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The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. The objective of this study is to examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. Users of data sets appreciate high data quality and are discouraged by lack of replicability of data set construction. Data related to machine learning or derived from the manufacturing sector are two suggestions of the respondents for future data papers. Overall, data papers have provided the foundation for a significant number of studies, but there is room for improvement in their utilization. This can be done by setting a higher bar for their publication, by encouraging their use, by promoting open science initiatives, and by providing incentives for the enrichment of existing data collections. [ABSTRACT FROM AUTHOR]

According to the data from the Scopus publication database, as analyzed in several recent studies, more than 70,000 papers have been published in the area of Software Engineering (SE) since late 1960's. According to our recent work, 43% of those papers have received no citations at all. Since citations are the most commonly used metric for measuring research (academic) impact, these figures raise questions (doubts) about the (non-existing) impact of such a large set of papers. It is a reality that typical academic reward systems encourage researchers to publish more papers and do not place a major emphasis on research impact. To shed light on the issue of volume (quantity) versus citation-based impact of SE research papers, we conduct and report in this paper a quantitative bibliometrics assessment in four aspects: (1) quantity versus impact of different paper types (e.g., conference versus journal papers), (2) ratios of uncited (non-impactful) papers, (3) quantity versus impact of papers originating from different countries, and (4) quantity versus impact of papers by each of the top-10 authors (in terms of number of papers). To achieve the above objective, we conducted a quantitative exploratory bibliometrics assessment, comprised of four research questions, to assess quantity versus impact of SE papers with respect to the aspects discussed above. We extracted the data through a systematic, automated and repeatable process from the Scopus paper database, which we also used in two previous papers. Our results show that the distribution of SE publications has a major inequality in terms of impact overall, and also when categorized in terms of the above four aspects. The situation in the SE literature is similar to the other areas of science as studied by previous bibliometrics studies. Also, among our results is the fact that journal articles and conference papers have been cited 12.6 and 3.6 times on average, confirming the expectation that journal articles have more impact, in general, than conference papers. Also, papers originated from English-speaking countries have in general more visibility and impact (and consequently citations) when compared to papers originated from non-English-speaking countries. Our results have implications for improvement of academic reward systems, which nowadays mainly encourage researchers to publish more papers and usually neglect research impact. Also, our results can help researchers in non-English-speaking countries to consider improvements to increase their research impact of their upcoming papers. [ABSTRACT FROM AUTHOR]

Software engineering is a socio-technical endeavor, and while many of our contributions focus on technical aspects, human stakeholders such as software developers are directly affected by and can benefit from our research and tool innovations. In this paper, we question how much of our research addresses human and social issues, and explore how much we study human and social aspects in our research designs. To answer these questions, we developed a socio-technical research framework to capture the main beneficiary of a research study (the who), the main type of research contribution produced (the what), and the research strategies used in the study (how we methodologically approach delivering relevant results given the who and what of our studies). We used this Who-What-How framework to analyze 151 papers from two well-cited publishing venues—the main technical track at the International Conference on Software Engineering, and the Empirical Software Engineering Journal by Springer—to assess how much this published research explicitly considers human aspects. We find that although a majority of these papers claim the contained research should benefit human stakeholders, most focus predominantly on technical contributions. Although our analysis is scoped to two venues, our results suggest a need for more diversification and triangulation of research strategies. In particular, there is a need for strategies that aim at a deeper understanding of human and social aspects of software development practice to balance the design and evaluation of technical innovations. We recommend that the framework should be used in the design of future studies in order to steer software engineering research towards explicitly including human and social concerns in their designs, and to improve the relevance of our research for human stakeholders. [ABSTRACT FROM AUTHOR]

An adaptive system is able to adapt at runtime to dynamically changing environments and to new requirements. Adaptive systems can be single adaptive entities or collective ones that consist of several collaborating entities. Rigorous engineering requires appropriate methods and tools that help guaranteeing that an adaptive system lives up to its intended purpose. This paper introduces the special section on "Rigorous Engineering of Collective Adaptive Systems." It presents the 11 contributions of the section categorizing them into five distinct research lines: correctness by design and synthesis, computing with bio-inspired communication, new system models, machine learning, and programming and analyzing ensembles. [ABSTRACT FROM AUTHOR]

This special issue of Software Tools for Technology Transfer comprises extended versions of selected papers from the 26th edition of the International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2020). The focus of this conference series is tools and algorithms for the rigorous analysis of software and hardware systems, and the papers in this special cover the spectrum of current work in this field. [ABSTRACT FROM AUTHOR]

Background: Assessing and communicating software engineering research can be challenging. Design science is recognized as an appropriate research paradigm for applied research, but is rarely explicitly used as a way to present planned or achieved research contributions in software engineering. Applying the design science lens to software engineering research may improve the assessment and communication of research contributions. Aim: The aim of this study is 1) to understand whether the design science lens helps summarize and assess software engineering research contributions, and 2) to characterize different types of design science contributions in the software engineering literature. Method: In previous research, we developed a visual abstract template, summarizing the core constructs of the design science paradigm. In this study, we use this template in a review of a set of 38 award winning software engineering publications to extract, analyze and characterize their design science contributions. Results: We identified five clusters of papers, classifying them according to their different types of design science contributions. Conclusions: The design science lens helps emphasize the theoretical contribution of research output—in terms of technological rules—and reflect on the practical relevance, novelty and rigor of the rules proposed by the research. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]

Quality requirements deal with how well a product should perform the intended functionality, such as start-up time and learnability. Researchers argue they are important and at the same time studies indicate there are deficiencies in practice. Our goal is to review the state of evidence for quality requirements. We want to understand the empirical research on quality requirements topics as well as evaluations of quality requirements solutions. We used a hybrid method for our systematic literature review. We defined a start set based on two literature reviews combined with a keyword-based search from selected publication venues. We snowballed based on the start set. We screened 530 papers and included 84 papers in our review. Case study method is the most common (43), followed by surveys (15) and tests (13). We found no replication studies. The two most commonly studied themes are (1) differentiating characteristics of quality requirements compared to other types of requirements, (2) the importance and prevalence of quality requirements. Quality models, QUPER, and the NFR method are evaluated in several studies, with positive indications. Goal modeling is the only modeling approach evaluated. However, all studies are small scale and long-term costs and impact are not studied. We conclude that more research is needed as empirical research on quality requirements is not increasing at the same rate as software engineering research in general. We see a gap between research and practice. The solutions proposed are usually evaluated in an academic context and surveys on quality requirements in industry indicate unsystematic handling of quality requirements. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric analysis of the Turkish software engineering (SE) community (researchers and institutions). The bibliometric analysis has been conducted based on the number of papers published in the software-engineering-related venues and indexed in the Scopus academic search engine until year 2014. According to the bibliometric analysis, the top-ranked institution is Middle East Technical University, and the top-ranked scholar is Ayşe Başar Bener (formerly with Boğaziçi University and now with Ryerson University in Canada). The analysis reveals other important findings and presents a set of implications for the Turkish SE community and stakeholders (e.g., funding agencies and decision makers) such as the followings: (1) Turkey produces only about 0.49 % of the world-wide SE knowledge, as measured by the number of papers in Scopus, which is very negligible unfortunately. To take a more active role in the global SE community, the Turkish SE community has to increase their outputs. (2) We notice a lack of diversity in the general SE spectrum, e.g., we noticed very little focus on requirements engineering, software maintenance and evolution, and architecture. This denotes the need to further diversification in SE research topics in Turkey. (3) In total, 89 papers in the pool (30.8 % of the total) are internationally-authored SE papers. Having a good level of international collaborations is a good sign for the Turkish SE community. The highest international collaborations have been with researchers from United States, Canada and Netherlands. (4) In general, the involvement of industry in SE search is low. All stakeholders (e.g., government, industry and academia) should aim at increasing the level of industry-academia collaboration in the Turkish SE community, (5) Citation to Turkish SE papers, in general, are significantly lower than a set of three representative pools of SE papers. This is an area of concern which needs further investigation, and (6) In general, there is a need to increase both the quantity and quality of the Turkish SE papers, in the global stage. The approach we use in this study could be replicated in other countries to provide insights and trends about the SE research performance in other countries. [ABSTRACT FROM AUTHOR]

It has been argued that reporting software engineering experiments in a standardized way helps researchers find relevant information, understand how experiments were conducted and assess the validity of their results. Various guidelines have been proposed specifically for software engineering experiments. The benefits of such guidelines have often been emphasized, but the actual uptake and practice of reporting have not yet been investigated since the introduction of many of the more recent guidelines. In this research, we utilize a mixed-method study design including sequence analysis techniques for evaluating to which extent papers follow such guidelines. Our study focuses on the four most prominent software engineering journals and the time period from 2000 to 2020. Our results show that many experimental papers miss information suggested by guidelines, that no de facto standard sequence for reporting exists, and that many papers do not cite any guidelines. We discuss these findings and implications for the discipline of experimental software engineering focusing on the review process and the potential to refine and extend guidelines, among others, to account for theory explicitly. [ABSTRACT FROM AUTHOR]

Compilers are widely-used infrastructures in accelerating the software development, and expected to be trustworthy. In the literature, various testing technologies have been proposed to guarantee the quality of compilers. However, there remains an obstacle to comprehensively characterize and understand compiler testing. To overcome this obstacle, we propose a literature analysis framework to gain insights into the compiler testing area. First, we perform an extensive search to construct a dataset related to compiler testing papers. Then, we conduct a bibliometric analysis to analyze the productive authors, the influential papers, and the frequently tested compilers based on our dataset. Finally, we utilize association rules and collaboration networks to mine the authorships and the communities of interests among researchers and keywords. Some valuable results are reported. We find that the USA is the leading country that contains the most influential researchers and institutions. The most active keyword is "random testing". We also find that most researchers have broad interests within small-scale collaborators in the compiler testing area. [ABSTRACT FROM AUTHOR]

Open-science initiatives have gained substantial momentum in computer science, and particularly in software-engineering research. A critical aspect of open-science is the public availability of artifacts (e.g., tools), which facilitates the replication, reproduction, extension, and verification of results. While we experienced that many artifacts are not publicly available, we are not aware of empirical evidence supporting this subjective claim. In this article, we report an empirical study on software artifact papers (SAPs) published at the International Conference on Software Engineering (ICSE), in which we investigated whether and how researchers have published their software artifacts, and whether this had scientific impact. Our dataset comprises 789 ICSE research track papers, including 604 SAPs (76.6 %), from the years 2007 to 2017. While showing a positive trend towards artifact availability, our results are still sobering. Even in 2017, only 58.5 % of the papers that stated to have developed a software artifact made that artifact publicly available. As we did find a small, but statistically significant, positive correlation between linking to artifacts in a paper and its scientific impact in terms of citations, we hope to motivate the research community to share more artifacts. With our insights, we aim to support the advancement of open science by discussing our results in the context of existing initiatives and guidelines. In particular, our findings advocate the need for clearly communicating artifacts and the use of non-commercial, persistent archives to provide replication packages. [ABSTRACT FROM AUTHOR]

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]

Software maintenance is a necessary and frequently occurring activity in software engineering. However, different factors such as inadequate documentation, project size, complex dependencies, and hard to understand architecture cause software maintenance to consume a large part of project resources. Therefore, it is important to assist the newcomers by providing program comprehension facilities that can reveal important information about the software system and can speed up the maintenance tasks. This important information about software includes knowledge about the core part (classes, components, design, etc.) of the system that mainly controls its whole functionality. In literature, different researches attempted to determine core part of the software using various structural, dynamic, and network metrics and termed them as key or critical classes. These approaches have an open scope for modeling coupling relations among different elements of software and most of these approaches need human expertise to identify key classes of the software. Moreover, multimedia software systems are generally interface driven and thus many micro level classes collectively constitute macro level units called as multimedia components. Therefore, this paper focuses to identify key critical units of the multimedia software at component level. The proposed approach in this paper consists of three main phases. In the first phase, different features of a class are identified and assigned a coupling based functional score that represents its significance in the overall functionality of the class. In the second phase, different independent components present in the multimedia software are identified by modeling the system as a dependency graph at the class level. Finally, key critical components of the multimedia software are identified by performing hierarchical agglomerative clustering based on the dependency strength among different identified components. The proposed approach is empirically evaluated on open-source multimedia software of different sizes and the obtained results support the feasibility and usability of the proposed approach of this paper. [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]

Provides an overview of selected papers that deals with different aspects of tool data integration techniques from a meta-modeling point of view, presented at the European Software Engineering Conference 2003 Workshop on Tool Integration in System Development, which took place on September 1-2, 2003 in Helsinki, Finland. Survey of model integration and transformation approaches; Suggested systematic approach for the development of the architecture of integrated development environments; List of sessions where the papers were presented.

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]

Presents an introduction to the articles related to software model checking published in the May 2005 issue of the journal "International Journal on Software Tools for Technology Transfer."

