Papers Code Completion
“Code Completion” 태그가 달린 논문 280편 · 필터 해제
Compilable Neural Code Generation with Compiler Feedback
Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering. Existing deep-learning a…
Code CompletionCode GenerationDecoderLanguage Modeling+3UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Pre-trained models for programming languages have recently demonstrated great success on code intelligence. To support both code-related understanding and generation tasks, recent works attempt to pre-train unified encod…
Code CompletionCode SearchContrastive LearningDecoderWhat Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code
Recently, many pre-trained language models for source code have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code completion, code search, and code summar…
Code CompletionCode SearchCode SummarizationCodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences
Code completion is an essential feature of IDEs, yet current autocompleters are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant drawbacks: grammar-based autocomp…
Code CompletionLanguage ModellingMulti-Task LearningExamining Zero-Shot Vulnerability Repair with Large Language Models
Human developers can produce code with cybersecurity bugs. Can emerging 'smart' code completion tools help repair those bugs? In this work, we examine the use of large language models (LLMs) for code (such as OpenAI's Co…
Code CompletionLong-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development envi…
Code CompletionCode GenerationCode SummarizationLanguage Modeling+3Time-Efficient Code Completion Model for the R Programming Language
In this paper we present a deep learning code completion model for the R language. We introduce several techniques to utilize language modeling based architecture in the code completion task. With these techniques, the m…
Code CompletionLanguage ModelingLanguage ModellingToward Less Hidden Cost of Code Completion with Acceptance and Ranking Models
Code completion is widely used by software developers to provide coding suggestions given a partially written code snippet. Apart from the traditional code completion methods, which only support single token completion a…
Code CompletionLanguage ModellingLearning to Complete Code with Sketches
Code completion is usually cast as a language modelling problem, i.e., continuing an input in a left-to-right fashion. However, in practice, some parts of the completion (e.g., string literals) may be very hard to predic…
Code CompletionCode GenerationLanguage ModellingA Syntax-Guided Edit Decoder for Neural Program Repair
Automated Program Repair (APR) helps improve the efficiency of software development and maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder architecture, to generate patches. Though exi…
Code CompletionCode GenerationDecoderProgram RepairEnergy-Based Models for Code Generation under Compilability Constraints
Neural language models can be successfully trained on source code, leading to applications such as code completion. However, their versatile autoregressive self-supervision objective overlooks important global sequence-l…
Code CompletionCode GenerationDiversityLearning to Extend Program Graphs to Work-in-Progress Code
Source code spends most of its time in a broken or incomplete state during software development. This presents a challenge to machine learning for code, since high-performing models typically rely on graph structured rep…
Code CompletionVariable misuseImproving Code Autocompletion with Transfer Learning
Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integrations. Recently, accuracy in autocompletion prediction improve…
Code CompletionTransfer LearningCode Completion by Modeling Flattened Abstract Syntax Trees as Graphs
Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they …
Code CompletionGraph AttentionMulti-Task LearningRepresentation LearningCodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine learning research for program understandin…
BIG-bench Machine LearningClone DetectionCloze TestCode Completion+9Neural Software Analysis
Many software development problems can be addressed by program analysis tools, which traditionally are based on precise, logical reasoning and heuristics to ensure that the tools are practical. Recent work has shown trem…
Code CompletionLogical ReasoningType predictionLearning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from leveraging program structure like control…
Code CompletionLearning to ExecuteProgram RepairProgram Synthesis+1On the Embeddings of Variables in Recurrent Neural Networks for Source Code
Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An example of this specificity i…
Bug fixingCode CompletionSpecificityA Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code
There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code ofte…
Bug fixingCode CompletionEmpirical Study of Transformers for Source Code
Initially developed for natural language processing (NLP), Transformers are now widely used for source code processing, due to the format similarity between source code and text. In contrast to natural language, source c…
Bug fixingCode Completion