Towards Social Network Based Approach for Software Engineering Ontology Sharing and Evolution

The ongoing trend towards multi-site software development not only brings the benefits but also creates additional challenges regarding remote communication and coordination. The Software Engineering Ontology (SE Ontology) was first developed to clarify the software engineering concepts and project information, and to enable knowledge sharing among dispersed teams. However, the current SE Ontology has the same passive structure as other ontologies that exist on the Web. Passive structure refers to a need to know exactly the concepts and relationships to which users are referring in the ontology. Otherwise, he/she may not be able to obtain the knowledge required. What is needed is active support that can help users find the information they need and provide them with meaningful output. In this paper, we propose an active Software Engineering Ontology through Multi-Agent System (SEOMAS) framework which is intended to provide active support to access software engineering domain knowledge and to recommend software project information captured in the SE Ontology. The UML and Agent UML are used to formalise the SEOMAS framework and to model interactions between the various agents. The prototype is developed and evaluated using several scenarios of the online shopping system development as a case study.

Challenges over multi-site software development are on working in virtual teams and sharing knowledge. It is quite normal that software engineers working in a virtual team have never met face to face in multi-site environments. In addition they have different educational backgrounds and interpret methods in different ways. Software engineering education, training, and practice are different between universities, cities, and countries. As a result, it is difficult to share a piece of knowledge between distributed teams and among remote team members. There are a number of standards that different teams could be referring to. Remote software engineers use a particular standard as their own individual guide and when they share their own knowledge base and terminology is different from those of others. Most issues raised are related to inconsistency in understanding software engineering theories and practice. Therefore sharing knowledge is the challenge and to resolve the differences between the distributed teams we need to understand its key variables of knowledge sharing. In this paper we propose an ontology-based approach for knowledge sharing measurement. Particularly in the approach, we look into measurement of transferability and complexity of knowledge. The impact of nature of knowledge on knowledge sharing is focused. A prototype is developed taking Software Engineering Ontology as example.KeywordsOntologyKnowledge Sharing MeasurementTransferability of KnowledgeComplexity of KnowledgeMulti-site Software DevelopmentSoftware Engineering

Analytical quality assurance, especially testing, is an integral part of software-intensive system development. With the increased usage of Artificial Intelligence (AI) and Machine Learning (ML) as part of such systems, this becomes more difficult as well-understood software testing approaches cannot be applied directly to the AI-enabled parts of the system. The required adaptation of classical testing approaches and the development of new concepts for AI would benefit from a deeper understanding and exchange between AI and software engineering experts. We see the different terminologies used in the two communities as a major obstacle on this way. As we consider a mutual understanding of the testing terminology a key, this paper contributes a mapping between the most important concepts from classical software testing and AI testing. In the mapping, we highlight differences in the relevance and naming of the mapped concepts.

This panel gives a non-standard view of the future of software engineering. Two of the speakers are recent Ph.D. graduates in computer science, with expertise in software engineering, who have taken academic positions; as people who will educate the next generation of software engineering practitioners and researchers, they provide a key vision of the future. The other two speakers are senior, having moved from the research community into a world in which they face the problems of engineering software on a daily basis. Collectively, along with interactions from the audience, these two often underrepresented perspectives provide a sense of the key directions in which software engineering— practice, research, and education—should and must go.

Abstract. Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Software engineering project information is frequently evolving and queried to reflect project development changes in the software requirements or in the design process, to incorporate additional functionality to systems or to allow incremental improvement and the like. Therefore, the project information needs enhancement to ease up-to-date ontological information and to ease communication. Ontologies are widely used for capturing and organising knowledge of a particular domain of interest. We propose the use of software engineering ontology instantiations and enrichment to capture the software engineering project information

A compiler course with a long-term project is a staple of many undergraduate computer science curricula, and often a cornerstone of a program's applied-engineering component. Software engineering expertise can help a student complete such a course, yet that expertise is often lacking. This problem can be addressed without detracting from the core class materials by integrating a few simple software engineering practices into the course. A domain-specific, risk-driven approach minimizes overhead and reinforces the compiler's material, while treating the project as a "real world" enterprise reinforces key engineering lessons. The method might be called "syntax-directed software engineering," being driven by specification centered around a BNF-style grammar. Engineering lessons are reinforced with general engineering principles and contextualization of the subject matter. The approach can be taught without substantial software engineering background. The domain-specific risk-driven software engineering approach can be applied in other courses such as operating systems by redesigning the practices around its domain.

