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This study aims to examine research collaboration between universities and industry in the field of computer science using bibliometric methods. To further understand the impact of individual countries on university-industry (UI) collaboration, data were retrieved from the computer science journal papers included in the Science Citation Index and published during the period 2002--2011. The density, degree centrality, betweenness centrality, and closeness centrality of UI collaborative networks were calculated in order to ascertain the core countries involved in international UI collaboration. This study investigates university-industry collaborative papers (UI papers) at the global and country levels. At the global level, the results show an increasing trend in UI papers and a stable UI collaboration ratio during the last decade. The average number of authors for UI papers is 4.13, which is higher than that for all other papers. Most UI papers result from two-institution collaboration, but we observed an increase in the number of UI papers produced by multiple universities and industrial entities. At the country level, the results also show that international UI paper collaboration has intensified. Meanwhile, the USA is the dominant collaborative partner with many countries and has produced the highest number of UI papers globally. [ABSTRACT FROM AUTHOR]

The present study aimed to discover how selected English language authors employ specific rhetorical, grammatical, and stylistic elements in presenting new knowledge to the target audience and how they communicate science to the community of academicians in order to convey scientific information to various audiences. To this end, the generic structures, as well as lexico-grammaticality systems inbuilt in introduction sections of English academic research papers of three different disciplines, linguistics, chemistry and software engineering, were explored. To collect the data, a corpus consisting of 90 papers, 30 for each discipline, published between 2014 and 2016 in Elsevier journals was selected randomly. For genre analysis, Hasan’s model of generic structure potential was used, while Halliday’s model was used for the analysis of lexico-grammaticality systems. The results showed that ‘Previous study’ and ‘Purpose of study’ were the obligatory elements among the 11 types of generic structures. Furthermore, material process, declarative mood, the use of conjunctions in textual theme and circumstantial adjunct were mostly used, while no interpersonal theme was found in the obligatory elements. The findings promise pedagogical implications for novice writers and university students of English language generally, and second language or foreign language writers from different cultures in different fields of study, in particular. [ABSTRACT FROM AUTHOR]

Despite rapid advances, modeling a variety of instructional designs to support variations in teaching and learning during the design of educational technologies is still an open challenge. In this paper, we propose a patterns based approach for the design of educational technologies to address this challenge. This is in contrast with existing literature that focuses either on patterns in education or in software, and not both. The core idea of our approach is to leverage patterns for modeling instructional design knowledge and to connect it with patterns in software architecture. We discuss different categories of patterns in instructional design. We then present the notion of Pattern-Oriented Instructional Design (POID) as a way to model instructional design as a connection of patterns (ProcessPattern, ContentPattern) and integrate it with Pattern-Oriented Software Architecture (POSA) based on fundamental principles in software engineering. We demonstrate our approach through adult literacy case study (287 million learners, 22 Indian Languages and a variety of instructional designs). The results of our approach (both web and mobile versions) are available at http://rice.iiit.ac.in and were transferred to National Literacy Mission Authority of Government of India for further proliferation. [ABSTRACT FROM AUTHOR]

Organizational dependency on Information and Communication Technology (ICT) drives the preparedness challenge to cope with business process disruptions. Business Continuity Management (BCM) encompasses effective planning to enable business functions to resume to an acceptable state of operation within a defined timeframe. This paper presents a systematic literature review that communicates the strategic guidelines to streamline the organizational processes in the BCM program, culminating in the Business Continuity Plan design, according to the organization's maturity. The systematic literature review methodology follows the Evidence-Based Software Engineering protocol assisted by the Parsifal tool, using the EbscoHost, ScienceDirect, and Scopus databases, ranging from 2000 to February 2021. International Standards and Frameworks guide the BCM program implementation, however, there is a gap in communicating metrics and what needs to be measured in the BCM program. The major paper result is the confirmation of the identified gap, through the analysis of the studies that, according to the BCM components, report strategic guidelines to streamline the BCM program. The analysis quantifies and discusses the contribution of the studies on each BCM component to design a framework supported by metrics, that allows assessing the organization's preparedness in each BCM component, focusing on Information Systems and ICT strategies. [ABSTRACT FROM AUTHOR]

Cross-project defect prediction (CPDP) technology can effectively ensure software quality, which plays an important role in software engineering. When encountering a newly developed project with insufficient training data, CPDP can be used to build defect predictors using other projects. However, CPDP does not take into account the prior knowledge of the target items and the class imbalance in the source item data. In this paper, we design an active learning selection algorithm for cross-project defect prediction to alleviate the above problems. First, we use clustering and active learning algorithms to filter and label some representative data from the target items and use these data as prior knowledge to guide the selection of source items. Then, the active learning algorithm is used to filter representative data from the source items. Finally, the balanced cross-item dataset is constructed using the active learning algorithm, and the defect prediction model is built. In this article, we selected 10 open-source projects by using common defect prediction models, active learning algorithms, and common evaluation metrics. The results show that the proposed algorithm can effectively filter the data, solve the class imbalance problem in cross-project data, and improve the defect prediction performance. [ABSTRACT FROM AUTHOR]

