A Road-Map for Transferring Software Engineering methods for Model-Based Early V&V of Behaviour to Systems Engineering
Johan Cederbladh, Antonio Cicchetti
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9 pages, 2 figures, SE2030, Software Engineering in 2030 Workshop, ACM International Conference on the Foundations of Software Engineering (FSE) 2024, Porto de Galinhas, Brazil
In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.
Context: The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. Goal: To present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems. Method: An iterative approach based on experimental studies to acquire evidence to define the IoT Roadmap. Next, the Systems Engineering Body of Knowledge life cycle was used to or…
The Roadmap is performed in the context of a Ph.D. research in collaboration between the Experimental Software Engineering Group, from the Systems Engineering and Computing Program of the Federal University of Rio de Janeiro (COPPE/UFRJ) and the Laboratory of Industrial and Human Automation Control, Mechanical engineering and Computer Science (LAMIH UMR CNRS 8201) in the Universitè Polytechnique Hauts-de-France (UPHF). The Roadmap resulted from an investigation on the particularities of IoT applications. It is the concrete organization of the concepts and evidence gathered from different exper…
Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and frameworks for responsible AI have been issued recently. However, they are high level and difficult to put into practice. On the other hand, most AI researchers focus on algorithmic solutions, while the responsible AI challenges actually crosscut the entire engineering lifecycle and components of AI systems. To close the gap in operationalizing responsible AI, this paper aims to develop a roadmap on software engineering for resp…
Integrating LLMs in Software Engineering Education: Motivators, Demotivators, and a Roadmap Towards a Framework for Finnish Higher Education Institutes
This paper reports on a study of transferring a desktop-based model-based engineering tool to a web application. The study has been conducted in the WEBMODEL project where the well-established technology stack around the Eclipse platform and the Eclipse Modeling Framework was lifted into a cloud-based environment. As results, a modeling language independent tooling kernel for web-based modeling tools and a minimal prototypical web-based implementation of the AutoFOCUS 3 model-based engineering tool are presented. Furthermore, the report documents experiences and implementation advises gained d…
Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
Simula Research Laboratory（Simula研究实验室）；National Institute of Informatics（日本信息机构）；Kristiania University of Applied Sciences and Oslo Metropolitan University（克里斯蒂安尼亚应用科学大学和奥斯陆 Metropolitan 大学）
A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI
University of Naples "Federico II" Napoli Italy；Ruhr Institute for Software Technology (paluno), University of Duisburg-Essen Essen Germany；Karlsruhe Institute of Technology (KIT) Karlsruhe Germany；Gran Sasso Science Institute (GSSI) L'Aquila Italy；University of Naples "Federico II"；Ruhr Institute for Software Technology (paluno), University of Duisburg-Essen；Karlsruhe Institute of Technology (KIT)；Gran Sasso Science Institute (GSSI)
Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-preformed studies and well-desigeworks in various research domains, including software requirements, software design and development, testing and debugging and software maintenance. This paper is a first attempt to systematically organize knowledge in this area by surveying a body of 139 papers on predictive models. We describe the key models and approaches used, classify th…
Background. Software Engineering (SE) researchers extensively perform experiments with human subjects. Well-defined samples are required to ensure external validity. Samples are selected \textit{purposely} or by \textit{convenience}, limiting the generalizability of results. Objective. We aim to depict the current status of participants selection in empirical SE, identifying the main threats and how they are mitigated. We draft a robust approach to participants' selection. Method. We reviewed existing participants' selection guidelines in SE, and performed a preliminary literature review to fi…
The rapid growth of software supply chain attacks has attracted considerable attention to software bill of materials (SBOM). SBOMs are a crucial building block to ensure the transparency of software supply chains that helps improve software supply chain security. Although there are significant efforts from academia and industry to facilitate SBOM development, it is still unclear how practitioners perceive SBOMs and what are the challenges of adopting SBOMs in practice. Furthermore, existing SBOM-related studies tend to be ad-hoc and lack software engineering focuses. To bridge this gap, we con…
Context: In recent years, leveraging machine learning (ML) techniques has become one of the main solutions to tackle many software engineering (SE) tasks, in research studies (ML4SE). This has been achieved by utilizing state-of-the-art models that tend to be more complex and black-box, which is led to less explainable solutions that reduce trust and uptake of ML4SE solutions by professionals in the industry. Objective: One potential remedy is to offer explainable AI (XAI) methods to provide the missing explainability. In this paper, we aim to explore to what extent XAI has been studied in the…
With recent advances in the development of more powerful quantum computers, the re-search area of quantum software engineering is emerging. Quantum software plays a critical role in exploiting the full potential of quantum computing systems. As a result, it has been drawing increasing attention recently to provide concepts, principles, and guidelines to address the ongoing challenges of quantum software development. The importance of the topic motivated us to voice out a call for action to develop a readiness model that will help an organization assess its capability of migration from classic …
University of California, Davis（加州大学戴维斯分校）；University of Virginia（弗吉尼亚大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；William & Mary（威廉玛丽学院）；Columbia University（哥伦比亚大学）；Google（谷歌公司）；Purdue University（普渡大学）
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was conducted in co-location with the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19) in San Diego, California. The goal of this workshop was to outline high priority areas for cross-cutting research. While a multitude of exciting directions for future work were identified, this report provides a general summary of the research areas representing the areas of highest priority which were di…
Washington & Lee University（华盛顿与李大学）；William & Mary（威廉与玛丽学院）；George Mason University（乔治梅森大学）
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this crosscutting area of work, from its modern inception to…