The design and implementation of wireless sensor network applications often require domain experts, who may lack expertise in software engineering, to produce resource-constrained, concurrent, real-time software without the support of high-level software engineering facilities. The Insense language aims to address this mismatch by allowing the complexities of synchronisation, memory management and event-driven programming to be borne by the language implementation rather than by the programmer. The main contribution of this paper is an initial step towards verifying the correctness of WSN applications with a focus on concurrency. We model part of the synchronisation mechanism of the Insense language implementation using Promela constructs and verify its correctness using Spin . We demonstrate how a previously published version of the mechanism is shown to be incorrect by Spin , and give complete verification results for the revised mechanism.

Forensic-ready software systems enhance the security posture by designing the systems prepared for potential investigation of incidents. Yet, the principal obstacle is defining their exact requirements, i.e., what they should implement. Such a requirement needs to be on-point and verifiable. However, what exactly comprises a forensic readiness requirement is not fully understood due to distinct fields of expertise in software engineering and digital forensics. This paper describes a forensic readiness qualitative factor reference model that enables the formulation of specific requirements for forensic-ready software systems. It organises the qualitative properties of forensic readiness into a taxonomy, which can then be used to formulate a verifiable requirement targeted at a specific quality. The model is then utilised in an automated valet parking service to define requirements addressing found inadequacies regarding a potential incident investigation.

In Engineering and Scientific computing, a significant number of software systems that are being commonly used by the Engineering community are developed and deployed as Open Source Systems (OSS). This collaborative development model has been widely accepted in both academia and industry. As these systems are typically developed by developers who are experts in their fields (e.g., Engineering), some of those programmers may be lacking an essential skillset today's software quality and engineering concepts (e.g., source code maintainability and analyzability, source code vulnerability, parallelization, etc.). An empirical study is presented that examines some of the source code quality aspects and programming practices (e.g., jumps statements, usage of recursion) from software engineering perspective in ten open source Engineering software systems. The studied systems are written in the C/C++ programming languages and comprised of over ten million lines of code, and present numerous tasks of the engineering community, including computer-aided design, computational fluid dynamics, and finite element analysis. Static analysis methods are applied to each system to determine the number of jump statements (Goto and Break statements) and functions that are recursively invoked. Functions that contain direct or indirect recursive calls are determined and counted. The results show that number of the used jump statements and functions that have recursive calls are significant and thus pose more of a roadblock to effectively and automatically maintain, comprehend, analyze, and re-engineer systems. Lastly, this leads to inefficiencies in a software engineer's ability to accomplish these common tasks. The study showed that issues in source code stemming from the lack of coding standards have increased over time and are prevalent.

Application lifecycle management (ALM) integrates the lifecycle workflows such as, requirements management, architecture, coding, testing, tracking, and release management. Process automation and seamless trace ability across the process are so important. But, different terminology and different model were used to implement the tool for specific workflow so that it is not possible to communicate between the tools. In this paper, we suggest new approach to achieve the seamless trace ability and process automation. For these features, knowledge formalization is critical. Evidence-based medicine is good reference architecture to ALM2. Software Engineering ontology is powerful solution to semantic gap between tools and guideline-based process execution environment can be practical solution to process automation. In this paper, we suggest the architecture for automating the ALM using process management technology and guideline-based engineering.

Artificial intelligence (AI) has revolutionized various industries, and its impact on the job market is significant. While AI has the potential to automate repetitive and time-consuming tasks, it also presents new job opportunities that require expertise in data analysis, software engineering, and machine learning. The paper provides an overview of the current state of the job market and the challenges it faces, and explains how AI is transforming the job market, including its impact on job creation and destruction, skill requirements, and wage distribution. The paper analyzes the job market trends from 2019 to 2023 based on the latest research and reports, and discusses the implications of AI for the workforce and policymakers. The paper concludes with some recommendations for future research and policy interventions. It discusses the benefits of AI, such as increased productivity, efficiency, and accuracy, and highlights the challenges that AI poses, including the displacement of certain jobs and the need for retraining workers. The review paper also discusses how AI is impacting workplace safety and health, including both the potential benefits and the new hazards that AI presents. Finally, the review paper explores the policies and regulations needed to ensure that AI is used ethically and responsibly in the job market. Overall, this review paper aims to provide a comprehensive understanding of the impact of AI on the job market and its implications for workers, businesses, and society

Introduction This chapter discusses philosophical reflections on the intellectual adventure of conducting Integrated Assessment (IA) Focus Groups with citizens, as presented in this volume. The task of this exercise was ambitious: to bridge the gap between sustainability science and democratic debate in the climate domain. The science component was mainly represented by models, most (although not all) having the appearance of describing future states of the global climate and their consequences for human society. At first it could seem a daunting, indeed, overwhelming task: it was hard to see how lay participants could meaningfully relate to models whose construction required very special expertise in mathematics and software engineering; and whose comprehension required knowledge of climate science. But having witnessed the debates among the modelers themselves, the research team already knew that IA models are quite problematic products of science. It is freely accepted, even emphasized, among the experts that the models do not provide simple predictions; and so their epistemic status and policy relevance were already open to question. In addition, there was the knowledge that experts are usually “laypersons” outside their specialties, and that policy-makers are generally no more knowledgeable than ordinary citizens. And, in any event, the democratic process involves debate over issues where both expert and lay voices are heard. Hence the IA models were an appropriate vehicle for developing a many-sided dialogue on basic issues. In the event, the involvement of this “extended peer community” proved far less difficult than anticipated.

