55 citations · 114 across the 12 of their papers we have counts for
13 papers
LogCopilot: Automating Log Aggregation Analysis through Large Language Models
Senyu Xie, Chenxi Zhang, Tong Zhou +4
Logs record the runtime behavior of software and are widely used in various tasks such as debugging, testing, and fault diagnosis. With the increase in system size and complexity,…
Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industry
Xueying Du, Jiayi Feng, Yi Zou +6
Static analysis tools (SATs) are widely adopted in both academia and industry for improving software quality, yet their practical use is often hindered by high false positive rates…
Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis
Xueying Du, Kai Yu, Chong Wang +6
Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large codebases often suffer from high false positive rates.…
PromCopilot: Simplifying Prometheus Metric Querying in Cloud Native Online Service Systems via Large Language Models
Chenxi Zhang, Bicheng Zhang, Dingyu Yang +6
With the increasing complexity of modern online service systems, understanding the state and behavior of the systems is essential for ensuring their reliability and stability. Ther…
TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment
Zhiqiang Yuan, Weitong Chen, Hanlin Wang +3
Code translation transforms code between programming languages while preserving functionality, which is critical in software development and maintenance. While traditional learning…
Large Language Model-Based Agents for Software Engineering: A Survey
Junwei Liu, Kaixin Wang, Yixuan Chen +4
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially exten…
STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis
Junwei Liu, Yixuan Chen, Mingwei Liu +2
Repository-level code completion is challenging as it involves complicated contexts from multiple files in the repository. To date, researchers have proposed two technical categori…
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
Xueying Du, Geng Zheng, Kaixin Wang +9
Although LLMs have shown promising potential in vulnerability detection, this study reveals their limitations in distinguishing between vulnerable and similar-but-benign patched co…
Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing
Xueying Du, Mingwei Liu, Hanlin Wang +3
Software crash bugs cause unexpected program behaviors or even abrupt termination, thus demanding immediate resolution. However, resolving crash bugs can be challenging due to thei…
Boosting Static Resource Leak Detection via LLM-based Resource-Oriented Intention Inference
Chong Wang, Jianan Liu, Xin Peng +2
Resource leaks, caused by resources not being released after acquisition, often lead to performance issues and system crashes. Existing static detection techniques rely on mechanic…
Recommending Analogical APIs via Knowledge Graph Embedding
Mingwei Liu, Yanjun Yang, Yiling Lou +4
Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One…
Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation
Zhiqiang Yuan, Junwei Liu, Qiancheng Zi +3
In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-sh…
No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
Zhiqiang Yuan, Yiling Lou, Mingwei Liu +4
Unit testing is essential in detecting bugs in functionally-discrete program units. Manually writing high-quality unit tests is time-consuming and laborious. Although traditional t…