A systematic mapping study of 46 papers shows that automatic issue report classification uses traditional ML, deep learning, and LLMs but lacks practitioner involvement, industrial evaluations, and consideration of factors beyond accuracy.
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3 Pith papers cite this work. Polarity classification is still indexing.
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The paper adapts prior reflection frameworks into an eight-indicator scheme for software engineering and validates fine-tuned encoder-only transformers that classify student reflections with human-level agreement on most indicators.
No single MLOps tool covers the full lifecycle, so practitioners combine tools for orchestration, data versioning, experiment tracking, and cloud platforms.
citing papers explorer
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Automatic techniques for issue report classification: A systematic mapping study
A systematic mapping study of 46 papers shows that automatic issue report classification uses traditional ML, deep learning, and LLMs but lacks practitioner involvement, industrial evaluations, and consideration of factors beyond accuracy.
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Identifying Quality Indicators in Student Self-Reflections in Software Engineering
The paper adapts prior reflection frameworks into an eight-indicator scheme for software engineering and validates fine-tuned encoder-only transformers that classify student reflections with human-level agreement on most indicators.
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A Systematic Review of MLOps Tools: Tool Adoption, Lifecycle Coverage, and Critical Insights
No single MLOps tool covers the full lifecycle, so practitioners combine tools for orchestration, data versioning, experiment tracking, and cloud platforms.