The use of robust software engineering processes is essential in the design and development of interactive systems. This ensures that software is both functionally correct and also usable in the required context. There are a variety of software engineering techniques that can be used to consider different aspects of a system under construction, and at different stages in the development process. There is, however, a natural tension between formal approaches (typically used in the domain of safety-critical systems to consider functional correctness) and informal design approaches, which focus on users and user requirements in a manner which is accessible by stakeholders. In this paper we present two new approaches which enable a tighter coupling of informal design requirements with formal models. We present examples of these two approaches and discuss the benefits that transformations between the informal and formal provide. We also discuss the current limitations of such work along with recommendations about how these might be addressed. [ABSTRACT FROM AUTHOR]

Sustainable tourism research focuses on mitigating or remediating environmental, social and economic impacts on tourism. In the past years, Big Data approaches have been applied to the field of tourism allowing for remarkable progress. However, there seems to be little evidence to support that such approaches are an inspiration to sustainable tourism and are being implemented. In this context, we aim to obtain a comprehensive overview of the use of Big Data in sustainable tourism to address various issues and understand how Big Data can support decision-making in such scenarios. To that end, this paper reports on the results of a literature review via a combination of a Systematic Literature Review (SLR) in Software Engineering, and the use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. In summary, we investigated four facets: (a) sources of big data, (b) approaches, (c) purposes, and (d) contexts of application. The results suggest that the use of various approaches have impacted practices in sustainable tourism. The findings provide a thorough understanding of the state of the art of Big Data application in sustainable tourism and provide valuable insights to foster growth both in terms of research and practice. [ABSTRACT FROM AUTHOR]

This paper introduces a new framework for understanding, modelling and software engineering in construction information activities. The current framework is based on understanding that products are the results of processes, which are performed by actors. Such frameworks are influenced by the available technology. The Internet of today is supporting also other kinds of human activities: communication and social interactions among humans. The construction industry uses them as well but without having a proper understanding of their role. There is a gap in the current framework. In this paper, the relevant theories to specify this gap in terms of what could exist in theory and what is offered by technology are analysed. As a result, a new framework of construction information activities that fills the gap is proposed. The key concepts and relations among them are identified and elaborated within the existing framework. The framework introduces the third major integrative element of the otherwise fragmented construction information activities – the social network – the existing two being the physical building and its digital twin. The framework provides a theoretical and conceptual basis for designing, planning, creating, monitoring and evaluating construction-related online services that include a strong social component and use social media services. [ABSTRACT FROM AUTHOR]

This paper argues that current practice in the Operational Research (OR) discipline needs to tackle management problems from a broader point of view by including a new perspective. While Hard OR is posed as the means to a solution, and Soft OR covers the stakeholder/business perspective, this paper proposes OR Engineering to tackle the model/program perspective. This proposal is substantiated by an empirical study that explored OR history and the rise of "wicked problems" in contrast to Software Engineering (SE) history and the idea of "no silver bullet". The main findings are five areas for future works, intertwined between Soft OR and OR Engineering: agility, documentation of versioning, technical debt, systems testing and architecture. Overall, although the goal may be ambitious, this paper aims to stimulate reflective thinking and promote a novel and different line of action and research among OR and SE practitioners present and future. [ABSTRACT FROM AUTHOR]

Architectural synthesis is a key phase of the conceptual design process. In the constant evolution of complex systems, architects have found it difficult to carry out an exhaustive search of all the feasible architectural solutions for a set of given requirements. This paper proposes a computational synthesis framework that automatically generates, from a set of formatted requirements and a sub-systems database, all architectures that are requirement consistent, minimalist regarding the number of components and non-redundant. The resolution principles are based on graph theory representations and Constraint Programming solving techniques. Key features of the framework and an application example are also detailed. Abbreviations: AO: architectural object; BPMN: business process model and notation; CSP: constraint satisfaction problem; DSM: design structure matrix; SE: systems engineering. [ABSTRACT FROM AUTHOR]

Collaboration Engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by domain experts without the ongoing support of external collaboration professionals. Since 2001, CE has been an active and productive topic of research that has attracted scientists from different backgrounds and disciplines. CE research started with studies on ways to transfer professional collaboration expertise to novices using a pattern language called thinkLets. Subsequent research focused on the development of theories to explain key phenomena, the development of a structured design methodology, training methods, technology support, design theories, and various field and experimental studies focusing on specific aspects of the CE approach. This paper details the contributions from CE research and practice based on a literature assessment of 331 publications. It extracts the key insights from the body of CE research thus far, identifies significant areas of inquiry that have not yet been explored, and looks ahead at the CE research opportunities that are emerging as our society, organizations, technologies, and the nature of collaboration evolve. [ABSTRACT FROM AUTHOR]

