Paper PDFs
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Accepted
To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair
ISSTA 2026
Program RepairCode Agents
How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring
ISSTA 2026
Static AnalysisCode Agents
Phantom Rendering Detection: Identifying and Analyzing Unnecessary UI Computations
FSE 2026
Performance
MazeBreaker: Multi-Agent Reinforcement Learning for Dynamic Jailbreaking of LLM Security Defenses
ICSE 2026
LLM Security
HapRepair: Learn to Repair OpenHarmony Apps
FSE Industry 2025
Program RepairStatic Analysis
Effective Fine-tuning for Low-resource Languages: A Case Study of Cangjie
EMSE 2026
Misc
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning
TOSEM 2024
Survey
Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs
TOSEM 2026
Static Analysis
Manuscripts (Short Intro)
Executing as You Generate: Hiding Execution Latency in LLM Code Generation
Proposes EAGER, a parallel execution paradigm for LLM code generation that overlaps code generation and execution, reducing end-to-end latency by up to 55%.