Towards standarized benchmarks of LLMs in software modeling tasks: a conceptual framework

IF 2 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software and Systems Modeling Pub Date : 2024-09-03 DOI:10.1007/s10270-024-01206-9
Javier Cámara, Lola Burgueño, Javier Troya
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Abstract

The integration of Large Language Models (LLMs) in software modeling tasks presents both opportunities and challenges. This Expert Voice addresses a significant gap in the evaluation of these models, advocating for the need for standardized benchmarking frameworks. Recognizing the potential variability in prompt strategies, LLM outputs, and solution space, we propose a conceptual framework to assess their quality in software model generation. This framework aims to pave the way for standardization of the benchmarking process, ensuring consistent and objective evaluation of LLMs in software modeling. Our conceptual framework is illustrated using UML class diagrams as a running example.

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软件建模任务中的 LLM 标准化基准:概念框架
在软件建模任务中集成大型语言模型(LLM)既是机遇也是挑战。这篇专家之声探讨了这些模型评估中的一个重要空白,主张需要标准化的基准框架。认识到提示策略、LLM 输出和解决方案空间的潜在可变性,我们提出了一个概念框架来评估它们在软件模型生成中的质量。该框架旨在为基准流程的标准化铺平道路,确保对软件建模中的 LLM 进行一致、客观的评估。我们将以 UML 类图为例说明我们的概念框架。
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来源期刊
Software and Systems Modeling
Software and Systems Modeling 工程技术-计算机:软件工程
CiteScore
6.00
自引率
20.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns: Domain-specific models and modeling standards; Model-based testing techniques; Model-based simulation techniques; Formal syntax and semantics of modeling languages such as the UML; Rigorous model-based analysis; Model composition, refinement and transformation; Software Language Engineering; Modeling Languages in Science and Engineering; Language Adaptation and Composition; Metamodeling techniques; Measuring quality of models and languages; Ontological approaches to model engineering; Generating test and code artifacts from models; Model synthesis; Methodology; Model development tool environments; Modeling Cyberphysical Systems; Data intensive modeling; Derivation of explicit models from data; Case studies and experience reports with significant modeling lessons learned; Comparative analyses of modeling languages and techniques; Scientific assessment of modeling practices
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