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Quantum computing is based on the principles of quantum mechanics, such as superposition, entanglement, measurement, and decoherence. The basic units of computation are qubits, which are abstract objects with a mathematical expression to implement the quantum mechanics principles. Alongside quantum hardware, software is a principal element for conducting quantum computing. The software consists of logic gates and quantum circuits that implement algorithms for the execution of quantum programs. Due to those characteristics, quantum computing is a paradigm that non-physics experts cannot understand. Under this new scheme for developing software, it is important to integrate a conceptual framework of the fundamentals on which quantum computing is based. In this paper, we present a kind of taxonomical view of the fundamental concepts of quantum computing and the derived concepts that integrate the emerging discipline of quantum software engineering. We performed a quasi-systematic mapping for conducting the systematic review because the objective of the review only intends to detect the fundamental concepts of quantum computing and quantum software. The results can help computer science students and professors as a starting point to address the study of this discipline. [ABSTRACT FROM AUTHOR]

In a world where Artificial Intelligence (AI) is pervasive, humans may feel threatened or at risk by giving up control to machines. In this context, ethicality becomes a major concern to prevent AI systems from being biased, making mistakes, or going rogue. Requirements Engineering (RE) is the research area that can exert a great impact in the development of ethical systems by design. However, proposing concepts, tools and techniques that support the incorporation of ethicality into the software development processes as explicit requirements remains a great challenge in the RE field. In this paper, we rely on Ontology-based Requirements Engineering (ObRE) as a method to elicit and analyze ethicality requirements ('Ethicality requirements' is adopted as a name for the class of requirements studied in this paper by analogy to other quality requirements studied in software engineering, such as usability, reliability, and portability, etc. The use of this term (as opposed to 'ethical requirements') highlights that they represent requirements for ethical systems, analogous to how 'trustworthiness requirements' represent requirements for trustworthy systems. To put simply: the predicates 'ethical' or 'trustworthy' are not meant to be predicated over the requirements themselves). ObRE applies ontological analysis to ontologically unpack terms and notions that are referred to in requirements elicitation. Moreover, this method instantiates the adopted ontology and uses it to guide the requirements analysis activity. In a previous paper, we presented a solution concerning two ethical principles, namely Beneficence and Non-maleficence. The present paper extends the previous work by targeting two other important ethicality principles, those of Explicability and Autonomy. For each of these new principles, we do ontological unpacking of the relevant concepts, and we present requirements elicitation and analysis guidelines, as well as examples in the context of a driverless car case. Furthermore, we validate our approach by analysing the requirements elicitation made for the driverless car case in contrast with a similar case, and by assessing our method's coverage w.r.t European Union guidelines for Trustworthy AI. [ABSTRACT FROM AUTHOR]

ICT tools in education are widely used to support the aim of achieving learning outcomes by improving critical areas such as student engagement, participation, and motivation. In this study, we examine literature to explore how game elements are used in capturing students' interest, which the study suggests is fundamental to the teaching and learning of Software Engineering in higher education. Given the potential of alternative ICT tools such as flipped classrooms to increase interest in learning activities, there is a gap in similar literature on capturing interest in gamified environments, which has the potential to improve the achievement of learning outcomes. We applied flow theory to provide a guiding frame for our study. Following a systematic literature review for our data, we analysed 15 papers from the initial 342 articles, which were extracted from IEEE Xplore and Science Direct databases. The main finding in the reviewed papers underscores the positive impact of gamified learning environments on capturing student interest when teaching and learning Software Engineering. While the reviewed papers were not conclusive in identifying the best game elements for capturing students' interest, we found, that game elements such as points and leaderboards were the most common mechanisms used to advance students' interest when studying Software Engineering courses. The findings also suggest that different game elements are used in gamified environments to increase participation and engagement. The paper adds voice to the practical implications of gamification for teaching and learning. Although our study requires empirical evidence to validate our claims, we believe it sets the stage for further discussion. In the future, comparative studies of game elements in similar environments will be beneficial for identifying the ones that are more engaging and assessing their long-term impacts. [ABSTRACT FROM AUTHOR]

The current paper focuses on a framework for structuring large document stores with the help of intelligent metadata. The described landscape includes a proprietary knowledge graph which ingests millions of concepts from external, third-party data providers and accommodates internal class taxonomies; an NLP service for automated annotation of textual data; an annotations quality control mechanism; tools for knowledge graph ontology and concept management; and an extensive API layer. The authors present an approach they have tested and proved successful in one of the leading media companies in the world, whose media content is a core data asset. The proposed solutions enable content analytics in their proper context and allow explicit and implicit connections between the content and other company data – i.e., user (media content consumer) data. The latter empowers the efficient application of advanced analytical models for searches and recommendations and the implementation of accurate data-driven virtual assistants. The paper advises addressing the metadata quality concerns, which the authors’ extensive practice identifies as an essential prerequisite for applied analytics delivering significant business value. [ABSTRACT FROM AUTHOR]