First, I thank Dr. Jensen for a paper that provides a great overview of the various disciplines that involve analysis of data, and which stimulated much discussion at the 2019 Stu Hunter conference. Because modeling and analysis is almost always an iterative process, version control systems are useful for managing analytics workflows, even beyond the Statistical or Machine Learning Engineering domains, and are commonly used by many industry analytics teams. Extensions to the commonly used version-control system git, such as git-annex ([4]) and Data Version Control (DVC [1]) (Open-source version control system) have been developed to try and address these issues. [Extracted from the article]

This paper contributes to rapid prototyping for Aboleth, a mobile application designed as a virtual character sheet for Dungeons and Dragons 5th edition. We propose augmenting an API framework, including details of the general design process and crucial details regarding the specific implementation of the project. The prototype is developed using Computer-Assisted Designs and Xamarin. Software engineering is integrated into game design to improve Human-Computer Interaction and eventually enhance user experiences. Additionally, we discuss the relevance of using such a tool to achieve rapid prototyping for enterprise mobile applications and Metaverse and explore opportunities to improve the project design. [ABSTRACT FROM AUTHOR]

Learning from success projects to build business process for customer requirements change is critical successful factor for other project in IoT enterprise systems. We proposed an ontology-based technology to mine the core knowledge of successful projects. From precious experiences of each successful projects, two ways of ontology-based domain knowledge pattern will be defined, first is for application project management, and second is for software engineering process. To decompose the project knowledge from the same application domain with tree structures. The results of this paper reduce development and requirement cost, and user satifaction is better than without ontology. [ABSTRACT FROM AUTHOR]

A test case is a cornerstone of the testing process; hence, it is quintessential to ensure the quality of the test cases. However, test case design in the Agile testing process has limitations that affect its quality standards, leading to the failure of many software projects. Previous studies are limited in providing a clear guideline for assessing the test case quality. However, evaluating the quality of test cases will help software testing practitioners to understand some critical issues in designing test cases from various perspectives. Therefore, this paper aims to present the verified factors and criteria of test case quality by software testing experts in an Agile environment. The proposed factors and criteria were verified through expert review techniques, which included an online survey of professionals and those in domain and knowledge experts. Twenty-three industry practitioners, including developers, testers, and systems analysts, were used to identify the domain experts. In contrast, the knowledge experts were drawn from 13 academic areas of software engineering and testing. The results showed that the quality of test cases is paramount in Agile projects; therefore, the experts accept seven factors and 32 criteria with a few mod- ifications and suggestions. The finding may assist the researchers in improving the proposed factors and criteria for validation purposes. Hence, may contribute to soft- ware testing practitioners as a guideline in constructing, designing, and assessing compelling test cases. Most significantly, the fixed factors and criteria may contribute to the body of knowledge in the software testing domain and industries. [ABSTRACT FROM AUTHOR]

During the last decade, Unmanned Aerial Vehicles (UAVs) gained wide attention and are integrated into diverse systems and deployed into many contexts. The need to assess the UAVs' performances, impacts of applications, routing protocols, mobility as well as other features in a network is necessary. However, conducting real experiments within UAV-based systems and particularly with multi-UAV-based systems can be extremely costly and complex. To fill this gap simulators, emulators, and frameworks are used to evaluate the performance and viability of UAV-based systems with lower cost and in a short period. Accordingly, researchers and software engineers developed several simulators and co-simulators dedicated to the performance evaluations of UAV-based systems. In this regard, this investigation highlights and identifies the most suitable simulators, co-simulators, emulators, and frameworks for UAV flights and management. Both the tools dedicated to mono-UAV-based systems and the tools dedicated to multi-UAV-based systems are pinpointed. In this paper, the goals, the requirements, the strengths, and the limitations of each studied tool are detailed. Besides that, a UAV simulators catalog is provided. It includes guidelines that will be useful for researchers and help them to identify and to select the adequate tools for UAVs performance analysis that meet their needs. [ABSTRACT FROM AUTHOR]

The Journal of Spatial Science has announced the winner of the Best Research Paper award for 2023. The winning paper is titled "Road edge detection based on combined deep learning and spatial statistics of LiDAR data" and was authored by Dragan D. Kukolj, Igor Marinovic, and Sandra D. Nemet. Dragan D. Kukolj is a full professor at the University of Novi Sad and has expertise in machine learning and image processing. Igor Marinovic is the CEO of Manufaktura Ltd and specializes in geographic information systems and AI applications. Sandra D. Nemet works at OBLO Living Ltd and focuses on software engineering and AI development. The authors have published numerous papers in their respective fields. [Extracted from the article]

