PoVSmith automates PoV test generation for library vulnerabilities in apps via call paths and LLM feedback, correctly identifying 96% of entry points and producing effective attack tests in 55% of 33 evaluated Java pairs.
InPro- ceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering(Singapore, Singapore) (ESEC/FSE 2022)
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FGDM is a sequential multi-agent system using flow graphs, CoT/ToT prompts, and FAISS retrieval that reports mean Levenshtein distance reductions of 24.33 (Python) and 8.37 (C) with cosine similarities of 0.951 and 0.974 on 100 programs from ten open-source projects.
LLMs reach moderate macro-F1 scores of 0.36-0.37 when classifying code review comments into six smells and three useful intents, with one-shot examples helping some models on intent labels.
citing papers explorer
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Generating Proof-of-Vulnerability Tests to Help Enhance the Security of Complex Software
PoVSmith automates PoV test generation for library vulnerabilities in apps via call paths and LLM feedback, correctly identifying 96% of entry points and producing effective attack tests in 55% of 33 evaluated Java pairs.
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FGDM: Reasoning Aware Multi-Agentic Framework for Software Bug Detection using Chain of Thought and Tree of Thought Prompting
FGDM is a sequential multi-agent system using flow graphs, CoT/ToT prompts, and FAISS retrieval that reports mean Levenshtein distance reductions of 24.33 (Python) and 8.37 (C) with cosine similarities of 0.951 and 0.974 on 100 programs from ten open-source projects.
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Automated Classification of Human Code Review Comments with Large Language Models
LLMs reach moderate macro-F1 scores of 0.36-0.37 when classifying code review comments into six smells and three useful intents, with one-shot examples helping some models on intent labels.