Papers Code Summarization
“Code Summarization” 태그가 달린 논문 163편 · 필터 해제
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization
Existing studies show that code summaries help developers understand and maintain source code. Unfortunately, these summaries are often missing or outdated in software projects. Code summarization aims to generate natura…
Code SummarizationInformativenessSource Code SummarizationSoTaNa: The Open-Source Software Development Assistant
Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an effective software development assistant. …
Code SummarizationGPUparameter-efficient fine-tuningBenchmarking Causal Study to Interpret Large Language Models for Source Code
One of the most common solutions adopted by software researchers to address code generation is by training Large Language Models (LLMs) on massive amounts of source code. Although a number of studies have shown that LLMs…
BenchmarkingCausal InferenceCode CompletionCode Generation+2Semantic Similarity Loss for Neural Source Code Summarization
This paper presents a procedure for and evaluation of using a semantic similarity metric as a loss function for neural source code summarization. Code summarization is the task of writing natural language descriptions of…
Code SummarizationSemantic SimilaritySemantic Textual SimilaritySentence+1AST-MHSA : Code Summarization using Multi-Head Self-Attention
Code summarization aims to generate concise natural language descriptions for source code. The prevailing approaches adopt transformer-based encoder-decoder architectures, where the Abstract Syntax Tree (AST) of the sour…
Code SummarizationDecoderStatement-based Memory for Neural Source Code Summarization
Source code summarization is the task of writing natural language descriptions of source code behavior. Code summarization underpins software documentation for programmers. Short descriptions of code help programmers und…
Code SummarizationDecoderSource Code SummarizationNatural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review
This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained u…
Clone DetectionCode CompletionCode GenerationCode Summarization+3TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and code summarization. The current mainstream …
Clone DetectionCode SummarizationCode TranslationRepresentation LearningAutomatic Code Summarization via ChatGPT: How Far Are We?
To support software developers in understanding and maintaining programs, various automatic code summarization techniques have been proposed to generate a concise natural language comment for a given code snippet. Recent…
Code SummarizationTram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significant performance in this field, they are l…
Code SummarizationDecoderRetrievalSentence+1Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization
Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attenti…
Code SummarizationSource Code SummarizationCodeT5+: Open Code Large Language Models for Code Understanding and Generation
Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. Fir…
Arithmetic ReasoningCode CompletionCode GenerationCode Search+4The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
We present The Vault, a dataset of high-quality code-text pairs in multiple programming languages for training large language models to understand and generate code. We present methods for thoroughly extracting samples t…
Code GenerationCode SearchCode SummarizationIs ChatGPT the Ultimate Programming Assistant -- How far is it?
Recently, the ChatGPT LLM has received great attention: it can be used as a bot for discussing source code, prompting it to suggest changes, provide descriptions or even generate code. Typical demonstrations generally fo…
Code GenerationCode SummarizationProgram RepairPrompt EngineeringAutomatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineering. We are still learning how to best "program" these LLMs to help developers. We start with the intuition that develop…
Code SummarizationInformation RetrievalLanguage ModelingLanguage Modelling+2Better Language Models of Code through Self-Improvement
Pre-trained language models for code (PLMCs) have gained attention in recent research. These models are pre-trained on large-scale datasets using multi-modal objectives. However, fine-tuning them requires extensive super…
Code SummarizationData AugmentationOne Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefit…
AllCode SearchCode SummarizationExploring Distributional Shifts in Large Language Models for Code Analysis
We systematically study how three large language models with code capabilities - CodeT5, Codex, and ChatGPT - generalize to out-of-domain data. We consider two fundamental applications - code summarization, and code gene…
Code GenerationCode SummarizationGreener yet Powerful: Taming Large Code Generation Models with Quantization
ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently, large pretrained deep learning models h…
Code GenerationCode SummarizationModel CompressionQuantizationDemystifying What Code Summarization Models Learned
Study patterns that models have learned has long been a focus of pattern recognition research. Explaining what patterns are discovered from training data, and how patterns are generalized to unseen data are instrumental …
Code Summarizationimage-classificationImage Classification