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20232025
most citedPrompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

24 citations · 37 across the 7 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.SE2024

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…

cs.SE2024

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…

cs.SE2024

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…

cs.SE2024

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.SE2024★ 1 cited

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,…