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]

Context: The recent research trend has highlighted that multiple stakeholders are involved during requirement gathering in agile software development. Hence, leading to an increased number of duplicate user stories in agile product backlog during requirement gathering. Objective: The objective of this paper is to evaluate the existing techniques employed in identifying and eliminating the duplicate user stories from agile product backlog and to overcome the existing gaps with the help of a newly proposed clustering algorithm. Method: An agile user story is expressed as a function of input and output parameters. That said multiple user stories having similar set of input parameters are most likely to be duplicate causing a redundancy. The newly proposed algorithm is used for clustering user stories having similar set of input parameters through various iterations and then removing the identified duplicate user stories from agile product backlog. This paper also introduces the concept of mass clustering which means clustering a number of user stories in single run. Results: Experimental results prove the proposed model is capable of handling small and large releases ranging between 100 to 1000 user stories with similar efficiency. The proposed clustering algorithm outperformed the clustering algorithms and resulted in 37% decrease in agile product backlog by eliminating duplicate user stories causing redundancy. The experimental results are obtained from the logs of the MATLAB tool. However, the provided algorithm is generic in nature and can be implemented using R, Python or SAS programming tools. The provided algorithms employs proven matrix operations. Conclusion: The proposed clustering algorithm overcomes the limitation of existing user story management methods and clearly out performs when compared with other clustering algorithms. Finally, this paper gives recommendations about the usage of the provided clustering algorithm during agile release planning for eliminating duplicate user stories from agile product backlog. [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]

Software engineering (SE) research should be relevant to industrial practice. There have been regular discussions in the SE community on this issue since the 1980's, led by pioneers such as Robert Glass. As we recently passed the milestone of "50 years of software engineering", some recent positive efforts have been made in this direction, e.g., establishing "industrial" tracks in several SE conferences. However, many researchers and practitioners believe that we, as a community, are still struggling with research relevance and utility. The goal of this paper is to synthesize the evidence and experience-based opinions shared on this topic so far in the SE community, and to encourage the community to further reflect and act on the research relevance. For this purpose, we have conducted a Multi-vocal Literature Review (MLR) of 54 systematically-selected sources (papers and non peer-reviewed articles). Instead of relying on and considering the individual opinions on research relevance, mentioned in each of the sources, the MLR aims to synthesize and provide the "holistic" view on the topic. The highlights of our MLR findings are as follows. The top three root causes of low relevance, discussed in the community, are: (1) Researchers having simplistic views (or wrong assumptions) about SE in practice; (2) Lack of connection with industry; and (3) Wrong identification of research problems. The top three suggestions for improving research relevance are: (1) Using appropriate research approaches such as action-research; (2) Choosing relevant (practical) research problems; and (3) Collaborating with industry. By synthesizing all the discussions on this important topic so far, this paper aims to encourage further discussions and actions in the community to increase our collective efforts to improve the research relevance. Furthermore, we raise the need for empirically-grounded and rigorous studies on the relevance problem in SE research, as carried out in other fields such as management science. [ABSTRACT FROM AUTHOR]

Blockchain has drawn significant attention from researchers for more than a decade. Recently, there has been a series of research works on blockchain technology reported across the globe. However, most of them covered either integration of specific technologies with blockchain, or engrossed all available technological applications in the form of a review. In this work, the goal is to provide a comprehensive insight of the existing literature with a focus on application of software engineering principles in blockchain technology application development. Towards this, a systematic literature review of the documents available on the renowned research database of web of sciences has been done. A comprehensive Citation analysis of 1171 documents was done and further, 15 research papers were selected for detailed content analysis. These documents were investigated in a reasonably detailed manner and findings have been presented. The review also sheds light on various challenges and shows directions for future research. Authrs firmly believe that this systematic literature review shall provide an all- inclusive recapitulation that will benefit the research community to understand the relationship and requirements of software engineering practices vis-a-vis blockchain technology. [ABSTRACT FROM AUTHOR]

This paper presents a model-based approach for converting Common Grid Model Exchange Specification (CGMES) power system models into operational networks. The approach utilizes the Model-Driven Architecture (MDA), borrowed from software engineering, to create a Platform-Independent Model (PIM) saved in a graph database, neo4j. The PIM is then transformed into Platform-Specific Model (PSM) using cypher queries. Finally, from the PSM, a Pandapower network is generated that is used later for running power flow simulations. The Common Information Model (CIM) ontology classes, which form the metamodel behind CGMES, are loaded into the same database, connecting them to the grid instances, allowing for automatic validation and inference. The proposed approach has been successfully applied to large-scale real-world grids that include renewable energy sources. [ABSTRACT FROM AUTHOR]

The systematic review (SR) is a methodology used to find and aggregate all relevant existing evidence about a specific research question of interest. One of the activities associated with the SR process is the selection of primary studies, which is a time consuming manual task. The quality of primary study selection impacts the overall quality of SR. The goal of this paper is to propose a strategy named 'Score Citation Automatic Selection' (SCAS), to automate part of the primary study selection activity. The SCAS strategy combines two different features, content and citation relationships between the studies, to make the selection activity as automated as possible. Aiming to evaluate the feasibility of our strategy, we conducted an exploratory case study to compare the accuracy of selecting primary studies manually and using the SCAS strategy. The case study shows that for three SRs published in the literature and previously conducted in a manual implementation, the average effort reduction was 58.2 % when applying the SCAS strategy to automate part of the initial selection of primary studies, and the percentage error was 12.98 %. Our case study provided confidence in our strategy, and suggested that it can reduce the effort required to select the primary studies without adversely affecting the overall results of SR. [ABSTRACT FROM AUTHOR]

Existing blockchain approaches exhibit a diverse set of dimensions, and on the other hand, IoT-based health care applications manifest a wide variety of requirements. The state-of-the-art analysis of blockchain concerning existing IoT-based approaches for the healthcare domain has been investigated to a limited extend. The purpose of this survey paper is to analyze current state-of-the-art blockchain work in several IoT disciplines, with a focus on the health sector. This study also attempts to demonstrate the prospective use of blockchain in healthcare, as well as the obstacles and future paths of blockchain development. Furthermore, the fundamentals of blockchain have been thoroughly explained to appeal to a diverse audience. On the contrary, we analyzed state-of-the-art studies from several IoT disciplines for eHealth, and also the study deficit but also the obstacles when considering blockchain to IoT, which are highlighted and explored in the paper with suggested alternatives. [ABSTRACT FROM AUTHOR]

Software quality assurance aims to ensure that the software product meets the quality standards expected by the customer. This special issue of Software Tools for Technology Transfer is concerned with the foundations on which software quality assurance is built. It introduces the papers that focus on this topic and that have been selected from the 20th International Conference on Fundamental Approaches to Software Engineering (FASE’17). [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Aysu Betin Can, Tevfik Bultan, and Benjamin Lux on eliminating synchronization faults in air traffic control software thru design for verification with concurrency controllers and another by Ana Milanova on compositional inference of UML class diagrams.

Requirements engineering (RE) has attracted a great deal of attention from researchers and practitioners in recent years. Requirements engineering education (REE) is therefore an important undertaking if the field is to have professionals who are capable of successfully accomplishing software projects. This increasing interest demands that academia should provide software engineering students with a solid foundation in the subject matter. This paper aims to identify and to present the current research on REE that is available at present, and to select useful approaches and needs for future research. A systematic mapping study was therefore performed to classify the selected studies into five classification criteria: research type, empirical type, contribution type, RE activity, and curricula. A total of 79 papers were selected and classified according to these criteria. The results of this systematic mapping study are discussed, and a list of advice obtained from the REE literature for instructors is provided. [ABSTRACT FROM AUTHOR]

Model-driven software engineering and specifically domain-specific languages have contributed to improve the quality of software and the efficiency in the development of software. However, the design and implementation of domain-specific languages requires still an enormous investment. Language workbenches are the most important tools in the field of software language engineering. The introduction of language workbenches has alleviated partly the development effort, but there are still a few major challenges that need to be tackled. This paper presents a personal perspective on the development of tools for language engineering and language workbenches in particular and future challenges to be tackled. [ABSTRACT FROM AUTHOR]

Big data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platforms that can address their specific objectives and requirements. To minimise costs, such platforms also need to co-exist with existing IT infrastructures and reuse knowledge and resources already accumulated within the organisation. To address such needs, this paper proposes the Data Analytics Solution Engineering (DASE) framework—a knowledge-driven approach supported by semantic web technologies for requirements engineering, design and development of new data analytics platforms. It includes a meta-model that captures data analytics platform requirements via a Knowledge Base, a set of guidelines that organisations can follow in engineering data analytics platforms and a reference architecture that demonstrates how to use these guidelines. We evaluate the DASE framework through two case studies and demonstrate how it can facilitate knowledge-based and requirements-driven data analytics platform engineering. The resulting data analytics platforms are observed to be user friendly, easy to maintain and flexible in handling changes to requirements. This work contributes to the body of knowledge in knowledge-driven requirements engineering, and data analytics platform engineering by providing a meta-model and a reference architecture that can be tailored to different analytics application domains. [ABSTRACT FROM AUTHOR]

