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A Cybersecurity Maturity Model for Digitally Transformed Organizations 数字化转型组织的网络安全成熟度模型
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-12-07 DOI: 10.1002/smr.70074
Malek Al Zeibak, Mohammad Alshayeb, Malak Baslyman, Mahmood Niazi

Cybersecurity is a critical component of digital transformation, as the two must be integrated to enable business modernization and the adoption of innovative technologies while ensuring system security. Modern technologies introduce increasingly complex threats as more data is stored, transmitted, and processed across systems. Threats include the violation or misuse of data, such as data corruption, data theft, or a privacy violation. Determining if an organization applies the best practices requires measurements using maturity models. Several maturity models with different goals and qualities have been developed. However, no cybersecurity maturity model is related to digitally enabled or transforming organizations. The objective of this study is to develop a model that measures cybersecurity maturity for digitally transformed organizations. The model aims to provide a customizable assessment method for organizations of different sizes and domains. Two multivocal literature reviews (MLRs) were conducted to identify the available maturity models and best practices from formal and grey literature. After analyzing 165 studies, seven cybersecurity categories, as the capability areas, and 22 practice areas, were identified. Moreover, an assessment tool that supports self-assessment was developed. Finally, the model was evaluated through five case studies and expert judgments. As a result, a cybersecurity maturity model for digitally transformed organizations is developed, along with its assessment methodology and automation tool. The model evaluation showed promising results in terms of the ability to identify the maturity level.

网络安全是数字化转型的关键组成部分,必须将两者结合起来,以实现业务现代化和采用创新技术,同时确保系统安全。随着越来越多的数据跨系统存储、传输和处理,现代技术带来了越来越复杂的威胁。威胁包括违反或滥用数据,例如数据损坏、数据盗窃或侵犯隐私。确定组织是否应用了最佳实践需要使用成熟度模型进行度量。已经开发了几个具有不同目标和质量的成熟度模型。然而,没有网络安全成熟度模型与数字化或转型组织相关。本研究的目的是开发一个模型来衡量数字化转型组织的网络安全成熟度。该模型旨在为不同规模和领域的组织提供一种可定制的评估方法。进行了两次多声音文献综述(MLRs),以从正式文献和灰色文献中确定可用的成熟度模型和最佳实践。在分析了165项研究后,确定了7个网络安全类别,作为能力领域和22个实践领域。此外,还开发了支持自我评估的评估工具。最后,通过五个案例分析和专家判断对模型进行了评价。因此,为数字化转型的组织开发了网络安全成熟度模型,以及其评估方法和自动化工具。在识别成熟度级别的能力方面,模型评估显示了有希望的结果。
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引用次数: 0
A Mapping Study on JavaScript Quality Attributes and Metrics JavaScript质量属性和度量的映射研究
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-12-05 DOI: 10.1002/smr.70073
Ioannis Zozas, Stamatia Bibi, Apostolos Ampatzoglou, Elvira-Maria Arvanitou, Pantelis Angelidis, Markos Tsipouras

Although JavaScript dominates modern software development, research on its quality attributes remains scarce, despite the fundamental differences that distinguish it from other languages. This motivates dedicated research related to JavaScript quality attributes and metrics. This paper aims to identify (a) the quality attributes of the JavaScript language that are mainly studied and (b) the quality metrics that are used to quantify them. Additionally, the paper provides information on the tools that can be used to measure quality metrics. To achieve these goals, we have conducted a mapping study on seven journals and eight conferences of high quality. A total of 142 primary studies, published between 2002 and February 2025, have been selected and analyzed, to identify and classify software metrics to high-level quality attributes, as described in ISO/IEC 25010:2011. Maintainability, Security, Reliability, and Usability quality attributes are the most studied ones. Furthermore, 78 generic and 48 JavaScript-specific metrics were identified. A wide dispersion of metrics has been identified for assessing each quality attribute, based on different development tasks. Moreover, a variety of tools and benchmarks were identified. A clear research trend in JavaScript quality assessment related to issues that involve software reuse, code testing, and dynamic code analysis has been identified. Yet differences among primary studies in quality assessment and quantification, along with tool adoption indicate the need for further exploration of these recurring topics.

