2 citations · 3 across the 12 of their papers we have counts for
9 papers · 1 filter
Compress What You See, Not What You Say: Anchored Context Distillation for Latent-Observation Software Engineering Agents
Zhensheng Zou, Guoqing Wang, Dan Hao
Tool observations dominate the context of software-engineering agents, making long interaction histories costly to maintain. Existing context compression methods can discard inform…
DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation
Zhihao Gong, Junzhe Yu, Dong Huang +3
Current data science (DS) code generation benchmarks equate correctness with quality, overlooking execution time differences that span orders of magnitude between correct solutions…
CausalRepair: Bridging the Causality Gap in Large Language Model-Based Automated Program Repair via Dual-Slicing
Linhao Wu, Yizhou Chen, Zhen Yang +2
Automated Program Repair (APR) has recently benefited from Large Language Models (LLMs), yet their effectiveness heavily depends on repair context. Existing LLM-based APR methods s…
WarmTuner: Program-Specific Warm Starts for Compiler Autotuning via Offline-to-Online Reinforcement Learning
Tianlu Qiao, Mingxuan Zhu, Zeyu Sun +1
Compilers are fundamental software tools that translate high-level programs into machine code. Modern compilers expose hundreds of optimizations, each turned on or off through an o…
GapForge: Directed Compiler Fuzzing via Coverage-Gap Analysis
Mingxuan Zhu, Qingyuan Liang, Junjie Chen +2
Modern compiler codebases (e.g., GCC and LLVM) are large and complex, making comprehensive coverage across diverse code regions highly challenging. Most existing test generation te…
DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging
Linhao Wu, Yifei Pei, Zhen Yang +9
Automated Program Repair (APR) has benefited from the code understanding and generation capabilities of Large Language Models (LLMs). Existing feedback-based APR methods iterativel…
TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning
Guoqing Wang, Chengran Yang, Xiaoxuan Zhou +4
With the rapid evolution of LLMs, automated software testing is witnessing a paradigm shift. While proprietary models like GPT-4o demonstrate impressive capabilities, their high de…
TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation
Zhihao Gong, Zeyu Sun, Dong Huang +3
While Large Language Models (LLMs) have substantially improved the functional correctness of code translation, the critical dimension of \textit{execution efficiency} remains overl…
Search-Induced Issues in Web-Augmented LLM Code Generation: Detecting and Repairing Error-Inducing Pages
Guoqing Wang, Zeyu Sun, Xiaofei Xie +4
Web-augmented large language models (LLMs) offer promising capabilities for automatic code generation. However, integrating live web search exposes models to unreliable or maliciou…