375 citations · 1.3k across the 18 of their papers we have counts for
18 papers
Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code
Chunqiu Steven Xia, Courtney Miller
Agentic coding tools are increasingly used to make autonomous repository-level changes to real-world projects. Prior work has largely evaluated these contributions at the pre-merge…
SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery
Yuvraj Virk, Zack Edds, Chunqiu Steven Xia +1
Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems. However, they tend to converge onto a single high-level approach, t…
SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?
Hwiwon Lee, Jiawei Liu, Dongjun Kim +4
Finding a real vulnerability in complicated systems is a challenging, long-horizon task that demands reasoning across an entire codebase to produce a working proof-of-concept (PoC)…
Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?
Chunqiu Steven Xia, Zhe Wang, Yan Yang +2
Large Language Models (LLMs) are reshaping almost all industries, including software engineering. In recent years, a number of LLM agents have been proposed to solve real-world sof…
Agentless: Demystifying LLM-based Software Engineering Agents
Chunqiu Steven Xia, Yinlin Deng, Soren Dunn +1
Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test gen…
UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging
Cheryl Lee, Chunqiu Steven Xia, Longji Yang +4
Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understand…
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM
Chunqiu Steven Xia, Yinlin Deng, Lingming Zhang
LLMs have become the go-to choice for code generation tasks, with an exponential increase in the training, development, and usage of LLMs specifically for code generation. To evalu…
Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair
Yuxiang Wei, Chunqiu Steven Xia, Lingming Zhang
During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Mo…
Fuzz4All: Universal Fuzzing with Large Language Models
Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian +2
Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in various software systems. Systems under test (SUTs) that take in programming or formal language a…
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang +1
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with cu…
Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT
Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang +3
Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need for reliable systems. Generating valid input programs for fuzzing DL libraries is challengin…
Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
Chunqiu Steven Xia, Lingming Zhang
Automated Program Repair (APR) aims to automatically generate patches for buggy programs. Recent APR work has been focused on leveraging modern Large Language Models (LLMs) to dire…
Revisiting the Plastic Surgery Hypothesis via Large Language Models
Chunqiu Steven Xia, Yifeng Ding, Lingming Zhang
Automated Program Repair (APR) aspires to automatically generate patches for an input buggy program. Traditional APR tools typically focus on specific bug types and fixes through t…
Conversational Automated Program Repair
Chunqiu Steven Xia, Lingming Zhang
Automated Program Repair (APR) can help developers automatically generate patches for bugs. Due to the impressive performance obtained using Large Pre-Trained Language Models (LLMs…
Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng +2
Detecting bugs in Deep Learning (DL) libraries (e.g., TensorFlow/PyTorch) is critical for almost all downstream DL systems in ensuring effectiveness/safety for end users. Meanwhile…
Practical Program Repair in the Era of Large Pre-trained Language Models
Chunqiu Steven Xia, Yuxiang Wei, Lingming Zhang
Automated Program Repair (APR) aims to help developers automatically patch software bugs. However, current state-of-the-art traditional and learning-based APR techniques face the p…
Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning
Chunqiu Steven Xia, Lingming Zhang
Due to the promising future of Automated Program Repair (APR), researchers have proposed various APR techniques, including heuristic-based, template-based, and constraint-based tec…
Optimizing the hybrid parallelization of BHAC
Salvatore Cielo, Oliver Porth, Luigi Iapichino +3
We present our experience with the modernization on the GR-MHD code BHAC, aimed at improving its novel hybrid (MPI+OpenMP) parallelization scheme. In doing so, we showcase the use…