尽管JavaScript主导着现代软件开发,但对其质量属性的研究仍然很少,尽管它与其他语言有根本的区别。这激发了与JavaScript质量属性和度量相关的专门研究。本文旨在确定(a)主要研究的JavaScript语言的质量属性和(b)用于量化它们的质量度量。此外,本文还提供了关于可用于度量质量度量的工具的信息。为了实现这些目标,我们对7种高质量期刊和8个高质量会议进行了地图研究。在2002年至2025年2月之间发表的总共142项主要研究已被选择和分析,以识别和分类软件度量标准到高级质量属性,如ISO/IEC 25010:2011中所述。可维护性、安全性、可靠性和可用性是研究最多的质量属性。此外,还确定了78个通用指标和48个javascript特定指标。基于不同的开发任务,已经确定了用于评估每个质量属性的广泛分散的度量标准。此外,还确定了各种工具和基准。JavaScript质量评估的一个明确的研究趋势是涉及软件重用、代码测试和动态代码分析的问题。然而,在质量评估和量化方面的主要研究之间的差异,以及工具的采用表明需要进一步探索这些反复出现的主题。
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引用次数: 0
A Multilevel Approach to TARA: Proposals for Attack Feasibility in Interference-Free Scenarios TARA的多层方法:无干扰情况下攻击可行性的建议
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-12-03 DOI: 10.1002/smr.70060
Thomas Liedtke, Richard Messnarz, Damjan Ekert, Alexander Much

Automotive SPICE for Cybersecurity incorporates the Cybersecurity Risk Management process (MAN.7), aligning with the Risk Assessment methods defined in ISO/SAE 21434:2021 (Clause 15). Both standards provide guidance on conducting Threat Analysis and Risk Assessments (TARA). However, they do not specify how to integrate the determination of attack feasibility when multiple TARAs emerge across different development phases. This paper explores how the concept of freedom from interference can facilitate a unified approach to determining attack feasibility in such scenarios.

汽车网络安全SPICE纳入了网络安全风险管理流程(MAN.7),与ISO/SAE 21434:2021(条款15)中定义的风险评估方法保持一致。这两个标准都提供了进行威胁分析和风险评估(TARA)的指导。然而,它们没有指定当多个tara出现在不同的开发阶段时,如何集成攻击可行性的确定。本文探讨了免受干扰的概念如何促进在这种情况下确定攻击可行性的统一方法。
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引用次数: 0
Software Asset Management Roles and Responsibilities for Projects Using AHP and WASPAS 使用AHP和WASPAS的项目的软件资产管理角色和职责
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-11-30 DOI: 10.1002/smr.70070
Runhan Zhang, Shah Nazir

Effective management of software assets in their whole lifespan is the main goal of software asset management (SAM) and is a contemporary organizational practice. It includes a range of tasks such as purchasing, implementing, and maintaining software inside a company. SAM seeks to minimize the risks and expenses related to software ownership while ensuring that software resources are used as efficiently as possible to support business activities. Recently, a great increase in the use of this technique has occurred, especially in large-scale enterprises where the complexity and diversity of software assets have faced major hurdles. The proposed study presents an overview of the analysis of the recent approaches and hurdles in the area of SAM. Because big software companies have access to a multitude of resources and experience, maximizing the reuse of software assets inside these organizations is a common topic of academic and industrial study. Through the integration of several important attributes from previous research endeavors, the current study seeks to determine the most common attributes for the research. The study aims to contribute to the area by integrating the Analytical Hierarchy Process (AHP) along with the Weighted Aggregated Sum Product Assessment (WASPAS) approaches to give a rigorous and systematic way to analyze and rate the prominent qualities for selection of the most appropriate choice among the available alternatives.