The presence of defect data related to different projects leads to cross-project defect prediction an open issue in the field of research in software engineering. In cross-project defect prediction, the source and the target projects are different. The prediction model is trained by using the data sources of the different projects and then it is tested on the target data source. The data source from the varying projects leads to a highly imbalanced source dataset. The performance of the predictive model degrades due to this imbalance nature of the dataset. This is termed as the class imbalance problem in machine learning. This paper conducts an empirical analysis in a bi-fold manner. It evaluates whether data sampling techniques can handle the class imbalance problem and improve the performance of the predictive model for cross-project defect prediction (CPDP). Secondly, it also evaluates whether the results of CPDP after data sampling are comparable to within project defect prediction (WPDP). Ensemble learning classifiers are used as the predictive model over 12 publically available object-oriented project datasets. The experimental results infer that SMOTE oversampling can be applied to overcome the problem of class imbalance on CPDP. It also gives comparable results to WPDP with statistical significance. [ABSTRACT FROM AUTHOR]

Test-suite minimization problem is an essential problem in software engineering as its application helps to improve the software quality. This paper proposes a quantum algorithm to solve the test-suite minimization problem with high probability in O 2 n , where n is the number of test cases. It generates an incomplete superposition to find the best solution. It also handles the non-uniform amplitudes' distribution case for the system with multisolutions. The proposed algorithm uses amplitude amplification techniques to search for the minimum number of test cases required to test all the requirements. The proposed algorithm employs two quantum search algorithms, Younes et al. algorithm for quantum searching via entanglement and partial diffusion to prepare incomplete superpositions that represent different search spaces such that the number of test cases is incremented in each search space, and updated Arima's algorithm to handle the multisolutions case. The updated Arima's algorithm searches for a quantum state that satisfies an oracle that represent the instance of the test-suite minimization problem. [ABSTRACT FROM AUTHOR]

Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since Nolan and Temple Lang's seminal paper, we have seen a greater emphasis on data wrangling, reproducible research, and visualization. This shift better prepares students for careers working with complex datasets and producing analyses for multiple audiences. But, we argue, statisticians are now often called upon to develop statistical software, not just analyses, such as R packages implementing new analysis methods or machine learning systems integrated into commercial products. This demands different skills. We describe a graduate course that we developed to meet this need by focusing on four themes: programming practices, software design, important algorithms and data structures, and essential tools and methods. Through code review and revision, and a semester-long software project, students practice all the skills of software engineering. The course allows students to expand their understanding of computing as applied to statistical problems while building expertise in the kind of software development that is increasingly the province of the working statistician. We see this as a model for the future evolution of the computing curriculum in statistics and data science. [ABSTRACT FROM AUTHOR]

Organisational learning is an area that aids organisations in improving their processes by reusing experience so that knowledge is accessible to the entire organisation. However, it is not a trivial task to bring about such learning in software development companies, especially as this is an area where processes and knowledge are often internalised in the minds of employees. Some approaches presented in the literature to facilitate organisational learning aim at transforming content generated within companies with Web 2.0 collaborative tools in courses. These courses are based on learning schema that describes their structure and content. However, the creation of these schemes requires a specialist who knows the company's needs as well as the pedagogical organisation's learning requirements. In order to facilitate learning and minimise dependence on specialists, this paper presents an approach that aims to aid companies in identifying developers' learning needs while creating learning schema in a semiautomatic way. Based on the approach proposed, an environment was implemented and an experimental study was conducted to evaluate the identification of relevant issues to the organisation and development of courses. Results suggest that this approach is feasible for discovering themes and creating in-company courses. [ABSTRACT FROM AUTHOR]

This paper describes a French research project to co-design an early literacy/early numeracy tablet app for kindergarten students. In order to create an app that takes into account classroom/teaching constraints and the results of recent research, we adopted a theoretical co-design framework and followed a methodology involving researchers, education professionals and software engineers. We describe the design process and the app's educational content, together with the results of an initial trial of the app on a small sample of preschoolers, which showed that it is ready for larger scale testing. We conclude by discussing the specificities of our project and its contribution to the literature on the development of classroom apps for preschoolers. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Software Engineering are considered as significant fields to solve various problems. However, there are weaknesses in certain problem-solving in each field. Thus, this paper is a broad-based review of using artificial intelligence (AI) to improve software engineering (SE), and vice versa. As well as it intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The aim of this review is highlighted in how the previous study benefited from incorporating the advantages of both fields. The researchers and practitioners on AI and SE belong to a wide range of audiences from the domains of optimization, engineering, data mining, clustering, etc., who will benefit from this study and areas for potential future research. [ABSTRACT FROM AUTHOR]

