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Conversational systems or chatbots are an example of AI-Infused Applications (AIIA).
Failure Modes in Medical Device Software: an Analysis of 15 Years of Recall Data
Wallace, D. R.; and Kuhn, D. R. 2001 · 2001
Earlier work this paper cites.
An Investigation of the Applicability of Design of Experiments to Software Testing
Kuhn, D. R.; and Reilly, M. J. 2002 · 2002
Earlier work this paper cites.
Software Fault Interactions and Implications for Software Testing
Kuhn, D. R.; Wallace, D. R.; and Gallo, A. M., Jr. 2004 · 2004
Earlier work this paper cites.
On effectiveness of pairwise methodology for testing network-centric software
Bell, K. Z.; and Vouk, M. A. 2005 · 2005
Earlier work this paper cites.
Optimizing Effectiveness and Efficiency of Software Testing: A Hybrid Approach
Bell, K. Z. 2006 · 2006
Earlier work this paper cites.
Characterizing Failure-causing Parameter Interactions by Adaptive Testing
Zhang, Z.; and Zhang, J. 2011 · 2011
Earlier work this paper cites.
Detection of data drift and outliers affecting machine learning model performance over time
Ackerman, S.; Farchi, E.; Raz, O.; Zalmanovici, M.; and Dube, P. 2021b · 2012
Earlier work this paper cites.
Bridging the Gap between ML Solutions and Their Business Requirements Using Feature Interactions
Barash, G.; Farchi, E.; Jayaraman, I.; Raz, O.; Tzoref-Brill, R.; and Zalmanovici, M. 2019 · 2019
Earlier work this paper cites.
FreaAI: Automated Extraction of Data Slices to Test Machine Learning Models
Ackerman, S.; Raz, O.; and Zalmanovici, M. 2020 · 2020
Earlier work this paper cites.
Do Not Have Enough Data? Deep Learning to the Rescue!
Anaby-Tavor, A.; Carmeli, B.; Goldbraich, E.; Kantor, A.; Kour, G.; Shlomov, S.; Tepper, N.; and Zwerdling, N. 2020 · 2020
Cited alongside, same era.
Efficient Intent Detection with Dual Sentence Encoders
Casanueva, I.; Temcinas, T.; Gerz, D.; Henderson, M.; and Vulic, I. 2020 · 2020
Cited alongside, same era.
Balancing via Generation for Multi-Class Text Classification Improvement
Tepper, N.; Goldbraich, E.; Zwerdling, N.; Kour, G.; Anaby Tavor, A.; and Carmeli, B. 2020 · 2020
Cited alongside, same era.
Machine Learning Model Drift Detection Via Weak Data Slices
Ackerman, S.; Dube, P.; Farchi, E.; Raz, O.; and Zalmanovici, M. 2021a · 2021
Cited alongside, same era.
3-D QA Framework for Testing AI-infused Conversational Interfaces
Coforge. Accessed April 2022 · 2021
Cited alongside, same era.
Chatbottest
chatbottest. Accessed April 2022 · 2022
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Botium Box Chatbot Testing
Cyara. Accessed April 2022 · 2022
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DeepChecks User Guide
DeepChecks. Accessed April 2022 · 2022
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Testing of AI Systems Need Not Be Complicated
InfoSys. Accessed April 2022 · 2022
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Conversational AI testing tool
QBox. Accessed April 2022 · 2022
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Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems
Rabinovich, E.; Vetzler, M.; Boaz, D.; Kumar, V.; Pandey, G.; and Anaby-Tavor, A. 2022 · 2022
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AIEnsured
testAIng.com. Accessed April 2022 · 2022
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Kour, G.; Zalmanovici, M.; Raz, O.; Ackerman, S.; and Anaby-Tavor, A. AAAI ESDMLS 2021 · 2021
Cited alongside, same era.
Software Engineering for AI-Based Systems: A Survey
Martínez-Fernández, S.; Bogner, J.; Franch, X.; Oriol, M.; Siebert, J.; Trendowicz, A.; Vollmer, A. M.; and Wagner, S. 2021 · 2021
Cited alongside, same era.
Detecting model drift using polynomial relations
Roffe, E.; Ackerman, S.; Raz, O.; and Farchi, E. 2021 · 2021
Cited alongside, same era.
Theory and Practice of Quality Assurance for Machine Learning Systems An Experiment Driven Approach
Ackerman, S.; Barash, G.; Farchi, E.; Raz, O.; and Shehory, O. 2022 · 2022
Cited alongside, same era.
Density-based interpretable hypercube region partitioning for mixed numeric and categorical data
Ackerman, S.; Farchi, E.; Raz, O.; Zalmanovici, M.; and Zohar, M. 2021c
Cited in the paper.
Automatically detecting data drift in machine learning classifiers
Ackerman, S.; Raz, O.; Zalmanovici, M.; and Zlotnick, A. 2021d
Cited in the paper.
Testing of AI/ML based systems
Wipro. Accessed April 2022 · 2022
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