在软件资产的整个生命周期中对其进行有效的管理是软件资产管理(SAM)的主要目标,也是一种当代的组织实践。它包括公司内部的一系列任务,如购买、实现和维护软件。SAM寻求最小化与软件所有权相关的风险和费用,同时确保尽可能有效地使用软件资源来支持业务活动。最近,这种技术的使用有了很大的增长,特别是在软件资产的复杂性和多样性面临主要障碍的大型企业中。拟议的研究概述了SAM领域最近的方法和障碍的分析。由于大型软件公司可以访问大量的资源和经验,因此在这些组织中最大化软件资产的重用是学术和工业研究的一个常见主题。通过整合以往研究的几个重要属性,本研究试图确定研究中最常见的属性。该研究旨在通过整合层次分析法(AHP)和加权总和产品评估(WASPAS)方法,为该领域做出贡献,以提供一种严格和系统的方法来分析和评估突出的品质,以便在可用的替代方案中选择最合适的选择。
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引用次数: 0
Enhancing OCR-based Engineering Diagram Analysis by Integrating Diverse External Legends with VLMs 集成多种外部图例与vlm,增强基于ocr的工程图分析
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-11-30 DOI: 10.1002/smr.70072
Vasil Shteriyanov, Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson

Manual analysis of diagrams and legend sheets in engineering projects is time consuming and needs automation. The lack of standardized legend formats complicates creating a general method for automated information extraction. Existing approaches require training and custom rules for each project. This study proposes a novel solution combining optical character recognition with vision language models and multimodal prompt engineering to automate information extraction from diverse legend sheets without training. It integrates legend information with information extracted from diagrams, unlike studies that only focus on diagrams. Our study shows that VLMs, guided by multimodal prompts, can accurately extract information from diverse legend sheets, enabling automatic information extraction in diagrams across engineering projects. We validate our method through a case study involving the extraction of instruments from piping and instrumentation diagrams (P&IDs) and their legends across three projects with varied formats and standards. The proposed method achieved 100% accuracy in legend classification and information extraction, and 99.68% precision and 95.91% recall in generating instrument listings. The results demonstrate the effectiveness of our approach, significantly enhancing the accuracy and efficiency of information extraction from diagrams. This method can be adapted to different legend formats and diagrams, providing a versatile solution for various industries.

在工程项目中手工分析图表和图例表是费时的,需要自动化。标准化图例格式的缺乏使创建用于自动信息提取的通用方法变得复杂。现有的方法需要针对每个项目进行培训和定制规则。本研究提出了一种将光学字符识别与视觉语言模型和多模态提示工程相结合的解决方案,可以在不需要训练的情况下自动从各种图例表中提取信息。它将图例信息与从图中提取的信息集成在一起,这与只关注图的研究不同。我们的研究表明,vlm在多模式提示的引导下,可以准确地从不同的图例表中提取信息,从而实现跨工程项目图中的自动信息提取。我们通过一个案例研究验证了我们的方法,该案例涉及从管道和仪表图(P& id)及其图例中提取仪器,涉及三个不同格式和标准的项目。该方法在图例分类和信息提取方面达到100%的准确率,在仪器清单生成方面达到99.68%的准确率和95.91%的召回率。结果证明了该方法的有效性,显著提高了从图中提取信息的准确性和效率。这种方法可以适应不同的图例格式和图表,为不同的行业提供了一个通用的解决方案。
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引用次数: 0
Enhancing Task Prioritization in Software Development Issues Tracking System 在软件开发问题跟踪系统中提高任务优先级
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-11-30 DOI: 10.1002/smr.70068
Karthik Shivashankar, Kristian Marison Haugerud, Antonio Martini

