most citedToward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy

1 citations · 1 across the 3 of their papers we have counts for

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6 papers

cs.SE20261 cited

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…