Family interventions appear to be effective at treating young people's substance misuse. However, implementation of family approaches in UK services is low. This study aimed to demonstrate the feasibility of recruiting young people to an intervention based on an adaptation of adult social behaviour and network therapy. It also sought to involve young people with experience of using substance misuse services in the research process. To demonstrate the feasibility of recruiting young people to family and social network therapy and to explore ways in which young people with experience of using substance misuse services could be involved in a study of this nature. A pragmatic, two-armed, randomised controlled open feasibility trial. Two UK-based treatment services for young people with substance use problems, with recruitment taking place from May to November 2014. Young people aged 12-18 years, newly referred and accepted for structured interventions for drug and/or alcohol problems. A remote, web-based computer randomisation system allocated young people to adapted youth social behaviour and network therapy (Y-SBNT) or treatment as usual (TAU). Y-SBNT participants were intended to receive up to six 50-minute sessions over a maximum of 12 weeks. TAU participants continued to receive usual care delivered by their service. Feasibility was measured by recruitment rates, retention in treatment and follow-up completion rates. The main clinical outcome was the proportion of days on which the main problem substance was used in the preceding 90-day period as captured by the Timeline Follow-Back interview at 3 and 12 months. In total, 53 young people were randomised (Y-SBNT, n = 26; TAU, n = 27) against a target of 60 (88.3%). Forty-two young people attended at least one treatment session [Y-SBNT 22/26 (84.6%); TAU 20/27 (74.1%)]; follow-up rates were 77.4% at month 3 and 73.6% at month 12. Data for nine young people were missing at both months 3 and 12, so the main clinical outcome analysis was based on 24 young people (92.3%) in the Y-SBNT group and 20 young people (74.1%) in the TAU group. At month 12, the average proportion of days that the main problem substance was used in the preceding 90 days was higher in the Y-SBNT group than in the TAU group (0.54 vs. 0.41; adjusted mean difference 0.13, 95% confidence interval -0.12 to 0.39; p = 0.30). No adverse events were reported. Seventeen young people with experience of substance misuse services were actively involved throughout the study. They informed key elements of the intervention and research process, ensuring that the intervention was acceptable and relevant to our target groups; contributing to the design of key trial documents, ideas for a new model of public involvement and this report. Two parents were also involved. The adapted intervention could be delivered in young people's services, and qualitative interviews found that Y-SBNT was acceptable to young people, family members and staff. Engagement of family and network members proved difficult within the intervention and research aspects. The study proved the feasibility of this work in routine services but outcome measurement based on narrow substance use variables may be limited and may fail to capture other important changes in wider areas of functioning for young people. Validation of the EuroQol-5 Dimensions for young people aged 12-18 years should be considered and flexible models for involvement of young people in research are required to achieve inclusive representation throughout all aspects of the research process. Although recommendation of a full trial of the Y-SBNT intervention compared with TAU is not supported, this study can inform future intervention development and UK research within routine addiction services. Current Controlled Trials ISRCTN93446265. This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 21, No. 15. See the NIHR Journals Library website for further project information.

Context. Development of safety assurance cases (SACs) is a trending need for certification of safety-critical systems in regulated domains. Their development encompasses collecting, linking, and structuring diverse project information, such as requirements, hazards, and components. A key building block of SACs is the explanation behind requirements and design decisions, the so-called assurance rationale, which provides compelling argumentation on the system suitability for use. Late development of SACs leads to late discovery of hazards, risks, requirement and design issues that affect the system safety. Question/ problem. Current practice is to manage the assurance rationale in the SAC tools or artefacts, even though it is closely related to project and traceability information. This separation inhibits the shared understanding between safety and software professionals and may cause loss of rationale that results in a poor quality of certification documentation. It also hinders the automated SAC construction and assessment directly from project management tools. Principal ideas/results. This paper presents an approach to support the management of assurance rationale integrated with project information. Our approach defines an assurance rationale model that binds requirements, safety analysis information and assurance case elements. We show the use of the model applied to an issue tracking system. Contribution. We propose a new perspective on assurance cases management by bringing their building blocks into project management tools. Such integration could help bridge Safety and Software Engineering activities and avoid loss of rationale through the system life cycle. Also, it fosters the development of new approaches for SAC generation and assessment.