Modern software development faces a critical bottleneck in manually prioritizing the overwhelming volume of issues generated in platforms like Jira and GitHub. This labor-intensive process leads to delays, increased costs, inconsistent handling, and developer burnout, worsened by the lack of standardized priority labels. This paper investigates the potential of automated issue priority classification using state-of-the-art Transformer models to alleviate this burden. We evaluate the performance of models like BERT, DeBERTa, and ModernBERT, comparing them against general large language models (LLMs) such as GPT-3.5, Qwen2.5-3B and Llama-3.2-3B, using curated datasets derived from public Jira and GitHub repositories. Our research addresses the effectiveness of these models for their generalization capabilities on out-of-distribution projects, the impact of fine-tuning, and performs a detailed performance comparison across different priority levels and model types. Results demonstrate that Transformer models, particularly ModernBERT, achieve high classification performance (e.g., accuracy > 81%), significantly outperforming the evaluated general LLMs (accuracy $$ approx $$ 75%) for this specific task. We find that binary classification is more effective than multilabel approaches, models generalize well to unseen projects, and performance is further enhanced by fine-tuning. Key contributions include the provision of cleaned, labeled datasets and a comprehensive evaluation confirming the viability and benefits of using specialized Transformer models for automated issue priority suggestion, offering a path to improved efficiency and resource allocation in software development workflows.

现代软件开发面临着一个关键的瓶颈,即手动确定Jira和GitHub等平台上产生的大量问题的优先级。这种劳动密集型的过程导致了延迟、成本增加、不一致的处理和开发人员的倦怠,并且由于缺乏标准化的优先级标签而恶化。本文研究了使用最先进的Transformer模型来减轻这一负担的自动化问题优先级分类的潜力。我们评估了BERT、DeBERTa和ModernBERT等模型的性能,将它们与GPT-3.5、Qwen2.5-3B和Llama-3.2-3B等通用大型语言模型(llm)进行了比较,使用了来自公共Jira和GitHub存储库的精选数据集。我们的研究解决了这些模型在非分布项目上的泛化能力的有效性,微调的影响,并在不同优先级和模型类型之间进行了详细的性能比较。结果表明,Transformer模型,特别是ModernBERT模型,实现了很高的分类性能(例如,准确率&gt; 81)%), significantly outperforming the evaluated general LLMs (accuracy ≈ $$ approx $$ 75%) for this specific task. We find that binary classification is more effective than multilabel approaches, models generalize well to unseen projects, and performance is further enhanced by fine-tuning. Key contributions include the provision of cleaned, labeled datasets and a comprehensive evaluation confirming the viability and benefits of using specialized Transformer models for automated issue priority suggestion, offering a path to improved efficiency and resource allocation in software development workflows.
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引用次数: 0
A Novel, Tool-Supported Catalog of Community Smell Symptoms 一个新颖的,工具支持的社区气味症状目录
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-11-21 DOI: 10.1002/smr.70067
Antonio Della Porta, Stefano Lambiase, Gemma Catolino, Filomena Ferrucci, Fabio Palomba

Software development is a multifaceted endeavor, requiring a profound grasp of both social dynamics and technical intricacies. Poor collaboration often leads to the accumulation of social debt, manifesting as unforeseen project costs due to sub-optimal team interactions. Community smells have emerged as indicators of these socio-technical inefficiencies and potential social debt. While previous research has focused on automated detection of community smells through analyzing developer communication patterns, our study offers a complementary approach. We emphasize the critical role of project managers in assessing socio-technical dynamics and propose a novel, tool-supported catalog of symptoms. This catalog can be used for manual inspections to identify early signs of community smells at the individual level, allowing managers to address issues before they escalate. Using a mixed-method design that leveraged an existing literature review and a user survey, we cataloged symptoms related to four community smell types. Additionally, we developed TOAST, a tool that operationalizes this catalog, and assessed its usability and practical usefulness through an experiment involving project managers. The study showed that even participants unfamiliar with the term “community smells” were able to interpret the tool's output, reflect on team dynamics, and recognize problematic behavioral patterns when supported by structured symptom-based information. The paper concludes by shedding light on the potential impact of our work and its contribution to advancing the detection and analysis of community smells.

