Towards a Common Testing Terminology for Software Engineering and Data Science Experts-Reference-Cited by-同舟云学术

Towards a Common Testing Terminology for Software Engineering and Data Science Experts

Published:2021 Issue: Volume: Page:281-289
ISSN:0302-9743
Container-title:Product-Focused Software Process Improvement
language:
Short-container-title:

Author:

Jöckel Lisa,Bauer Thomas,Kläs Michael,Hauer Marc P.,Groß Janek

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-91452-3_19

Reference16 articles.

1. Zhang, J.M., Harman, M., Ma, L., Liu, Y.: Machine learning testing: survey, landscapes and horizons. IEEE Trans. Softw. Eng. (2020). https://doi.org/10.1109/TSE.2019.2962027

2. Kläs, M., Adler, R., Jöckel, L., et al.: Using complementary risk acceptance criteria to structure assurance cases for safety-critical AI components. In: AISafety 2021 (2021)

3. Lecture Notes in Computer Science;M Kläs,2019

4. Riccio, V., Jahangirova, G., Stocco, A., et al.: Testing machine learning based systems: a systematic mapping. Empir. Softw. Eng. 25, 5193–5254 (2020)

5. IEEE Standard for Software and System Test Documentation. IEEE Std. 829 (2008)

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning;Product-Focused Software Process Improvement;2023-12-02

2. Artificial Intelligence Applied to Software Testing: A Tertiary Study;ACM Computing Surveys;2023-10-06

3. Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning;2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W);2023-06

4. Auditing and Testing AI – A Holistic Framework;Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Health, Operations Management, and Design;2022