Papers Code Summarization
“Code Summarization” 태그가 달린 논문 163편 · 필터 해제
Rethinking the effects of data contamination in Code Intelligence
In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models (PLMs) and Large Language Models (LLMs)…
Code GenerationCode SummarizationCode TranslationAn LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
Large Language Models (LLMs) and other automated techniques have been increasingly used to support software developers by generating software artifacts such as code snippets, patches, and comments. However, accurately as…
Code GenerationCode SummarizationHumanEvalProgram RepairLEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models
Large Language Models for code often entail significant computational complexity, which grows significantly with the length of the input code sequence. We propose LeanCode for code simplification to reduce training and p…
Code SearchCode SummarizationDecoderEVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective
Assessing the programming capabilities of Large Language Models (LLMs) is crucial for their effective use in software engineering. Current evaluations, however, predominantly measure the accuracy of generated code on sta…
Adversarial AttackCode GenerationCode SummarizationVariational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Models
Recent advancements in source code summarization have leveraged transformer-based pre-trained models, including Large Language Models of Code (LLMCs), to automate and improve the generation of code summaries. However, ex…
Code SummarizationRerankingSource Code SummarizationLarge Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks
Pre-trained code models rely heavily on high-quality pre-training data, particularly human-written reference comments that bridge code and natural language. However, these comments often become outdated as software evolv…
Code SearchCode SummarizationCode-Craft: Hierarchical Graph-Based Code Summarization for Enhanced Context Retrieval
Understanding and navigating large-scale codebases remains a significant challenge in software engineering. Existing methods often treat code as flat text or focus primarily on local structural relationships, limiting th…
Code SummarizationInformation RetrievalRetrievalEnhancing Code LLM Training with Programmer Attention
Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost of collecting eye-tracking data, there ha…
Code SummarizationCommenting Higher-level Code Unit: Full Code, Reduced Code, or Hierarchical Code Summarization
Commenting code is a crucial activity in software development, as it aids in facilitating future maintenance and updates. To enhance the efficiency of writing comments and reduce developers' workload, researchers has pro…
Code SummarizationCode Summarization Beyond Function Level
Code summarization is a critical task in natural language processing and software engineering, which aims to generate concise descriptions of source code. Recent advancements have improved the quality of these summaries,…
Code SummarizationFew-Shot LearningIn-Context LearningPrompt Engineering+1Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption
Decoding strategies significantly influence the quality and diversity of the generated texts in large language models (LLMs), yet their impact on computational resource consumption, particularly GPU energy usage, is insu…
BenchmarkingCode SummarizationDiversityGPU+2Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering
Recently, large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation, significantly advancing the automation of SE tasks. However, assessing the quality of thes…
Code GenerationCode SummarizationCode TranslationSemantic Similarity+1Can Large Language Models Understand Intermediate Representations?
Intermediate Representations (IRs) are essential in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. This paper presents a pioneering empirical study to…
Code SummarizationShould Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
Learning-based techniques, especially advanced pre-trained models for code have demonstrated capabilities in code understanding and generation, solving diverse software engineering (SE) tasks. Despite the promising resul…
Clone DetectionCode SummarizationIncremental LearningAnalysis of Student-LLM Interaction in a Software Engineering Project
Large Language Models (LLMs) are becoming increasingly competent across various domains, educators are showing a growing interest in integrating these LLMs into the learning process. Especially in software engineering, L…
Code GenerationCode SummarizationHierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs
In large-scale software development, understanding the functionality and intent behind complex codebases is critical for effective development and maintenance. While code summarization has been widely studied, existing m…
Code SummarizationHow to Select Pre-Trained Code Models for Reuse? A Learning Perspective
Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detec…
Code GenerationCode SummarizationModel SelectionVulnerability DetectionAnalysis on LLMs Performance for Code Summarization
Code summarization aims to generate concise natural language descriptions for source code. Deep learning has been used more and more recently in software engineering, particularly for tasks like code creation and summari…
Code SummarizationOn the Compression of Language Models for Code: An Empirical Study on CodeBERT
Language models have proven successful across a wide range of software engineering tasks, but their significant computational costs often hinder their practical adoption. To address this challenge, researchers have begun…
Code SearchCode SummarizationKnowledge DistillationQuantization+1Transducer Tuning: Efficient Model Adaptation for Software Tasks Using Code Property Graphs
Large language models have demonstrated promising performance across various software engineering tasks. While fine-tuning is a common practice to adapt these models for downstream tasks, it becomes challenging in resour…
Code SummarizationCode TranslationLarge Language Model