6 papers
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…
Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy
Hamed Taherkhani, Jiho Shin, Muhammad Ammar Tahir +3
Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. 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…
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…