Introduces SolidityBench benchmark and SolidityScore metric for repository-level Solidity code generation, finding supervised fine-tuning outperforms prompting, CoT, ICL, and RAG methods on evaluated LLMs.
Testing CPS with design assumptions-based metamorphic relations and genetic programming
4 Pith papers cite this work. Polarity classification is still indexing.
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Search-based optimization over rendered in-cabin scenes finds up to 10× more VLM failures and up to 3.6× higher failure-cluster coverage than random generation for question answering and captioning.
CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
A review of 80 studies from 2021-2025 on transformer-based software vulnerability detection identifies trends in architectures, datasets, and challenges such as data imbalance and interpretability.
citing papers explorer
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Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning
Introduces SolidityBench benchmark and SolidityScore metric for repository-level Solidity code generation, finding supervised fine-tuning outperforms prompting, CoT, ICL, and RAG methods on evaluated LLMs.
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Search-based Testing of Vision Language Models for In-Car Scene Understanding
Search-based optimization over rendered in-cabin scenes finds up to 10× more VLM failures and up to 3.6× higher failure-cluster coverage than random generation for question answering and captioning.
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Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
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A systematic literature Review for Transformer-based Software Vulnerability detection
A review of 80 studies from 2021-2025 on transformer-based software vulnerability detection identifies trends in architectures, datasets, and challenges such as data imbalance and interpretability.