52 citations · 63 across the 12 of their papers we have counts for
7 papers · 1 filter
Speculative Forking: Turning Idle Wait into Parallel Exploration for Repository Environment Setup
Yuling Shi, Mingchu Zhong, Zhensu Sun +8
Before developers can use a software project, they must first get it to run. This often requires installing the right language runtime, system packages, build tools, and test comma…
SWE-at-Scale: Scalable Issue Synthesis from Historical Bug Fixes
Silin Chen, Zihan Zhang, Xiaodong Gu +7
As coding agents become increasingly capable, robust evaluation requires a broader and more diverse supply of verifiable software engineering (SWE) tasks. However, real-world tasks…
Schrödinger's Code Repository: Have LLMs Learned SWE-bench or Memorized It?
Silin Chen, Yufei Yang, Xiaodong Gu +3
Repository-level coding benchmarks have become the standard for evaluating coding agents, yet they inherently suffer from data leakage because they are built upon popular open-sour…
EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
Zimu Wang, Yuling Shi, Mengfan Li +4
Code efficiency is a fundamental aspect of software quality, yet how to harness large language models (LLMs) to optimize programs remains challenging. Prior approaches have sought…
Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems
Shengming Zhao, Yuchen Shao, Yuheng Huang +4
Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges w…
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers
Yuling Shi, Hongyu Zhang, Chengcheng Wan +1
Large language models have catalyzed an unprecedented wave in code generation. While achieving significant advances, they blur the distinctions between machine- and human-authored…
On the Effectiveness of Large Language Models in Domain-Specific Code Generation
Xiaodong Gu, Meng Chen, Yalan Lin +6
Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire…