91 citations · 99 across the 10 of their papers we have counts for
11 papers
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
Ankur Singh, Jinqiu Yang, Tse-Hsun Chen
AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files…
LLM4Log: A Systematic Review of Large Language Model-based Log Analysis
Zeyang Ma, Jinqiu Yang, Tse-Hsun Chen
Software systems generate massive, evolving, semi-structured logs that are central to reliability engineering and AIOps, yet difficult to analyze at scale under drift and limited l…
RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
Feng Lin, Dong Jae Kim, Zhenhao Li +3
When using LLMs to address Non-Functional Requirements (NFRs), developers may behave differently (e.g., expressing the same NFR in different words). Robust LLMs should output consi…
Studying and Benchmarking Large Language Models For Log Level Suggestion
Yi Wen Heng, Zeyang Ma, Zhenhao Li +3
Large Language Models (LLMs) have become a focal point of research across various domains, including software engineering, where their capabilities are increasingly leveraged. Rece…
A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion
Md Nakhla Rafi, Dong Jae Kim, Tse-Hsun Chen +1
Identifying and resolving software faults remains a challenging and resource-intensive process. Traditional fault localization techniques, such as Spectrum-Based Fault Localization…
LibreLog: Accurate and Efficient Unsupervised Log Parsing Using Open-Source Large Language Models
Zeyang Ma, Dong Jae Kim, Tse-Hsun Chen
Log parsing is a critical step that transforms unstructured log data into structured formats, facilitating subsequent log-based analysis. Traditional syntax-based log parsers are e…
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents
Zehao Wang, Dong Jae Kim, Tse-Hsun Chen
Configuration settings are essential for tailoring software behavior to meet specific performance requirements. However, incorrect configurations are widespread, and identifying th…
SBEST: Spectrum-Based Fault Localization Without Fault-Triggering Tests
Md Nakhla Rafi, Lorena Barreto Simedo Pacheco, An Ran Chen +3
Fault localization is a critical step in software maintenance. Yet, many existing techniques, such as Spectrum-Based Fault Localization (SBFL), rely heavily on the availability of…
Towards Better Graph Neural Network-based Fault Localization Through Enhanced Code Representation
Md Nakhla Rafi, Dong Jae Kim, An Ran Chen +2
Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization,…
LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing
Zeyang Ma, An Ran Chen, Dong Jae Kim +2
Logs are important in modern software development with runtime information. Log parsing is the first step in many log-based analyses, that involve extracting structured information…
Back to the Future! Studying Data Cleanness in Defects4J and its Impact on Fault Localization
Md Nakhla Rafi, An Ran Chen, Tse-Hsun Chen +1
For software testing research, Defects4J stands out as the primary benchmark dataset, offering a controlled environment to study real bugs from prominent open-source systems. Howev…