Although very important for analysis of drying processes, physics-based models are limited in terms of their prediction ability and in most cases are unsuitable for real-time process control and optimization of industrial drying. In this paper, we provide an overview of the machine learning (ML) techniques and the state-of-the-art ML applications in drying of food and biomaterials. The applications include but not limited to data-driven models, nonlinear control and multi-objective optimization. The advantages of integration of ML with machine vision for real-time observation of product quality and fine-tuning control strategies are briefly discussed. Future research should focus on the integration of ML software tools with sensors to measure process and product variables. In addition, the drying research community should contribute towards building of open-source datasets, which is extremely important to leverage the power of ML algorithms. Integration of sensors, process analysis and software engineering will enable the development of "intelligent" drying systems. [ABSTRACT FROM AUTHOR]

The digital transformation taking place in all areas of life has led to a massive increase in digital data – in particular, related to the places where and the ways how we live. To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multi-source data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP's pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development. [ABSTRACT FROM AUTHOR]

High electricity and water expenditure constitute a significant issue, as recent statistics demonstrate the continuous increase in energy consumption in the European Union (EU). This results in environmental, financial, and social issues. One such major issue is energy poverty. Software tools can influence users' behavior, enabling them to improve certain situations and enhance their awareness and education around important topics. Such tools must also be usable and offer a positive user experience. In this context, a set of 15 innovative ICT tools were created under the Erasmus+ EU-funded project IDEA: "Innovative Direction in Energy Advising," aiming, on the one hand, to assist individuals in decreasing their households' electricity and water consumption through the development of energy awareness, and on the other hand, to help energy experts in their fight against energy poverty. Our previous work presented an overall brief view of the 15 ICT tools. In this work, we focus on the two core sets of tools. We present (i) the software engineering process we followed toward developing the tools, including a detailed needs analysis and important design and development aspects of the tools, as well as the final product; and (ii) a user evaluation from the perspectives of usability and user experience, during which quantitative and qualitative data were collected and analyzed, followed by a presentation of the analysis conducted and discussion of the results. We concluded that energy experts felt that the IDEA toolkit appears very useful in comparison to what they are currently using. In regards to the wider audience, participants' experience satisfaction (Water Tool (WT) (93%), Lighting Tool (LT) (86%), and Heating Tool (HT) (81%)), and usefulness of the tools (WT (86%), LT (83%), HT (76%)) were highly rated. [ABSTRACT FROM AUTHOR]

The invention of the computer has led to the establishment of a new research paradigm, computation, which has recently become more and more popular in scientific exploration. However, computation is not well represented in high school and university curricula in science and engineering, although it applies to a wide range of disciplines beyond computer science and software engineering. In light of the increasing need to provide students with computational education, this paper presents a novel way to develop computational thinking among students. The proposed approach is based on the implementation of Papert's theory of constructionism in electronic spreadsheets. In this approach, students build their knowledge while constructing the difference equation that describes a physical (or engineering) phenomenon, based on specific cases investigated in the spreadsheet. The method does not require the students to write code or perform complex calculations in the spreadsheet and makes it possible to teach advanced subjects at a relatively early stage. The method is demonstrated through contents taken from the secondary and tertiary curricula in mechanics and electromagnetism. [ABSTRACT FROM AUTHOR]

In the twenty years since the agile approach to systems development was introduced, a wide range of organizations have adopted its principles. Despite numerous reports that highlight agile success stories, there are relatively few recent publications that provide practical guidance on the potential pitfalls and areas of risk related to agile. In response, this practice-oriented paper draws on 56 practitioner interviews to identify a series of "lessons learned" pertaining to agile adoption, tailoring, and use. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for state estimation and accuracy improvement of computer simulations of computer aided engineering (CAE) models based on prediction and correction state estimation techniques and sensing. The aim is to simulate the dynamic behaviour of a real system, which can be sensed, and obtain values of states that are not measurable due to economic or technical limitations. This methodology can be applied to both optimization of design processes and on-line control of complex systems. State estimation techniques are currently used only on mathematical models, where the relationships among system variables are expressed by means of mathematical language, making state observer implementation possible but leading to limitations in system modelling and knowledge. Favoured over mathematical models, multibody CAE models (created by means of computer-aided engineering software) have become the essential tool for complex system development, simulation, analysis, optimization and control, such as multibody systems; one of their main advantages is the ease and flexibility in creating and modifying them, allowing the faithful modelling of complex systems. [ABSTRACT FROM AUTHOR]