软件开发是一项多方面的工作,需要对社会动态和技术复杂性有深刻的把握。糟糕的协作通常会导致社会债务的积累,表现为由于次优团队交互而导致的不可预见的项目成本。社区气味已经成为这些社会技术效率低下和潜在社会债务的指标。虽然以前的研究主要集中在通过分析开发人员通信模式来自动检测社区气味,但我们的研究提供了一种补充方法。我们强调项目经理在评估社会技术动态中的关键作用,并提出了一种新的、工具支持的症状目录。该目录可用于手动检查,以识别个人级别上社区气味的早期迹象,从而允许管理人员在问题升级之前解决问题。使用混合方法设计,利用现有文献综述和用户调查,我们编目了与四种社区气味类型相关的症状。此外,我们开发了TOAST,这是一个操作该目录的工具,并通过涉及项目经理的实验评估了它的可用性和实用性。研究表明,即使是不熟悉术语“社区气味”的参与者也能够解释工具的输出,反映团队动态,并在基于症状的结构化信息的支持下识别出有问题的行为模式。论文最后阐明了我们工作的潜在影响及其对推进社区气味检测和分析的贡献。
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引用次数: 0
How Far Is Machine Learning From the Detection of Complex Microservice Bad Smells? 机器学习离检测复杂微服务的不良气味还有多远?
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-11-20 DOI: 10.1002/smr.70064
Yongchao Xing, Weipan Yang, Yiming Lv, Dianhui Chu, Zhiying Tu

Microservice bad smells, arising from poor design and development practices, can severely degrade system quality if unaddressed. While rule-based detection methods exist, their applicability is limited by subjective metric thresholds and the difficulty in defining certain bad smells, particularly complex microservice bad smells that are challenging to express through rules or involve high subjectivity. These smells often involve multiple services or manifest across multiple layers within a service, making them particularly challenging to detect using traditional methods. Without efficient and accurate detection mechanisms, the self-healing capabilities of microservices during operation and continuous evolution will also be compromised. Given the promise of machine learning in code smell detection, this study empirically evaluates its performance in detecting complex microservice bad smells. We employ two sampling techniques and eight classification models on 1180 samples from 55 systems, generating 45 detection models and identifying top classifiers for seven complex microservice bad smell types. We compare machine learning with rule-based methods for high-subjectivity smells, analyze performance gaps, and propose a MAPE-K-based conceptual framework for runtime detection and refactoring. Finally, we discuss the necessity for future research.

由于糟糕的设计和开发实践而产生的微服务不良气味,如果不加以处理,可能会严重降低系统质量。虽然存在基于规则的检测方法,但它们的适用性受到主观度量阈值和定义某些不良气味的困难的限制,特别是复杂的微服务不良气味,难以通过规则表达或涉及高度主观性。这些气味通常涉及多个服务或在服务中的多个层中表现出来,这使得使用传统方法检测它们特别具有挑战性。如果没有高效、准确的检测机制,微服务在运行和持续演化过程中的自愈能力也将受到影响。鉴于机器学习在代码气味检测中的前景,本研究对其在检测复杂微服务不良气味方面的性能进行了实证评估。我们采用了两种采样技术和8种分类模型,对来自55个系统的1180个样本进行了分析,生成了45种检测模型,并确定了7种复杂微服务臭味类型的顶级分类器。我们将机器学习与基于规则的高主观性气味方法进行比较,分析性能差距,并提出基于mape的运行时检测和重构概念框架。最后,讨论了未来研究的必要性。
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引用次数: 0
Minimizing Inter-Dependencies in Functional Requirements for Timely Delivery of Software Projects: A Prioritization Approach Using AHP and Spanning Trees 最小化软件项目及时交付的功能需求中的相互依赖:使用AHP和生成树的优先级方法
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-11-18 DOI: 10.1002/smr.70063
Muhammad Yaseen, Anum Bahar, Gohar Rahman, Mirjalol Ashurov, Erkin Kholiyarov

