Papers Code Completion
“Code Completion” 태그가 달린 논문 280편 · 필터 해제
CoRoVA: Compressed Representations for Vector-Augmented Code Completion
Retrieval-augmented generation has emerged as one of the most effective approaches for code completion enhancement, especially when repository-level context is important. However, adding this extra retrieved context sign…
Code CompletionSpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion
Large Language Models (LLMs) excel at code-related tasks but often struggle in realistic software repositories, where project-specific APIs and cross-file dependencies are crucial. Retrieval-augmented methods mitigate th…
Code CompletionOn Pretraining for Project-Level Code Completion
Repository-level pretraining is commonly used to enable large language models for code to leverage codebase-wide context. This enhances their ability to generate accurate and context-aware code completions. In this work,…
Code CompletionSpareCodeSearch: Searching for Code Context When You Have No Spare GPU
Retrieval-Augmented Generation (RAG) frameworks aim to enhance Code Language Models (CLMs) by including another module for retrieving relevant context to construct the input prompt. However, these retrieval modules commo…
Code CompletionMellum: Production-Grade in-IDE Contextual Code Completion with Multi-File Project Understanding
We present the Mellum models family, open-weight code completion models designed for interactive use in JetBrains IDEs. Mellums have 4B parameters, adopt a Llama-style architecture, and are pre-trained on ~4T tokens of p…
Code CompletionRelative Positioning Based Code Chunking Method For Rich Context Retrieval In Repository Level Code Completion Task With Code Language Model
Code completion can help developers improve efficiency and ease the development lifecycle. Although code completion is available in modern integrated development environments (IDEs), research lacks in determining what ma…
Semantic SimilarityCode CompletionChallenge on Optimization of Context Collection for Code Completion
The rapid advancement of workflows and methods for software engineering using AI emphasizes the need for a systematic evaluation and analysis of their ability to leverage information from entire projects, particularly in…
Code CompletionCode4MeV2: a Research-oriented Code-completion Platform
The adoption of AI-powered code completion tools in software development has increased substantially, yet the user interaction data produced by these systems remain proprietary within large corporations. This creates a b…
Code CompletionLongCodeZip: Compress Long Context for Code Language Models
Code generation under long contexts is becoming increasingly critical as Large Language Models (LLMs) are required to reason over extensive information in the codebase. While recent advances enable code LLMs to process l…
Question AnsweringCode GenerationCode CompletionLita: Light Agent Uncovers the Agentic Coding Capabilities of LLMs
Large language models (LLMs) are increasingly being applied to programming tasks, ranging from single-turn code completion to autonomous agents. Current code agent designs frequently depend on complex, hand-crafted workf…
Code CompletionRANGER -- Repository-Level Agent for Graph-Enhanced Retrieval
General-purpose automated software engineering (ASE) includes tasks such as code completion, retrieval, repair, QA, and summarization. These tasks require a code retrieval system that can handle specific queries about co…
Natural Language QueriesQuestion AnsweringCode CompletionCode SearchLocalizing Malicious Outputs from CodeLLM
We introduce FreqRank, a mutation-based defense to localize malicious components in LLM outputs and their corresponding backdoor triggers. FreqRank assumes that the malicious sub-string(s) consistently appear in outputs …
Code GenerationCode CompletionCodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion
Repository-level code completion automatically predicts the unfinished code based on the broader information from the repository. Recent strides in Code Large Language Models (code LLMs) have spurred the development of r…
Code CompletionLAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation
KV Cache is commonly used to accelerate LLM inference with long contexts, yet its high memory demand drives the need for cache compression. Existing compression methods, however, are largely heuristic and lack dynamic bu…
Code CompletionEvolKV: Evolutionary KV Cache Compression for LLM Inference
Existing key-value (KV) cache compression methods typically rely on heuristics, such as uniform cache allocation across layers or static eviction policies, however, they ignore the critical interplays among layer-specifi…
Code CompletionThe Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion
Code completion entails the task of providing missing tokens given a surrounding context. It can boost developer productivity and serve as a code discovery tool. Code completion has recently been approached with Large La…
Code CompletionThe Hidden Cost of Readability: How Code Formatting Silently Consumes Your LLM Budget
Source code is usually formatted with elements like indentation and newlines to improve readability for human developers. However, these visual aids do not seem to be beneficial for large language models (LLMs) in the sa…
Code CompletionFrom Context to Intent: Reasoning-Guided Function-Level Code Completion
The growing capabilities of Large Language Models (LLMs) have led to their widespread adoption for function completion within code repositories. Recent studies on such tasks show promising results when explicit instructi…
Code CompletionSaraCoder: Orchestrating Semantic and Structural Cues for Resource-Optimized Repository-Level Code Completion
Despite Retrieval-Augmented Generation improving code completion, traditional retrieval methods struggle with information redundancy and a lack of diversity within limited context windows. To solve this, we propose a res…
Code CompletionNext Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
The rapid advancement of large language models (LLMs) has led to the widespread adoption of AI-powered coding assistants integrated into a development environment. On one hand, low-latency code completion offers completi…
Code Completion