Quantitative partial equivalence analysis quantifies behavioral differences between original and patched programs via symbolic analysis and a range-based heuristic for numerical domains.
Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering , pages =
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A systematic mapping study of 248 papers introduces a taxonomy of synergistic effects, inter-analysis workflows, and mapping functions to catalog patterns in combined program analysis techniques.
LDMDroid applies LLMs in a state-aware process to trigger data manipulation functions and uses visual cues to detect errors, finding 17 bugs across 24 Android apps with 14 developer confirmations.
PtrTrans builds a Pointer Knowledge Graph with points-to flows, struct abstractions, and Rust annotations to guide LLMs toward project-level C-to-Rust translations that cut unsafe code by 99.9% and raise functional correctness by 29.3%.
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
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Quantitative Symbolic Patch Impact Analysis
Quantitative partial equivalence analysis quantifies behavioral differences between original and patched programs via symbolic analysis and a range-based heuristic for numerical domains.
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Combined Program Analysis Techniques: A Systematic Mapping Study
A systematic mapping study of 248 papers introduces a taxonomy of synergistic effects, inter-analysis workflows, and mapping functions to catalog patterns in combined program analysis techniques.
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LDMDroid: Leveraging LLMs for Detecting Data Manipulation Errors in Android Apps
LDMDroid applies LLMs in a state-aware process to trigger data manipulation functions and uses visual cues to detect errors, finding 17 bugs across 24 Android apps with 14 developer confirmations.
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Project-Level C-to-Rust Translation via Pointer Knowledge Graphs
PtrTrans builds a Pointer Knowledge Graph with points-to flows, struct abstractions, and Rust annotations to guide LLMs toward project-level C-to-Rust translations that cut unsafe code by 99.9% and raise functional correctness by 29.3%.
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LLM4C2Rust: Large Language Models for Automated Memory-Safe Code Transpilation
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
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