The field of code analysis and generation is rapidly advancing, with a focus on improving the accuracy and efficiency of large language models (LLMs) in coding tasks. Recent research has highlighted the potential of LLMs in code generation, code completion, and code review, as well as their limitations in terms of code quality and security. To address these limitations, researchers are exploring new approaches to code analysis and generation, including the use of intermediate representations, chain-of-thought prompting, and multimodal specification extraction. These advancements have the potential to significantly improve the quality and reliability of software development, and to enable new applications such as automated code review and repair. Notably, papers such as RTNinja and White-Basilisk have made significant contributions to the field by introducing novel frameworks for analyzing random telegraph noise signals and detecting code vulnerabilities. Additionally, papers like LLMCup and AssertCoder have demonstrated the effectiveness of LLMs in comment updating and assertion generation tasks.
Advancements in Code Analysis and Generation
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RTNinja: a generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices
White-Basilisk: A Hybrid Model for Code Vulnerability Detection
NL in the Middle: Code Translation with LLMs and Intermediate Representations
LLMCup: Ranking-Enhanced Comment Updating with LLMs
OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps
When Developer Aid Becomes Security Debt: A Systematic Analysis of Insecure Behaviors in LLM Coding Agents
A Mixture of Linear Corrections Generates Secure Code
It Only Gets Worse: Revisiting DL-Based Vulnerability Detectors from a Practical Perspective
SimStep: Chain-of-Abstractions for Incremental Specification and Debugging of AI-Generated Interactive Simulations
Is Quantization a Deal-breaker? Empirical Insights from Large Code Models
Efficient FRW Transitions via Stochastic Finite Differences for Handling Non-Stratified Dielectrics
Iceberg: Enhancing HLS Modeling with Synthetic Data
Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks
ReDemon UI: Reactive Synthesis by Demonstration for Web UI
Breaking the Myth: Can Small Models Infer Postconditions Too?
AssertCoder: LLM-Based Assertion Generation via Multimodal Specification Extraction
CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks
ARPaCCino: An Agentic-RAG for Policy as Code Compliance
DALI-PD: Diffusion-based Synthetic Layout Heatmap Generation for ML in Physical Design
A Code Comprehension Benchmark for Large Language Models for Code
Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction
MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoning
Evaluating Generated Commit Messages with Large Language Models
Function-to-Style Guidance of LLMs for Code Translation
REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
MetaLint: Generalizable Idiomatic Code Quality Analysis through Instruction-Following and Easy-to-Hard Generalization
LLAMA: Multi-Feedback Smart Contract Fuzzing Framework with LLM-Guided Seed Generation
Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization
When Retriever Meets Generator: A Joint Model for Code Comment Generation
Investigating the Performance of Small Language Models in Detecting Test Smells in Manual Test Cases
Towards Formal Verification of LLM-Generated Code from Natural Language Prompts