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20212026
most citedStudying Duplicate Logging Statements and Their Relationships with Code Clones

27 citations · 32 across the 16 of their papers we have counts for

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22 papers · 1 filter

cs.SE2026

AgentExecutor: Partial Code Execution via Agentic Context Generation

Junkai Chen, Chengran Yang, Xing Hu +3

Executing code snippets is essential for dynamic program analysis, but it remains challenging to execute an arbitrary code snippet due to issues like missing context and incomplete…

cs.SE2026

SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvements

Pengyu Xue, He Yang Yuan, Xin Wang +6

Although coding agents have achieved impressive performance on correctness-oriented benchmarks, their ability to make behavior-preserving non-functional improvements (NFIs) remains…

cs.SE2026

Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs

He Yang Yuan, Xin Wang, Kundi Yao +3

Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. However, insecure logging practice…

cs.SE2026

ReLog: Execution-Aware Logging with Runtime Feedback for LLM-Oriented Debugging

Xin Wang, Yang Feng, Xiaoqian Jiao +3

Logging statements are essential for software debugging and maintenance. However, existing approaches to automatic logging generation rely on static analysis and produce statements…

cs.SE2026

LLM4Perf: Large Language Models Are Effective Samplers for Multi-Objective Performance Modeling

Xin Wang, Zhenhao Li, Zishuo Ding

The performance of modern software systems is critically dependent on their complex configuration options. Building accurate performance models to navigate this vast space requires…

cs.SE2025

Evaluating Software Process Models for Multi-Agent Class-Level Code Generation

Wasique Islam Shafin, Md Nakhla Rafi, Zhenhao Li +1

Modern software systems require code that is not only functional but also maintainable and well-structured. Although Large Language Models (LLMs) are increasingly used to automate…