CGFuse enables deep token-level fusion of graph-derived structural features into language models, yielding 10-16% BLEU and 6-11% CodeBLEU gains on code generation tasks.
citation dossier
Codebleu: a method for automatic evaluation of code synthesis
19Pith papers citing it
20reference links
cs.SEtop field · 13 papers
UNVERDICTEDtop verdict bucket · 15 papers
why this work matters in Pith
Pith has found this work in 19 reviewed papers. Its strongest current cluster is cs.SE (13 papers). The largest review-status bucket among citing papers is UNVERDICTED (15 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
representative citing papers
LLM-based Java program repair models lose over 50% of their bug-fixing success rate when presented with equivalent but syntactically varied buggy code.
A cascaded large-small model system generates edit sketches with the large model and applies them with the small model to make code editing both accurate and token-efficient.
SynthFix adaptively routes LLM code repairs to supervised fine-tuning or symbolic-reward fine-tuning, yielding up to 32% higher exact match on JavaScript and C vulnerability benchmarks.
A semantic correctness score based on execution matching shows LLM decompilers outperform heuristics for binary lifting while BLEU correlates poorly with functional accuracy.
Fine-tuning 7B code LLMs on a custom multi-file DSL dataset achieves structural fidelity of 1.00, high exact-match accuracy, and practical utility validated by expert survey and execution checks.
Hallucination Inspector verifies symbols in LLM-generated API migration code against a documentation-derived knowledge base using AST extraction, identifying scaffolding hallucinations and cutting false positives versus standard metrics in preliminary Android tests.
Evolving Parameter Isolation (EPI) periodically updates parameter isolation masks using online gradient signals during supervised fine-tuning to protect emerging task-critical parameters and reduce interference and forgetting.
ARuleCon uses AI agents plus execution-based checks to convert SIEM rules across vendors with 15% higher fidelity than standard LLM translation.
RoboTwin 2.0 automates diverse synthetic data creation for dual-arm robots via MLLMs and five-axis domain randomization, leading to 228-367% gains in manipulation success.
StarCoder2-15B matches or beats CodeLlama-34B on code tasks despite being smaller, and StarCoder2-3B outperforms prior 15B models, with open weights and exact training data identifiers released.
APPS benchmark shows models like GPT-Neo pass roughly 20% of test cases on introductory problems, indicating machine learning is beginning to learn basic coding.
Fine-tuning and prompting reduce some CWEs in AI-generated code but frequently introduce new weaknesses, with no strategy working reliably across models or languages.
Function-based chunking underperforms other strategies in RAG code completion by 3.57-5.64 points, with context length as the dominant factor.
LLM judges for human-AI coding co-creation show moderate performance (ROC-AUC 0.59) and low agreement, with co-creation success concentrating early in interactions.
DepTrans translates entire C repositories to Rust at 60.7% compilation success and 43.5% functional accuracy by combining reinforcement-aligned syntax training with dependency-guided iterative refinement.
StarCoderBase matches or beats OpenAI's code-cushman-001 on multi-language code benchmarks; the Python-fine-tuned StarCoder reaches 40% pass@1 on HumanEval while retaining other-language performance.
Code evaluation metrics like CrystalBLEU perform comparably to dedicated tools such as Dolos and JPlag when ranking plagiarized code pairs across modification levels on open datasets.
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.
citing papers explorer
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Deep Graph-Language Fusion for Structure-Aware Code Generation
CGFuse enables deep token-level fusion of graph-derived structural features into language models, yielding 10-16% BLEU and 6-11% CodeBLEU gains on code generation tasks.
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HEJ-Robust: A Robustness Benchmark for LLM-Based Automated Program Repair
LLM-based Java program repair models lose over 50% of their bug-fixing success rate when presented with equivalent but syntactically varied buggy code.
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Cascaded Code Editing: Large-Small Model Collaboration for Effective and Efficient Code Editing
A cascaded large-small model system generates edit sketches with the large model and applies them with the small model to make code editing both accurate and token-efficient.
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SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair
SynthFix adaptively routes LLM code repairs to supervised fine-tuning or symbolic-reward fine-tuning, yielding up to 32% higher exact match on JavaScript and C vulnerability benchmarks.
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Beyond BLEU: A Semantic Evaluation Method for Code Translation
A semantic correctness score based on execution matching shows LLM decompilers outperform heuristics for binary lifting while BLEU correlates poorly with functional accuracy.
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Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
Fine-tuning 7B code LLMs on a custom multi-file DSL dataset achieves structural fidelity of 1.00, high exact-match accuracy, and practical utility validated by expert survey and execution checks.
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Hallucination Inspector: A Fact-Checking Judge for API Migration
Hallucination Inspector verifies symbols in LLM-generated API migration code against a documentation-derived knowledge base using AST extraction, identifying scaffolding hallucinations and cutting false positives versus standard metrics in preliminary Android tests.
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Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
Evolving Parameter Isolation (EPI) periodically updates parameter isolation masks using online gradient signals during supervised fine-tuning to protect emerging task-critical parameters and reduce interference and forgetting.
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ARuleCon: Agentic Security Rule Conversion
ARuleCon uses AI agents plus execution-based checks to convert SIEM rules across vendors with 15% higher fidelity than standard LLM translation.
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RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
RoboTwin 2.0 automates diverse synthetic data creation for dual-arm robots via MLLMs and five-axis domain randomization, leading to 228-367% gains in manipulation success.
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StarCoder 2 and The Stack v2: The Next Generation
StarCoder2-15B matches or beats CodeLlama-34B on code tasks despite being smaller, and StarCoder2-3B outperforms prior 15B models, with open weights and exact training data identifiers released.
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Measuring Coding Challenge Competence With APPS
APPS benchmark shows models like GPT-Neo pass roughly 20% of test cases on introductory problems, indicating machine learning is beginning to learn basic coding.
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On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies
Fine-tuning and prompting reduce some CWEs in AI-generated code but frequently introduce new weaknesses, with no strategy working reliably across models or languages.
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How Does Chunking Affect Retrieval-Augmented Code Completion? A Controlled Empirical Study
Function-based chunking underperforms other strategies in RAG code completion by 3.57-5.64 points, with context length as the dominant factor.
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LLM-as-a-Judge for Human-AI Co-Creation: A Reliability-Aware Evaluation Framework for Coding
LLM judges for human-AI coding co-creation show moderate performance (ROC-AUC 0.59) and low agreement, with co-creation success concentrating early in interactions.
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Dependency-Guided Repository-Level C-to-Rust Translation with Reinforcement Alignment
DepTrans translates entire C repositories to Rust at 60.7% compilation success and 43.5% functional accuracy by combining reinforcement-aligned syntax training with dependency-guided iterative refinement.
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StarCoder: may the source be with you!
StarCoderBase matches or beats OpenAI's code-cushman-001 on multi-language code benchmarks; the Python-fine-tuned StarCoder reaches 40% pass@1 on HumanEval while retaining other-language performance.
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Can Code Evaluation Metrics Detect Code Plagiarism?
Code evaluation metrics like CrystalBLEU perform comparably to dedicated tools such as Dolos and JPlag when ranking plagiarized code pairs across modification levels on open datasets.
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A Survey on Large Language Models for Code Generation
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.