testing profession by: Defining and maintaining a Body of Knowledge which allows testers to be certified based on best practices, connecting the international software testing community, and encouraging research. ISTQB – Vision
testing as a profession to individuals and organizations. 2. We help software testers to be more efficient and effective in their work, through the certification of competencies. 3. We enable testers to progress their career through a Professionals' Code of Ethics and a multi-level certification pathway that provides them with the skills and knowledge they need to fulfil their growing responsibilities and to achieve increased professionalism. 4.We continually advance the Testing Body of Knowledge by drawing on the best available industry practices and the most innovative research, and we make this knowledge freely available to all. 5. We set the criteria for accrediting training providers, to ensure consistent delivery of the Body of Knowledge, world-wide. 6. We regulate the content and coverage of exam questions, the examination process, and the issuing of certifications by official examination bodies. 7. We are committed to expanding software testing certifications around the world, by admitting member boards into the ISTQB®. These boards adhere to the constitution, bylaws, and processes defined by the ISTQB®, and participate in regular audits. 8.We nurture an open international community, committed to sharing knowledge, ideas, and innovations in software testing. 9. We foster relationships with academia, government, media, professional associations and other interested parties. 10.We provide a reference point against which the effectiveness of testing services can be evaluated, by maintaining our prominence as a respected source of knowledge in software testing. ISTQB – Mission
*https://info.eggplantsoftware.com/gartner-report-2021-market-guide?utm_campaign=FY22%20Modern%20Application%20Campaign&utm_source=homepage-alert- banner&utm_content=gartner-market-guide And where is the trend going in testing? Gartner Report* in December 2021
an independent field of research ▪ 1940 to the end of 1950s – The early days of AI ▪ 1960 to early 1980 – research in symbolic and subsymbolic AI ▪ 1980 to early 2000 – boom in knowledge-based systems in industry, followed by disenchantment and decline ▪ Since 2010 – success of machine learning (ML) led to huge interest in industry and vast amount of applications History of Artificial Intelligence (AI) Automatic deduction techniques based on mathematical logic ▪ ▪ ▪ Artificial neural networks ▪ Automatic Deduction systems ▪ Prolog ▪ Knowledge-based systems ▪ Mathematical models based on probababilitic reasoning
▪ artificial neural networks Leap in development and industrial application due to ▪ hugh increase of processing power ▪ availability of large amounts of data ML is the name of the game!
▪ 4 days training with hands-on exercises *https://istqb-main-web-prod.s3.amazonaws.com/media/documents/ISTQB_CT-AI_Syllabus_v1.0_mghocmT.pdf … even more hard work …
AI Effect, Narrow AI, General AI, Super AI, .. ▪ Brief overview of AI technologies, e.g. Fuzzy logic, Search algorithms, reasoning techniques and Machine Learning techniques, .. ▪ Examples for AI Development Frameworks, e.g. KERAS, PyTorch, Scikit-learn, .. ▪ Hardware for AI-Based Systems, e.g. NVIDIA, Intel, Google TPUs, .. Part I – Introduction to AI
▪ Accuracy, Precision, Recall, F1-score ▪ Metrics for Classification, Regression and Clustering ▪ Limitations of ML Functional Performance Metrics ▪ Selection of ML Functional Performance Metrics Part I – Introduction to AI
of AI-Based System, e.g. ▪ Input Data Testing, ML Model Testing, Component Testing, Component Integration Testing, … ▪ Test Data for Testing AI-Based Systems ▪ Testing for Automation Bias in AI-Based Systems ▪ Testing for Concept Drift ▪ Selecting a Test Approach for an ML System Part II – Testing of AI-Based Systems
Self-Learning Systems ▪ Testing autonomous AI-Based Systems ▪ Testing for Algorithmics, Sample and Inappropriate Bias ▪ Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems ▪ Test Oracles for AI-Based Systems Part II – Testing of AI-Based Systems
AI-Based Systems ▪ Adversarial Attacks ▪ Data Poisoning ▪ Pairwise Testing ▪ Back-to-Back Testing ▪ Metamorphic Testing ▪ Experience-Based Testing of AI-Based Systems Part II – Testing of AI-Based Systems
for Testing, e.g. classification, prediction, search & optimization techniques ▪ Using AI to Analyse Reported Defects ▪ Using AI for Test Case Generation ▪ Using AI for the Optimization of Regression Test Suites ▪ Using AI for Defect Prediction ▪ Using AI for Testing Interfaces Part III – Testing with AI
an excellent introduction into AI ▪ explains how machine learning works ▪ shows the challenges when testing AI-Based Systems ▪ introduces new test techniques ▪ gives examples on how AI can support testing ▪ provides hands-on experiences with AI-Based Systems Summary
Testing of AI-Based systems“ ▪ ISO/IEC DIS 25059 „Quality model for AI systems“ ▪ New Subcommittee on AI started in 2017: ISO/IEC JTC1/SC42 ▪ New standards are to be expected Related Standards
exactpro
trishagee
iamctodd
honnibal
garrettdimon
inesmontani
davidcarrasco
skipperchong
rakko
trallard
soudai
gurzu
moore
![ISTQB® 2023 29 Questions?! [email protected]](https://files.speakerdeck.com/presentations/8a1943f1a7414618a7ff8cb9b8df54c3/slide_28.jpg){kind=link}