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Expand your knowledge of AI-based sytems and how to test them
Explore the latest trends in AI Testing and how to customize them for your needs
ISTQB Syllabus and Sample Exams
ISTQB Certified Tester AI Testing v.2021 Sample Exam
Syllabus (ISTQB Foundation Level: CTFL)
Sample Exams
ISTQB Certified Tester Foundation Level v.2018 Sample Exam A
ISTQB Certified Tester Foundation Level v.2018 Sample Exam B
ISTQB Certified Tester Foundation Level v.2018 Sample Exam C
Glossary
Glossary
Sample Exams
Syllabus (ISTQB AI Testing: CT-AI)
ISTQB Recommended Reading for CT-AI
12.2 ISTQB® Documents
12.3 Books and Articles
12.4 Other References
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[B04] Chris Wiltz, Can Apple Use Its Latest AI Chip for More Than Photos?, Electronics & Test, Artificial Intelligence, (accessed May 2021)
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[B05] HUAWEI Reveals the Future of Mobile AI at IFA 2017, Huawei Press Release, (accessed May 2021).
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[B08] G20 Ministerial Statement on Trade and Digital Economy: Annex. Available from:(accessed May 2021).
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[B09] Concrete Problems in AI Safety, Dario Amodei (Google Brain), Chris Olah (Google Brain), Jacob Steinhardt (Stanford University), Paul Christiano (UC Berkeley), John Schulman (OpenAI), Dan Man´e (Google Brain), March 2016. (accessed May 2021).
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[B17] Papernot, N. et al, Transferability in machine learning: from phenomena to black-box attacks using adversarial samples, arXiv preprint arXiv:1605.07277, 2016. (accessed May 2021).
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[B18] Chen et al, Metamorphic Testing: A Review of Challenges and Opportunities, ACM Comput. Surv. 51, 1, Article 4, January 2018. (accessed May 2021).
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[B19] Huai Liu, Fei-Ching Kuo, Dave Towey, and Tsong Yueh Chen., How effectively does metamorphic testing alleviate the oracle problem?, IEEE Transactions on Software Engineering 40, 1, 4–22, 2014
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[B20] James Whittaker, Exploratory Software Testing: Tips, Tricks, Tours and Techniques to Guide Test Design, 1. Edition, Addison-Wesley Professional, 2009.
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[B21] L. Wilkinson, A. Anand, and R. Grossman. High-dimensional visual analytics:Interactive exploration guided by pairwise views of point distributions. Visualization and Computer Graphics, IEEE Transactions on, 12(6):1363–1372, 2006, (accessed May 2021).
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[B22] Ryan Hafen and Terence Critchlow, EDA and ML – A Perfect Pair for Large-Scale Data Analysis, IEEE 27th International Symposium on Parallel and Distributed Processing, 2013, (accessed May 2021).
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[B23] Breck, Eric, Shanqing Cai, Eric Nielsen, Michael Salib, and D. Sculley., The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction, IEEE International Conference on Big Data (Big Data), 2017, (accessed May 2021).
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[B24] Harman, The Role of Artificial Intelligence in Software Engineering, In First International Workshop on Realizing AI Synergies in Software Engineering (RAISE), pp. 1-6. IEEE, June 2012, (accessed May 2021).
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[B25] Nilambri et al, A Survey on Automated Duplicate Detection in a Bug Repository, International Journal of Engineering Research & Technology (IJERT), 2014, (accessed May 2021)
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[B26] Kim, D.; Wang, X.; Kim, S.; Zeller, A.; Cheung, S.C.; Park, S. (2011). “Which Crashes Should I Fix First? Predicting Top Crashes at an Early Stage to Prioritize Debugging Efforts,” in the IEEE Transactions on Software Engineering, volume 37, (accessed May 2021).
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[B27] Mao et al, Sapienz: multi-objective automated testing for Android applications, Proceedings of the 25th International Symposium on Software Testing and Analysis, July 2016, (accessed May 2021).
