From the 1 of 4 linked papers with an AI index.
6 papers
Escaping the Self-Repair Trap: Improving Test Oracle Generation via Dual-Context Awareness
Kefan Li, Hongyue Yu, Yuan Yuan
Large Language Models (LLMs) have shown strong potential for regression-oracle completion, where a test prefix is given and the current program version is treated as expected behav…
MemTrace: Tracing and Attributing Errors in Large Language Model Memory Systems
Xinle Deng, Ruobin Zhong, Hujin Peng +15
The paper introduces MemTrace, a framework that converts large language model memory pipelines into executable graphs to trace and attribute errors, and provides a benchmark (MemTr…
DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents
Shijie Cao, Yuan Yuan, Jing Liu
Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension: static benchmarks encourag…
CoCoEvo: Co-Evolution of Programs and Test Cases to Enhance Code Generation
Kefan Li, Yuan Yuan, Hongyue Yu +2
Large Language Models (LLMs) have shown remarkable performance in automated code generation. However, existing approaches often rely heavily on pre-defined test cases, which become…
Revisiting Evolutionary Program Repair via Code Language Model
Yunan Wang, Tingyu Guo, Zilong Huang +1
Software defects are an inherent part of software development and maintenance. To address these defects, Automated Program Repair (APR) has been developed to fix bugs automatically…
Large Language Models as Test Case Generators: Performance Evaluation and Enhancement
Kefan Li, Yuan Yuan
Code generation with Large Language Models (LLMs) has been extensively studied and achieved remarkable progress. As a complementary aspect to code generation, test case generation…