A survey of 419 practitioners shows strong reliance on reusable GitHub Actions for core CI/CD tasks but limited adoption of reusable workflows, with copy-pasting remaining common due to versioning and trust issues.
InInternational Conference on Source Code Analysis and Manipulation (SCAM)
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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.
An empirical study of security DSLs and code analyzers finds few common concepts, overly general weakness descriptions, and that even experts are overwhelmed by the complexity of potential mappings.
citing papers explorer
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Automation and Reuse Practices in GitHub Actions Workflows: A Practitioner's Perspective
A survey of 419 practitioners shows strong reliance on reusable GitHub Actions for core CI/CD tasks but limited adoption of reusable workflows, with copy-pasting remaining common due to versioning and trust issues.
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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.
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Can I Check What I Designed? Mapping Security Design DSLs to Code Analyzers
An empirical study of security DSLs and code analyzers finds few common concepts, overly general weakness descriptions, and that even experts are overwhelmed by the complexity of potential mappings.