dell EMC
AI: Test Practitioners to follow Tariq M. King, Ph.D Director, Quality Engineering at Ultimate Software Dionny Santiago Test Architect at Ultimate Software Peter J. Clarke, Ph.D Associate Professor at Florida International University Alexander Andelkovic Senior Agile Testing Lead at King Angie Jones Snr Software Engineer in Test at Twitter Paul Mowat Accenture Global Quality Engineering SME
“In this book, I try to understand the challenge presented by the prospect of superintelligence, and how we might best respond. This is quite possibly the most important and most daunting challenge humanity has ever faced. And - whether we succeed or fail - it is probably the last challenge we will ever face.” Nick Bostrom, Superintelligence
The Tester of Tomorrow
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Artificial Intelligence: The Future of Testing Software