Papers Code Summarization
“Code Summarization” 태그가 달린 논문 163편 · 필터 해제
Model Editing for LLMs4Code: How Far are We?
Large Language Models for Code (LLMs4Code) have been found to exhibit outstanding performance in the software engineering domain, especially the remarkable performance in coding tasks. However, even the most advanced LLM…
16kCode GenerationCode SummarizationContrastive Learning+5GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks
The increasing demand for spatiotemporal data and modeling tasks in geosciences has made geospatial code generation technology a critical factor in enhancing productivity. Although large language models (LLMs) have demon…
Code GenerationCode SummarizationHallucinationLanguage Modeling+4Building A Coding Assistant via the Retrieval-Augmented Language Model
Pretrained language models have shown strong effectiveness in code-related tasks, such as code retrieval, code generation, code summarization, and code completion tasks. In this paper, we propose COde assistaNt viA retri…
Code CompletionCode GenerationCode SummarizationLanguage Modeling+3ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries
Recent advancements in large language models (LLMs) have significantly enhanced their ability to understand both natural language and code, driving their use in tasks like natural language-to-code (NL2Code) and code summ…
Code SummarizationHallucinationDo Current Language Models Support Code Intelligence for R Programming Language?
Recent advancements in developing Pre-trained Language Models for Code (Code-PLMs) have urged many areas of Software Engineering (SE) and brought breakthrough results for many SE tasks. Though these models have achieved …
Code SummarizationMethod name predictionTACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning
The increasing prevalence of large language models (LLMs) such as GPT-4 in various applications has led to a surge in the size of prompts required for optimal performance, leading to challenges in computational efficienc…
Code SummarizationComputational EfficiencyQuestion Answeringreinforcement-learning+5Icing on the Cake: Automatic Code Summarization at Ericsson
This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach called Automatic Semantic Augmentation of P…
Code SummarizationInformation RetrievalLarge Language ModelRetrievalXMainframe: A Large Language Model for Mainframe Modernization
Mainframe operating systems, despite their inception in the 1940s, continue to support critical sectors like finance and government. However, these systems are often viewed as outdated, requiring extensive maintenance an…
Code SummarizationLanguage ModelingLanguage ModellingLarge Language Model+3DeepCodeProbe: Towards Understanding What Models Trained on Code Learn
Machine learning models trained on code and related artifacts offer valuable support for software maintenance but suffer from interpretability issues due to their complex internal variables. These concerns are particular…
Clone DetectionCode SummarizationComment GenerationDecision Making+1Source Code Summarization in the Era of Large Language Models
To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural language summary (i.e., comment) for a g…
Code SummarizationSource Code SummarizationInverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
Recent advancements in open-source code large language models (LLMs) have been driven by fine-tuning on the data generated from powerful closed-source LLMs, which are expensive to obtain. This paper explores whether it i…
Code GenerationCode SummarizationData AugmentationHumanEval+2An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks, increasing the application of code LLMs in software development. Despite the success of …
Code SummarizationMethod name predictionSensitivityESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization
(Source) code summarization aims to automatically generate succinct natural language summaries for given code snippets. Such summaries play a significant role in promoting developers to understand and maintain code. Insp…
Code SummarizationDecoderLanguage ModelingLanguage Modelling+4MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization
Binary malware summarization aims to automatically generate human-readable descriptions of malware behaviors from executable files, facilitating tasks like malware cracking and detection. Previous methods based on Large …
Code SummarizationLarge Language Models for Code Summarization
Recently, there has been increasing activity in using deep learning for software engineering, including tasks like code generation and summarization. In particular, the most recent coding Large Language Models seem to pe…
Code GenerationCode SummarizationAutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test ($\mathbf{90.9\%}$ vs. $\mathbf{90.2\%}$). In addition, AutoCoder offers a …
Class-level Code GenerationCode CompletionCode GenerationCode Repair+8On Training a Neural Network to Explain Binaries
In this work, we begin to investigate the possibility of training a deep neural network on the task of binary code understanding. Specifically, the network would take, as input, features derived directly from binaries an…
Code SummarizationDiagnosticSource Code SummarizationCalibration of Large Language Models on Code Summarization
A brief, fluent, and relevant summary can be helpful during program comprehension; however, such a summary does require significant human effort to produce. Often, good summaries are unavailable in software projects, whi…
Code SummarizationAnalyzing the Performance of Large Language Models on Code Summarization
Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We show that for the task of code summariza…
Code GenerationCode SummarizationCSA-Trans: Code Structure Aware Transformer for AST
When applying the Transformer architecture to source code, designing a good self-attention mechanism is critical as it affects how node relationship is extracted from the Abstract Syntax Trees (ASTs) of the source code. …
Code SummarizationStochastic Block Model