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[B28] Rai et al, Regression Test Case Optimization Using Honey Bee Mating Optimization Algorithm with Fuzzy Rule Base, World Applied Sciences Journal 31 (4): 654-662, 2014, (accessed May 2021).
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[B29] Dusica Marijan, Arnaud Gotlieb, Marius Liaaen. A learning algorithm for optimizing continuous integration development and testing practice, Journal of Software :Practice and Experience, Nov 2018.
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[B30] Tosun et al, AI-Based Software Defect Predictors: Applications and Benefits in a Case Study, Proceedings of the Twenty-Second Innovative Applications of Artificial Intelligence Conference (IAAI-10), 2010.
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[B31] Kim et al, Predicting Faults from Cached History, 29th International Conference on Software Engineering (ICSE’07), 2007.
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[B32] Nagappan 2008 Nagappan et al, The Influence of Organizational Structure on Software Quality: An Empirical Case Study, Proceedings of the 30th international conference on Software engineering (ICSE’08), May 2008.
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[B33] Kuhn et al, Software Fault Interactions and Implications for Software Testing, IEEE Transactions on Software Engineering vol. 30, no. 6, (June 2004) pp. 418-421.
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[R01] Wikipedia contributors, "AI effect," Wikipedia, (accessed May 2021).
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[R02] https://mxnet.apache.org/ (accessed May 2021).
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[R03] https://docs.microsoft.com/en-us/cognitive-toolkit/ (accessed May 2021).
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[R04] IBM Watson
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[R05] https://www.tensorflow.org/ (accessed May 2021).
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[R06] https://keras.io/ (accessed May 2021).
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[R07] https://pytorch.org/ (accessed May 2021).
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[R08] https://scikit-learn.org/stable/whats_new/v0.23.html (accessed May 2021).
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[R09] NVIDIA VOLTA, (accessed May 2021).
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[R10] Cloud TPU, (accessed May 2021).
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[R11] Edge TPU, (accessed May 2021).
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[R12] Intel® Nervana™ Neural Network processors deliver the scale and efficiency demanded by deep learning model evolution, (accessed May 2021).
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[R13] The Evolution of EyeQ, (accessed May 2021).
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[R14] ImageNet (accessed May 2021).
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[R15] Google’s BERT (accessed May 2021).
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[R16] https://www.kaggle.com/datasets (accessed May 2021).
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[R17] https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-datascience-survey (accessed May 2021).
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[R18] MLCommons (accessed May 2021).
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[R19] DAWNBench (accessed May 2021).
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[R20] MLMark (accessed May 2021).
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[R21] Shaping Europe’s digital future (europa.eu) (accessed August 2021)
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[R22] https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai (accessed August 2021)
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[R23] Google GraphicsFuzz (accessed May 2021).
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[R24] http://www.openrobots.org/morse/doc/0.2.1/morse.html (accessed May 2021).
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[R25] https://ai.facebook.com/blog/open-sourcing-ai-habitat-a-simulation-platform-forembodied-ai-research/ (accessed May 2021).
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[R26] https://www.nvidia.com/en-gb/self-driving-cars/drive-constellation/ (accessed May 2021).
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[R27] https://uk.mathworks.com/discovery/artificial-intelligence.html#ai-with-matlab (accessed May 2021).
Extend your knowledge even further!
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Advancing_Neuromorphic_Computing_With_Loihi_A_Survey_of_Results_and_Outlook
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Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles
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Addressing Cognitive Biases in Augmented Business Decision Systems
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How effectively does metamorphic testing alleviate the oracle problem
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a-survey-on-automated-duplicate-detection-in-a-bug-repository-IJERTV3IS041769
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Regression Test Case Optimization Using Honey Bee Mating Optimization Algorithm with Fuzzy Rule Base
-
A learning algorithm for optimizing continuous integration development and testing practice
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The Influence of Organizational Structure on Software Quality
SE1
SE2
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