24 citations · 37 across the 7 of their papers we have counts for
7 papers · 1 filter
Demystifying Errors in LLM Reasoning Traces: An Empirical Study of Code Execution Simulation
Mohammad Abdollahi, Khandaker Rifah Tasnia, Soumit Kanti Saha +3
Understanding a program's runtime reasoning behavior, meaning how intermediate states and control flows lead to final execution results, is essential for reliable code generation,…
Automatic Instantiation of Assurance Cases from Patterns Using Large Language Models
Oluwafemi Odu, Alvine B. Belle, Song Wang +3
An assurance case is a structured set of arguments supported by evidence, demonstrating that a system's non-functional requirements (e.g., safety, security, reliability) have been…
Program Slicing in the Era of Large Language Models
Kimya Khakzad Shahandashti, Mohammad Mahdi Mohajer, Alvine Boaye Belle +2
Program slicing is a critical technique in software engineering, enabling developers to isolate relevant portions of code for tasks such as bug detection, code comprehension, and d…
Retrieval-Augmented Test Generation: How Far Are We?
Jiho Shin, Nima Shiri Harzevili, Reem Aleithan +2
Retrieval Augmented Generation (RAG) has advanced software engineering tasks but remains underexplored in unit test generation. To bridge this gap, we investigate the efficacy of R…
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…
I came, I saw, I certified: some perspectives on the safety assurance of cyber-physical systems
Mithila Sivakumar, Alvine B. Belle, Kimya Khakzad Shahandashti +5
The execution failure of cyber-physical systems (e.g., autonomous driving systems, unmanned aerial systems, and robotic systems) could result in the loss of life, severe injuries,…
Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Jiho Shin, Clark Tang, Tahmineh Mohati +3
The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prom…