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20222026
most citedReACC: A Retrieval-Augmented Code Completion Framework

9 citations · 9 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.SE2026

R-SQL: Ranking Reward and Resampling for Text-to-SQL

Hojae Han, Yeonseok Jeong, Seung-won Hwang +2

Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limitations. First, they often scor…

cs.SE2026

DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode

Hojae Han, Jaejin Kim, Seung-won Hwang +2

This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first t…

cs.SE2025

ConvCodeWorld: Benchmarking Conversational Code Generation in Reproducible Feedback Environments

Hojae Han, Seung-won Hwang, Rajhans Samdani +1

Large language models (LLMs) have proven invaluable for code generation, particularly in interactive settings. However, existing code generation benchmarks fail to capture the dive…

cs.SE2024

PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation

Jaeseok Yoo, Hojae Han, Youngwon Lee +2

Code generation with large language models has shown significant promise, especially when employing retrieval-augmented generation (RAG) with few-shot examples. However, selecting…

cs.SE2024

ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models

Hojae Han, Jaejin Kim, Jaeseok Yoo +2

This paper aims to extend the code generation capability of large language models (LLMs) to automatically manage comprehensive software requirements from given textual descriptions…

cs.SE20229 cited

ReACC: A Retrieval-Augmented Code Completion Framework

Shuai Lu, Nan Duan, Hojae Han +3

Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development. Recent work has proved that…