STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.
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API misuses in data-centric libraries share key characteristics with deep learning misuses and occur regardless of whether documentation directives are present.
DeepFWI is a multi-modal LSTM model with cross-attention that identifies bug-sensitive warnings at warning granularity, reaching 67.06% F1 on a 280k-warning dataset and surfacing 25 confirmed bugs in four open-source projects.
citing papers explorer
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Towards Better Static Code Analysis Reports: Sentence Transformer-based Filtering of Non-Actionable Alerts
STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.
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An Empirical Study of API Misuses of Data-Centric Libraries
API misuses in data-centric libraries share key characteristics with deep learning misuses and occur regardless of whether documentation directives are present.
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DeepFWI: Identifying Bug-Sensitive Warnings with Multi-Modal Code-Warning Semantics
DeepFWI is a multi-modal LSTM model with cross-attention that identifies bug-sensitive warnings at warning granularity, reaching 67.06% F1 on a 280k-warning dataset and surfacing 25 confirmed bugs in four open-source projects.