MDE recursively deduplicates data representations in pointer analysis to eliminate over 90% redundant set-union operations, yielding up to 18x lower peak memory and 8x faster runtime on SPEC benchmarks with gains increasing for larger programs.
In defense of soundiness: a manifesto
4 Pith papers cite this work. Polarity classification is still indexing.
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SpecDetect4ML detects 22 ML code smells via DSL specifications and CPG-based analysis, reporting 95.82% precision and 88.14% recall on 890 ML systems while outperforming prior tools.
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
Hidden dependencies and component variants in SBOMs cause inconsistent vulnerability reporting and VEX handling across scanners.
citing papers explorer
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Points-to Analysis Using MDE: A Multi-level Deduplication Engine for Repetitive Data and Operations
MDE recursively deduplicates data representations in pointer analysis to eliminate over 90% redundant set-union operations, yielding up to 18x lower peak memory and 8x faster runtime on SPEC benchmarks with gains increasing for larger programs.
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ML Code Smells: From Specification to Detection
SpecDetect4ML detects 22 ML code smells via DSL specifications and CPG-based analysis, reporting 95.82% precision and 88.14% recall on 890 ML systems while outperforming prior tools.
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SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
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Hidden Dependencies and Component Variants in SBOM-Based Software Composition Analysis
Hidden dependencies and component variants in SBOMs cause inconsistent vulnerability reporting and VEX handling across scanners.