Component deployment is a combinatorial optimisation problem in software engineering that aims at finding the best allocation of software components to hardware resources in order to optimise quality attributes, such as reliability. The problem is often constrained because of the limited hardware resources, and the communication network, which may connect only certain resources. Owing to the non-linear nature of the reliability function, current optimisation methods have focused mainly on heuristic or metaheuristic algorithms. These are approximate methods, which find near-optimal solutions in a reasonable amount of time. In this paper, we present a mixed integer linear programming (MILP) formulation of the component deployment problem. We design a set of experiments where we compare the MILP solver to methods previously used to solve this problem. Results show that the MILP solver is efficient in finding feasible solutions even where other methods fail, or prove infeasibility where feasible solutions do not exist. [ABSTRACT FROM AUTHOR]

Prototyping plays various roles in software engineering: it can function in an exploratory way in order to gather requirements, or generate an artifact that, through iterative cycles of development, leads to final delivery of the product or system. According to literature (Beaudouin-Lafon, M., and W. Mackay. 2007. “Prototyping Tools and Techniques.” InHuman Computer Interaction Handbook: Fundamentals. CRC Press.; Lim, Y. K., E. Stolterman, and J. Tenenberg. 2008. “The anatomy of prototypes: Prototypes as filters, prototypes as manifestations of design ideas.”ACM Transactions on Computer-Human Interaction (TOCHI)15 (2): 7.), a prototype can be described as an incomplete but flexible communication tool for a design idea, both manifesting and filtering interesting aspects of the original idea. On this basis, we propose a Peircean definition for this tool: a prototype can be considered as a complex set of signs, or better as a text, because each of its features literally “stands for” corresponding features of the final artifact, foreseeing particular aspects or the overall capacity of the outcome. But what happens when stakeholders involved in a project have no or little competence in tools and technologies discussed? Can they correctly interpret a prototype? To answer this question, we propose a Discourse Analysis (Duranti, A. 1997.Linguistic Anthropology. Cambridge, Mass: Cambridge University Press; Potter, J. 2004. “Discourse analysis as a way of analyzing naturally occurring talk.” InQualitative research: Theory, Method and Practice, edited by D. Silverman, 200–221. SAGE; Schegloff, J. 1989. “Harvey Sacks’ lectures on conversation: an Introduction/memoir.”Human Studies12: 185–209.) of a co-design session with elders of a daily centre for frail adults in Rome. In this context, we could observe how interactions between the material aspect of the prototype (i.e. the product itself) and its intended meaning (i.e. its significance) can produce distortions, in the form of aberrant interpretations or even a complete lack of comprehension. During this communication breakdown, prototypes lose their connection to the features and behaviors of the product: the relation betweenrepresentamenandinterpretantis lost. [ABSTRACT FROM PUBLISHER]

Cyber-physical system (CPS) is an emerging area, which cannot work efficiently without proper software handling of the data and business logic. Software and middleware is the soul of the CPS. The software development of CPS is a critical issue because of its complicity in a large scale realistic system. Furthermore, object-oriented approach (OOA) is often used to develop CPS software, which needs some improvements according to the characteristics of CPS. To develop software in a CPS environment, a new systematic approach is proposed in this paper. It comes from practice, and has been evolved from software companies. It consists of (A) Requirement analysis in event-oriented way, (B) architecture design in data-oriented way, (C) detailed design and coding in object-oriented way and (D) testing in event-oriented way. It is a new approach based on OOA; the difference when compared with OOA is that the proposed approach has different emphases and measures in every stage. It is more accord with the characteristics of event-driven CPS. In CPS software development, one should focus on the events more than the functions or objects. A case study of a smart home system is designed to reveal the effectiveness of the approach. It shows that the approach is also easy to be operated in the practice owing to some simplifications. The running result illustrates the validity of this approach. [ABSTRACT FROM AUTHOR]

In response to the so-called ‘software crisis’ of the late 1960s, many approaches were proposed to turn (parts of) software engineering and programming into more systematic disciplines, to turn an art into a science. This paper studies one popular example often used in these proposals, the computation of a list of primes, to discuss some salient features of the proposed programming paradigms. It also looks at the actual implementation in the early 1970s of the prime program on a time-sharing system (MULTICS) and on a complex scientific computer (ILLIAC IV). Confronting theory with practice uncovers what the programming paradigms fail to grasp: the interaction with the user and the interaction with the machine. [ABSTRACT FROM PUBLISHER]

One prominent approach in the exploration of the variations in project team performance has been to study two components of the aggregate personalities of the team members: conscientiousness and agreeableness. A second line of research, known as self-categorisation theory, argues that identifying as team members and the team's performance norms should substantially influence the team's performance. This paper explores the influence of both these perspectives in university software engineering project teams. Eighty students worked to complete a piece of software in small project teams during 2007 or 2008. To reduce limitations in statistical analysis, Monte Carlo simulation techniques were employed to extrapolate from the results of the original sample to a larger simulated sample (2043 cases, within 319 teams). The results emphasise the importance of taking into account personality (particularly conscientiousness), and both team identification and the team's norm of performance, in order to cultivate higher levels of performance in student software engineering project teams. [ABSTRACT FROM PUBLISHER]

