EvoGraph turns linear AI-assisted programming into a manipulable graph of branching histories, reducing cognitive load and enabling better iteration according to a user study with 20 developers.
Automating code review activities by large-scale pre- training,
5 Pith papers cite this work. Polarity classification is still indexing.
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LLM-based security code review is vulnerable to framing bias, with a novel iterative refinement attack achieving 100% success in reintroducing vulnerabilities across real projects.
A multi-agent LLM framework with Behavioral Specification Graphs preserves business logic in legacy modernization, achieving non-zero mean BER on all tested scenarios where baseline LLM approaches scored zero.
SAILOR combines static analysis and LLM-orchestrated synthesis to automatically generate symbolic execution harnesses, discovering 379 previously unknown memory-safety vulnerabilities across 10 large open-source C/C++ projects where the strongest baseline found only 12.
Empirical study finds Git references enable over 86% success in mapping NVD records to vulnerability-fixing commits while non-Git references succeed under 14%, yielding an automated pipeline and external mining that together cover only 11.3% of records at 87% precision.
citing papers explorer
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Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph
EvoGraph turns linear AI-assisted programming into a manipulable graph of branching histories, reducing cognitive load and enabling better iteration according to a user study with 20 developers.
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Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
LLM-based security code review is vulnerable to framing bias, with a novel iterative refinement attack achieving 100% success in reintroducing vulnerabilities across real projects.
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AgentModernize: Preserving Business Logic in Legacy Modernization with Multi-Agent LLMs and Behavioral Specification Graphs
A multi-agent LLM framework with Behavioral Specification Graphs preserves business logic in legacy modernization, achieving non-zero mean BER on all tested scenarios where baseline LLM approaches scored zero.
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Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery
SAILOR combines static analysis and LLM-orchestrated synthesis to automatically generate symbolic execution harnesses, discovering 379 previously unknown memory-safety vulnerabilities across 10 large open-source C/C++ projects where the strongest baseline found only 12.
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Mapping NVD Records to Their Vulnerability-fixing Commits: How Hard is It?
Empirical study finds Git references enable over 86% success in mapping NVD records to vulnerability-fixing commits while non-Git references succeed under 14%, yielding an automated pipeline and external mining that together cover only 11.3% of records at 87% precision.