12 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair
Rabeya Khatun Muna, Muhammad Ahasanuzzaman, Nakhla Rafi +3
Large language model (LLM) agents increasingly reuse prior experience, but most approaches assume that problems and solutions are already aligned. Software histories rarely provide…
Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair
S M Farah Al Fahim, Md Nakhla Rafi, Md Ahasanuzzaman +5
Bug reports serve as task specifications for repository-level automated program repair (APR) agents, but they often describe only the observed failure and omit repair-relevant info…
FALAT: Tracing Failures in LLM Agent Trajectories via Dependency-Guided Search
Md Nakhla Rafi, Md Ahasanuzzaman, Dong Jae Kim +2
LLM-based agents increasingly solve complex tasks through long trajectories involving reasoning steps, tool calls, and inter-agent communication. However, when these agents fail, i…
Assessing and Improving the Representativeness of Code Generation Benchmarks Using Knowledge Units (KUs) of Programming Languages -- An Empirical Study
Md Ahasanuzzaman, Bram Adams, Emad Fallahzadeh +2
Large Language Models (LLMs) such as GPT-4, Claude and LLaMA have shown impressive performance in code generation, typically evaluated using benchmarks (e.g., HumanEval). However,…