A software engineering perspective on engineering machine learning systems: State of the art and challenges

2021/06/25 by Görkem Giray · 5 citations
Computer Science · #Software Engineering Research #Software Reliability and Analysis Research #Software System Performance and Reliability

paper · doi:10.1016/j.jss.2021.111031

openalex publication_date 2021/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Citations

• The Oracle Problem in Software Testing: A Survey
• On Testing Non-Testable Programs
• Gradient-based learning applied to document recognition
• ImageNet Large Scale Visual Recognition Challenge
• Artificial intelligence: a modern approach
• A Survey on Transfer Learning
• A Survey on Metamorphic Testing
• Searching and synthesising ‘grey literature’ and ‘grey information’ in public health: critical reflections on three case studies
• Datasheets for datasets
• Engineering AI Systems: A Research Agenda
• Fastai: A Layered API for Deep Learning
• Machine Learning Testing: Survey, Landscapes and Horizons
• Metamorphic Testing: A New Approach for Generating Next Test Cases
• Learning Multiple Layers of Features from Tiny Images

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• From Pre-labeling to Production: Engineering Lessons from a Machine Learning Pipeline in the Public Sector
• Enhancing software product lines with machine learning components
• DIP-AI: A Discovery Framework for AI Innovation Projects
• Machine Learning Pipeline for Software Engineering: A Systematic Literature Review