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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…
Consistency Meets Verification: Enhancing Test Generation Quality in Large Language Models Without Ground-Truth Solutions
Hamed Taherkhani, Alireza DaghighFarsoodeh, Mohammad Chowdhury +2
Large Language Models (LLMs) have significantly advanced automated test generation, yet existing methods often rely on ground-truth code for verification, risking bug propagation a…
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…
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…
Task-oriented Prompt Enhancement via Script Generation
Chung-Yu Wang, Alireza DaghighFarsoodeh, Hung Viet Pham
Large Language Models (LLMs) have demonstrated remarkable abilities across various tasks, leveraging advanced reasoning. Yet, they struggle with task-oriented prompts due to a lack…
Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity
Chung-Yu Wang, Alireza DaghighFarsoodeh, Hung Viet Pham
Large Language Models (LLMs) have demonstrated impressive performance in software engineering tasks. However, improving their accuracy in generating correct and reliable code remai…