Papers Code Completion
“Code Completion” 태그가 달린 논문 280편 · 필터 해제
Constrained Decoding of Diffusion LLMs with Context-Free Grammars
Large language models (LLMs) have shown promising performance across diverse domains. Many practical applications of LLMs, such as code completion and structured data extraction, require adherence to syntactic constraint…
Code CompletionImpact-driven Context Filtering For Cross-file Code Completion
Retrieval-augmented generation (RAG) has recently demonstrated considerable potential for repository-level code completion, as it integrates cross-file knowledge with in-file preceding code to provide comprehensive conte…
Computational EfficiencyCode CompletionTreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs
Token-level code completion is one of the most critical features in modern Integrated Development Environments (IDEs). It assists developers by suggesting relevant identifiers and APIs during coding. While completions ar…
Prompt EngineeringCode CompletionMetamorphic Testing of Deep Code Models: A Systematic Literature Review
Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-related tasks. These models can process source…
Code CompletionEnhancing Project-Specific Code Completion by Inferring Internal API Information
Project-specific code completion is a critical task that leverages context from a project to generate accurate code. State-of-the-art methods use retrieval-augmented generation (RAG) with large language models (LLMs) and…
Code CompletionGitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities
The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent version updates while preserving backward compatibility. While existing code evol…
Code GenerationCode CompletionA Code Comprehension Benchmark for Large Language Models for Code
Large Language Models have shown impressive capabilities in coding tasks like code generation and code completion, as they have been trained on a large amount of code data. Also, since one of the core pretraining objecti…
Code GenerationCode CompletionRecurrent Memory-Augmented Transformers with Chunked Attention for Long-Context Language Modeling
We present a Transformer architecture for long-context language modeling that combines global attention with two biologically inspired components: chunked local attention and a gated FIFO memory mechanism. This unified a…
Code CompletionBeyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
AI-powered code assistants are widely used to generate code completions, significantly boosting developer productivity. However, these tools typically present suggestions without explaining their rationale, leaving their…
Code CompletionDecision MakingPlan for Speed -- Dilated Scheduling for Masked Diffusion Language Models
Masked diffusion language models (MDLM) have shown strong promise for non-autoregressive text generation, yet existing samplers act as implicit planners, selecting tokens to unmask via denoiser confidence or entropy scor…
Code CompletionGSM8KHumanEvalMath+3Seed-Coder: Let the Code Model Curate Data for Itself
Code data in large language model (LLM) pretraining is recognized crucial not only for code-related tasks but also for enhancing general intelligence of LLMs. Current open-source LLMs often heavily rely on human effort t…
Code CompletionCode GenerationLarge Language ModelHiLDe: Intentional Code Generation via Human-in-the-Loop Decoding
While AI programming tools hold the promise of increasing programmers' capabilities and productivity to a remarkable degree, they often exclude users from essential decision-making processes, causing many to effectively …
Code CompletionCode GenerationDecision MakingSWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
Large Language Models (LLMs) have shown strong capability in diverse software engineering tasks, e.g. code completion, bug fixing, and document generation. However, feature-driven development (FDD), a highly prevalent re…
Bug fixingChatbotCode CompletionReinforcement Learning (RL)Structure-Aware Corpus Construction and User-Perception-Aligned Metrics for Large-Language-Model Code Completion
Code completion technology based on large language model has significantly improved the development efficiency of programmers. However, in practical applications, there remains a gap between current commonly used code co…
Code CompletionLanguage ModelingLanguage ModellingLarge Language ModelAlignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification
Recent works have revealed the great potential of speculative decoding in accelerating the autoregressive generation process of large language models. The success of these methods relies on the alignment between draft ca…
Code CompletionQuestion AnsweringCan You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective
Code security and usability are both essential for various coding assistant applications driven by large language models (LLMs). Current code security benchmarks focus solely on single evaluation task and paradigm, such …
Code CompletionCode GenerationVulnerability DetectionCodeMixBench: Evaluating Large Language Models on Code Generation with Code-Mixed Prompts
Large Language Models (LLMs) have achieved remarkable success in code generation tasks, powering various applications like code completion, debugging, and programming assistance. However, existing benchmarks such as Huma…
Code CompletionCode GenerationHumanEvalmbppProcedural Memory Is Not All You Need: Bridging Cognitive Gaps in LLM-Based Agents
Large Language Models (LLMs) represent a landmark achievement in Artificial Intelligence (AI), demonstrating unprecedented proficiency in procedural tasks such as text generation, code completion, and conversational cohe…
AllCode CompletionText GenerationAKD : Adversarial Knowledge Distillation For Large Language Models Alignment on Coding tasks
The widespread adoption of Large Language Models (LLMs) for code generation, exemplified by GitHub Copilot\footnote{A coding extension powered by a Code-LLM to assist in code completion tasks} surpassing a million users,…
Code CompletionCode GenerationKnowledge DistillationModels AlignmentNoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation
Large Language Models (LLM) are typically trained on vast amounts of data from various sources. Even when designed modularly (e.g., Mixture-of-Experts), LLMs can leak privacy on their sources. Conversely, training such m…
Code CompletionMixture-of-Expertsparameter-efficient fine-tuningTransfer Learning