Requirements prioritization provides a structured way to rank and sequence requirements, which is particularly important in large-scale ERP systems where development tasks are distributed among multiple teams. Prerequisite requirements often depend on one another and must be implemented in a specific order. Improper handling of these dependencies can delay project timelines, yet limited research addresses this challenge. This study aims to develop a systematic approach to prioritize requirements in order to minimize dependencies and improve the timely completion of the project. The Analytical Hierarchical Process (AHP) combined with spanning tree methodology was applied to analyze requirement dependencies. In addition, the NA technique was used to classify prioritized requirements into distinct categories. ODOO ERP requirements served as the case study for evaluation. The proposed methodology produced a prioritized list of requirements grouped into categories, which significantly reduced inter-dependencies and improved the organization of requirements. Minimizing requirement dependencies through structured prioritization enhances the reliability and timely completion of software development projects. In the ODOO ERP case study, the suggested approach reduced 90% of dependencies. Priority grouping showed that the top 25 requirements eliminated 90% of dependencies, while the top 20 and 15 removed 82% and 67% respectively. This reduction lowered the projected project delay rate from about 25% to under 5%, confirming the approach's practical effectiveness and scalability for large ERP projects.

需求优先级提供了一种结构化的方式来对需求进行排序和排序,这在开发任务分布在多个团队中的大型ERP系统中尤为重要。先决条件要求通常是相互依赖的,并且必须按照特定的顺序实现。对这些依赖关系的不当处理可能会延迟项目的进度,然而针对这一挑战的研究有限。本研究旨在开发一种系统的方法来确定需求的优先级,以最大限度地减少依赖并提高项目的及时完成。将层次分析法(AHP)与生成树方法相结合,应用于需求依赖分析。此外,NA技术用于将优先级需求划分为不同的类别。ODOO ERP需求作为评估的案例研究。所建议的方法产生了按类别分组的需求的优先级列表,这大大减少了相互依赖并改进了需求的组织。通过结构化的优先级最小化需求依赖,增强了软件开发项目的可靠性和及时完成。在ODOO ERP案例研究中,建议的方法减少了90%的依赖性。优先级分组显示,前25个需求消除了90%的依赖,而前20个和前15个需求分别消除了82%和67%的依赖。这一减少将预计的项目延迟率从大约25%降低到5%以下,确认了该方法在大型ERP项目中的实际有效性和可扩展性。
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引用次数: 0
Generating Quality Assurance Constraints From Natural Language With LLMs 用法学硕士从自然语言生成质量保证约束
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2025-11-17 DOI: 10.1002/smr.70062
Christoph Mayr-Dorn, Anmol Bilal, Cosmina Ratiu, Alexander Egyed

This paper addresses the challenge of automating process-centric quality assurance (QA) in safety-critical domains, where compliance with regulations is crucial. Currently, QA engineers manually check compliance using tedious methods like browsing engineering artifacts and ad-hoc scripts. Automated support could improve efficiency, but it requires constraints to be written in structured, executable forms (e.g., in the Object Constraint Language, OCL), whereas engineers prefer natural language. To bridge this gap, we propose the use of large language models (LLMs) to generate OCL from natural language, enhanced by schema-based prompting and domain-specific language (DSL)–based repairs. Unlike prior work focused on UML models, this work applies OCL to software process QA. Evaluating six LLMs, we find o1-mini and Codestral perform best, with our automatic repairs ensuring constraint executability for 22%–44% of an LLM's generated OCL constraints that would otherwise remain nonexecutable due to errors.

本文讨论了在安全关键领域中自动化以过程为中心的质量保证(QA)的挑战,在这些领域中遵守法规是至关重要的。目前,QA工程师使用浏览工程工件和临时脚本等繁琐的方法手动检查遵从性。自动化支持可以提高效率,但是它要求约束以结构化的、可执行的形式编写(例如,用对象约束语言,OCL),而工程师更喜欢自然语言。为了弥补这一差距,我们建议使用大型语言模型(llm)从自然语言生成OCL,并通过基于模式的提示和基于领域特定语言(DSL)的修复进行增强。不像以前的工作集中在UML模型上,这项工作将OCL应用于软件过程QA。在评估6个LLM时,我们发现01 -mini和Codestral表现最好,我们的自动修复确保了22%-44%的LLM生成的OCL约束的约束可执行性,否则这些约束将由于错误而无法执行。
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引用次数: 0
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