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Research Challenges of Industry 4.0 for Quality Management., and . ERP Future, volume 245 of Lecture Notes in Business Information Processing, page 121-137. Springer, (2015)Risk-based data validation in machine learning-based software systems., and . MaLTeSQuE@ESEC/SIGSOFT FSE, page 13-18. ACM, (2019)Data pipeline quality: Influencing factors, root causes of data-related issues, and processing problem areas for developers., , , and . J. Syst. Softw., (January 2024)Data Pipeline Quality: Influencing Factors, Root Causes of Data-related Issues, and Processing Problem Areas for Developers., , , and . CoRR, (2023)Integrating software quality models into risk-based testing., and . SE/SWM, volume P-292 of LNI, page 173-174. GI, (2019)An approach for assessing industrial IoT data sources to determine their data trustworthiness., and . Internet Things, (July 2023)Preliminary findings on the occurrence and causes of data smells in a real-world business travel data processing pipeline., , , and . SEA4DQ@ESEC/SIGSOFT FSE, page 18-21. ACM, (2022)Data Science Challenges to Improve Quality Assurance of Internet of Things Applications., and . ISoLA (2), volume 9953 of Lecture Notes in Computer Science, page 707-726. (2016)Technical Debt in Data-Intensive Software Systems., , and . SEAA, page 338-341. IEEE, (2019)Integrating software quality models into risk-based testing., and . Software Quality Journal, 26 (2): 809-847 (2018)