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.
Ferreira, Rui Abreu, and Pedro Cruz
7 Pith papers cite this work, alongside 96 external citations. Polarity classification is still indexing.
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Using a corpus of 5542 fault-injected traces from 38 DL programs, the study finds a 0.19 balanced accuracy gap in fault diagnosis between within-program and cross-program evaluation caused by program-specific feature structures.
GiANT uses divide-and-conquer and Chain-of-Thought prompting on 388 Code4rena reports to produce a 7,711-finding vulnerability corpus validated at 4.76/5 quality by manual review.
dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
AttackPathGNN introduces a State Interference Graph and conjunction pooling inside a GNN to detect cross-function vulnerabilities in Solidity contracts, reporting 92.3% F1 on SmartBugs Wild.
Chaintrix achieves 71.7% recall on 120 high-severity vulnerabilities in the EVMbench benchmark and outperforms the strongest frontier-model baseline by 26 percentage points through LLM pipelines grounded in a Cross-Contract Interaction Model and filtered by structural checks.
A controlled comparison of 12 smart contract pairs shows Move reduces security check density by 60% versus Solidity but increases code size by 47%, with developers reporting higher safety confidence in Move despite moderate learning difficulty.
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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Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs
Using a corpus of 5542 fault-injected traces from 38 DL programs, the study finds a 0.19 balanced accuracy gap in fault diagnosis between within-program and cross-program evaluation caused by program-specific feature structures.
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On the Shoulders of Giants: Empowering Automated Smart Contract Auditing via the GiAnt Corpus
GiANT uses divide-and-conquer and Chain-of-Thought prompting on 388 Code4rena reports to produce a 7,711-finding vulnerability corpus validated at 4.76/5 quality by manual review.
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Are We Lost in the Woods? Detecting Silent Semantic Faults for Random Forest Classifiers with Data-informed Static Analysis
dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
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AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling
AttackPathGNN introduces a State Interference Graph and conjunction pooling inside a GNN to detect cross-function vulnerabilities in Solidity contracts, reporting 92.3% F1 on SmartBugs Wild.
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CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing
Chaintrix achieves 71.7% recall on 120 high-severity vulnerabilities in the EVMbench benchmark and outperforms the strongest frontier-model baseline by 26 percentage points through LLM pipelines grounded in a Cross-Contract Interaction Model and filtered by structural checks.
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Comparing Smart Contract Paradigms: A Preliminary Study of Security and Developer Experience
A controlled comparison of 12 smart contract pairs shows Move reduces security check density by 60% versus Solidity but increases code size by 47%, with developers reporting higher safety confidence in Move despite moderate learning difficulty.