For many years, component-based software engineering has been the most promising software engineering approach to develop complex software systems. However, with the increasing adoption of internet technologies, the demand for dynamic and flexible approaches is becoming essential. Thus, the service-oriented software engineering has emerged. Both approaches have strengths and weaknesses; it can be difficult to identify the appropriate approach that perfectly suits a particular system's need, principally in highly dynamic environments. In order to take full advantages of both approaches, the hybrid approach between component and service (HACS) has been proposed. HACS makes possible a definition of a hybrid, flexible, and controllable architecture consisting simultaneously of component and web services and respecting the software architect administration rules. In this paper, the dynamic recomposition in HACS is explained and validated by a simulator. We use Dijkstra algorithm over a GraphPlan with a new weighting function calculated from quality of service properties and the similarity matching between different HACS components. We also suggest a solution to minimize the execution time of future invocations. A detailed example is given to prove the efficiency of our proposal. [ABSTRACT FROM AUTHOR]

Sundown of the twentieth century saw the emergence of the World Wide Web. A decade later, semantic web (SW) envisioned enriching of web-accessible information and services with machine-processable semantics to solve the problems of information management and sharing which are aroused by the success of the web. However, success of the SW largely depends on the availability of formal ontologies for representing and structuring information in different domains. To facilitate web ontology engineering (OE), methodologies are proposed by the researchers since years. However, OE particularly web OE is still immature and none of the methodologies is standardized for web OE projects as yet. This paper presents an evolutionary SW ontology engineering methodology called POEM. POEM presents a complete methodology comprising of various phases for performing conceptualization, designing, implementation, evaluation, and project management tasks. POEM exploits and incorporates the large experiences from the widely used software engineering standards to make the methodology more realistic and provide greater functionality. Due to its broad nature, POEM has the potential applicability in a wider range of OE projects. Comparison of POEM with other OE methodologies has ascertained POEM as more advantageous and easy methodology for SW ontology engineering. [ABSTRACT FROM PUBLISHER]

As a once-in-100-years pandemic, COVID-19 is changing and reshaping the world. Hosting COVID-19 applications such as COVID-19 Spatiotemporal Rapid Response Gateway, COVID-19 campus simulation, and COVID-19 Data Scraper (Rice et al. [19]); Supporting COVID-19 research such as Air Quality impact during COVID-19, COVID-19 vaccine-related tweets analysis, COVID-19 policy analysis, and COVID-19 related data storage. Such sharing will allow for better data as well as faster response to crises like the contemporary COVID-19 pandemic: the long-term data maintenance and sources. The international team formed by experts and participants from almost every US state and worldwide every time zone including the Geo-Computation Center for Social Sciences at Wuhan University, Tsinghua University, the China Data Institute at Michigan, the University of Queensland in Australia, RMDS Lab at Los Angles, and many other institutions to achieve the objectives of (1) providing data support for the spatiotemporal study of COVID-19 at local, regional and global levels with information collected and integrated from different sources; (2) facilitating quantitative research on spatial spreading and impacts of COVID-19 with advanced methodology and technology; (3) promoting collaborative research on the spatiotemporal study of COVID-19 on the Spatial Data Lab and Dataverse platforms; and (4) building research capacity for future collaborative projects. [Extracted from the article]

For blockchain software systems, framework developers may introduce technical debts that application developers are not aware of. Because these technical debts can have a negative impact on software projects, we need to investigate the issue of technical debt in blockchain software systems. We wanted to investigate what types of self-introduced technical debt exist in open-source blockchain software systems, and how these technical debts are distributed. We have selected six most popular blockchain software projects from GitHub. Then the code comments from these software projects were extracted and manually labelled. Finally, the code comments were statistically analysed. We propose a new type of technical debt, resource debt, which is explicitly identified by the framework developers and requires special attention in subsequent production systems. Six types of technical debt are prevalent and there is not any algorithm debt. In addition, we find that the code comments containing technical debt are not entirely determined by task tags. SATD is prevalent in blockchain projects. There is more significant variability between different application software projects for different technical debts. The results of the study imply that for detecting SATD, deep semantic discovery models should be used, such as pre-trained models. [ABSTRACT FROM AUTHOR]

