1 citations · 2 across the 6 of their papers we have counts for
6 papers
Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark
Hamed Taherkhani, Mohammad Abdollahi, Melika Sepidband +3
Large language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear. Existing repository-level QA benchmarks mainly…
On the Role of Fault Localization Context for LLM-Based Program Repair
Melika Sepidband, Hung Viet Pham, Hadi Hemmati
Fault Localization (FL) is a key component of Large Language Model (LLM)-based Automated Program Repair (APR), yet its impact remains underexplored. In particular, it is unclear ho…
RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models
Melika Sepidband, Hamed Taherkhani, Hung Viet Pham +1
Fault Localization (FL) is a critical step in Automated Program Repair (APR), and its importance has increased with the rise of Large Language Model (LLM)-based repair agents. In r…
Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach
Melika Sepidband, Hamed Taherkhani, Song Wang +1
Automatic code generation has gained significant momentum with the advent of Large Language Models (LLMs) such as GPT-4. Although many studies focus on improving the effectiveness…
Deep-Bench: Deep Learning Benchmark Dataset for Code Generation
Alireza Daghighfarsoodeh, Chung-Yu Wang, Hamed Taherkhani +4
Deep learning (DL) has revolutionized areas such as computer vision, natural language processing, and more. However, developing DL systems is challenging due to the complexity of D…
Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm
Hamed Taherkhani, Melika Sepindband, Hung Viet Pham +2
Large Language Models have seen increasing use in various software development tasks, especially in code generation. The most advanced recent methods attempt to incorporate feedbac…