Requirements engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or budget-overspending of software development projects. It is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. To this date, there exists no central concept of what RE education shall comprise. To lay a foundation, we report on a systematic literature review of the field and provide a systematic map describing the current state of RE education. Doing so allows us to describe how the educational landscape has changed over the last decade. Results show that only a few established author collaborations exist and that RE education research is predominantly published in venues other than the top RE research venues (i.e., in venues other than the RE conference and journal). Key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills. Finally, we discuss open opportunities in RE education, such as training for security requirements and supply chain risk management, as well as developing a pedagogical foundation grounded in evidence of effective instructional approaches. [ABSTRACT FROM AUTHOR]

The article discusses the IEEE International Requirements Engineering Conference (RE). This issue contains an introduction of the best papers presented during the conference on requirements activities in the fields of systems and software engineering. The conference was held at the University of Paris 1-Pantheon Sorbonne.

Bibliometric rankings are quite common in the field of software engineering. For example, there are a series of ranking repeated every year which identify the top researchers and institutions at the international level in the field. There are also other studies to determine the most cited articles in software engineering journals, the most popular research topics in this area, or identify the top researchers and institutions in regional levels. However, there exists no existing bibliometric quantitative analysis of publications in the area of software engineering (SE), including relative and absolute growth in the number of all SE publications as well as an analysis among countries. This is the main goal and motivation of this article. Besides, this study intends to provide an overall quantitative trend of the software engineering papers, and compare that trend to research output in other areas of science. The bibliometric study reported in this paper is motivated by the fact that understanding the amount of geographical research contributions to the field of software engineering can help identify different countries' level of commitment to support research activities in this area over years. We analyze how the contribution levels of top-ranked countries have changed over the years and how SE compares to other disciplines of engineering and science. Among the most interesting findings of this study are: (1) Over 40 years, in total about 60% of the SE literature has been contributed by only 7% of all countries, (2) the SE research output of different countries does not necessarily correlate with their GDPs, (3) the share of contributions to the SE discipline by the American researchers has declined from 71.43% (in 1980) to 14.90% (in 2008), and (4) China is the country with the biggest share growth in the number of publications (from 0.82% of the entire SE publications in 1991 to 13.82% in 2009). [ABSTRACT FROM AUTHOR]

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An existing statechart testing method developed by the authors allows a tester to show the equivalence of the behaviour of an implementation of some software system to its Harel statechart specification, by testing. This requires a rather large test set and satisfaction of certain requirements for both a specification and an implementation. Introduction of additional constraints permits reduction in a test set size, without weakening of the conclusions obtained by testing. This paper describes how a test set can be derived from a relatively big statechart and investigates the role of a few specific constraints in the reduction of test set size, depending on where in the system structure these constraints are used. The main contribution of the paper is a demonstration that enforcing relatively weak constraints in the right place of a system's structure makes it possible to reduce the size of a test set by many orders of magnitude. The practical use of the contribution is to help developers structure a system under construction in such a way that it is completely testable using the described testing method with few additional constraints. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1)What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development. [ABSTRACT FROM AUTHOR]

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and we label the bottom 70% repositories as unpopular. The remaining 10% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features—indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community. [ABSTRACT FROM AUTHOR]

In most areas of computer science (CS), and in the software domain in particular, international conferences are as important as journals as a venue to disseminate research results. This has resulted in the creation of rankings to provide quality assessment of conferences (specially used for academic promotion purposes) like the well-known CORE ranking created by the Computing Research and Education Association of Australasia. In this paper we analyze 102 CORE-ranked conferences in the software area (covering all aspects of software engineering, programming languages, software architectures and the like) included in the DBLP dataset, an online reference for computers science bibliographic information. We define a suite of metrics focusing on the analysis of the co-authorship graph of the conferences, where authors are represented as nodes and co-authorship relationships as edges. Our aim is to first characterize the patterns and structure of the community of researchers in software conferences. We then try to see if these values depend on the quality rank of the conference justifying this way the existence of the different classifications in the CORE-ranking system. [ABSTRACT FROM AUTHOR]

Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We review 136 papers from top conference proceedings and journals in the last decade and summarize 11 research topics prediction models can apply to. Based on the papers, we conclude several big challenges and directions. We believe that the comprehensive taxonomy will help us understand the research area deeper and infer several useful and practical implications. [ABSTRACT FROM AUTHOR]