To assume the productivity of students in the workplace, the higher educational institutions would be faced by a challenging reality that of how to keep focus on technicalities while improving the set of soft skills. Therefore, the main aim of this research is improving students' soft skills and thus their cognitive skills in parallel, to prepare them professionally, where they are put in a problem-based learning environment that is based on developing a software project. In this humble study, students undergo an experimental process where they are asked to develop a software project. The latter is defined by a teacher-set deadline period in which students' performed actions would be recorded to be used in the assessment process. To demonstrate the effectiveness of the developed system and the proposed approach for improving the soft skills, the prospective experiment was conducted at the level of a higher education institution. Over and above, the obtained results were highly satisfying and very encouraging. They also showed that the cognitive profiles and soft skills of most students were improved. [ABSTRACT FROM AUTHOR]

While incorporating project-based exercise is a common practice in software engineering education, few studies have been conducted in investigating how real-world project development influences university students' proactive learning and knowledge transformation. This study aims to evaluate the effectiveness of developing real-world projects with industry engagement in encouraging students to apply knowledge to practice and be more proactive in learning. Using a two-group, post-test quasi-experimental design, the performance between the students taking real-world project development and the students in the control group are compared using descriptive statistics, the independent samples t-test and Welch t-test, accordingly. Both the Spearman's rank-order and Kendall's τ-b are used to examine the relationship between students' practical works and the level of knowledge transformation estimated by the students through online surveys. The results suggest that using real-world projects in the classroom can be an effective motivational device for proactive learning and knowledge transformation. Project-based exercise should be both comprehensive and keeping pace with technology development driven by the software industry evolution to be more effective. The direct interaction with stakeholders, dynamic requirements change, employment of Agile methods, self-organising teams, and using challenging real-world projects, are essential in simulating a real-world software development environment in the classroom. [ABSTRACT FROM AUTHOR]

The competition in the software market demands that the time required for any software product to reach the market be reduced if the product is to survive competition from other developers. The pursuit of this goal has led to the adoption of agile software development methodologies. While other agile methodologies provide guidelines as to the software engineering (SE) practices to be used during the development lifecycle, Scrum does not. The purpose of this study is twofold: first, to identify the usage and level of importance of software engineering practices in the Scrum development environment; and second, to investigate how Scrum teams adopt an appropriate set of SE techniques and whether a hybrid Scrum/Extreme Programming (XP) methodology is an appropriate approach to take. This research was conducted by examining sample data from five organizations using the Scrum methodology. The sample included a range of industries including communications and embedded systems, financial asset management, software development houses and consulting firms in South Africa. The study employed a mixed method approach. A key finding was that, regardless of the fact that Scrum does not explicitly recommend engineering practices, there was extensive use of these practices by all of the participating organizations. The study also found that the lack of software engineering practices in Scrum does not constitute a barrier to a successful adoption of Scrum, provided the 'inspect and adapt' principle inherent in Scrum is properly followed. The study discusses the findings, explains the implications and suggests future research. [ABSTRACT FROM AUTHOR]

The prediction profiler is an interactive display in JMP statistical software that allows a user to explore the relationships between multiple factors and responses. A common use case of the profiler is for exploring the predicted model from a designed experiment. For experiments with a constrained design region defined by disallowed combinations, the profiler was recently enhanced to obey such constraints. In this case study, we show how our validation approach for this enhancement touched upon many of the fundamental principles of statistical engineering. The team approached the task as a design of experiments problem. [ABSTRACT FROM AUTHOR]

Vulnerability prediction facilitates the development of secure software, as it enables the identification and mitigation of security risks early enough in the software development lifecycle. Although several factors have been studied for their ability to indicate software security risk, very limited attention has been given to technical debt (TD), despite its potential relevance to software security. To this end, in the present study, we investigate the ability of common TD indicators to indicate security risks in software products, both at project-level and at class-level of granularity. Our findings suggest that TD indicators may potentially act as security indicators as well. [ABSTRACT FROM AUTHOR]

Software effort estimation (SEE) is a software engineering problem that requires robust predictive models. To establish robust models, the most feasible configuration of hyperparameters of regression methods is searched. Although only a few works, which include hyperparameter optimisation (HO), have been done so far for SEE, there is not any comprehensive study including deep learning models. In this study, a feed-forward deep neural network algorithm (FFDNN) is proposed for software effort estimation. The algorithm relies on a binary-search-based method for finding hyperparameters. FFDNN outperforms five comparison algorithms in the experiment that uses two performance parameters. The results of the study suggest that: 1) Employing traditional methods such as grid and random search increases tuning time remarkably. Instead, sophisticated parameter search methods compatible with the structure of regression method should be developed; 2) The performance of SEE is enhanced when associated hyperparameter search method is devised according to the essentials of chosen deep learning approach; 3) Deep learning models achieve in competitive CPU time compared to the tree-based regression methods such as CART_DE8. [ABSTRACT FROM AUTHOR]