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research. [ABSTRACT FROM AUTHOR]

This special issue of Software Tools for Technology Transfer presents extended versions of two selected papers from the 23rd edition of TACAS, the International Conference on Tools and Algorithms for the Construction and Analysis of Systems that took place in April 2017 in Uppsala. The papers included in this special issue concern various aspects of automated design and formal verification; they therefore contribute to the development of more reliable computer systems. [ABSTRACT FROM AUTHOR]

'On the Mapping of Genotype to Phenotype in Evolutionary Algorithms,' by Peter A. Whigham, Grant Dick, and James Maclaurin, is a welcome reminder that evolutionary computation practitioners should be wary of taking their biological analogies too seriously. But more importantly, it is a reminder to practitioners to consider carefully their representations and operators, rather than blindly implementing a biological analogy without sufficient attention to the constraints of software engineering. 'It works in biology, so it should work in EC' is poor, even lazy, software design. The primary contribution of this paper is exactly what a commentary should be: to (re)ignite discussions about how biological inspiration should inform EC practice. [ABSTRACT FROM AUTHOR]

Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set. The increasing adoption of MLSs in safety critical domains such as autonomous driving, healthcare, and finance has fostered much attention towards the quality assurance of such systems. Despite the advances in software testing, MLSs bring novel and unprecedented challenges, since their behaviour is defined jointly by the code that implements them and the data used for training them. Objective: To identify the existing solutions for functional testing of MLSs, and classify them from three different perspectives: (1) the context of the problem they address, (2) their features, and (3) their empirical evaluation. To report demographic information about the ongoing research. To identify open challenges for future research. Method: We conducted a systematic mapping study about testing techniques for MLSs driven by 33 research questions. We followed existing guidelines when defining our research protocol so as to increase the repeatability and reliability of our results. Results: We identified 70 relevant primary studies, mostly published in the last years. We identified 11 problems addressed in the literature. We investigated multiple aspects of the testing approaches, such as the used/proposed adequacy criteria, the algorithms for test input generation, and the test oracles. Conclusions: The most active research areas in MLS testing address automated scenario/input generation and test oracle creation. MLS testing is a rapidly growing and developing research area, with many open challenges, such as the generation of realistic inputs and the definition of reliable evaluation metrics and benchmarks. [ABSTRACT FROM AUTHOR]

We define perceived diversity as the diversity factors that individuals are born with. Perceived diversity in Software Engineering has been recognized as a high-value team property and companies are willing to increase their efforts to create more diverse work teams. The current diversity state-of-the-art shows that gender diversity studies have been growing during the past decade, and they have shown the benefits of including women in software teams. However, less is known about how other perceived diversity factors such as race, nationality, disability, and age of developers are related to Software Engineering. Through a systematic literature review, we aim to clarify the research area concerned with perceived diversity in Software Engineering. Our goal is to identify (1) what issues have been studied and what results have been reported; (2) what methods, tools, models, and processes have been proposed to help perceived diversity issues; and (3) what limitations have been reported when studying perceived diversity in Software Engineering. Furthermore, our ultimate goal is to identify gaps in the current literature and create a call for future action in perceived diversity in Software Engineering. Our results indicate that the individual studies have typically had a gender diversity perspective focusing on showing gender bias or gender differences instead of developing methods and tools to mitigate the gender diversity issues faced in SE. Moreover, perceived diversity aspects related to SE participants’ race, age, and disability need to be further analyzed in Software Engineering research. From our systematic literature review, we conclude that researchers need to consider a wider set of perceived diversity aspects for future research. [ABSTRACT FROM AUTHOR]

This article provides a summary of the current state of the Software & Systems Modeling (SoSyM) journal in 2023. It highlights the publication of new papers and the addition of new editors to the journal. The retirement of two esteemed editors is also mentioned, with gratitude expressed for their contributions. The article includes statistics on the number of papers published, the impact factor of the journal, and the number of submissions and downloads. It announces the recipients of the journal's ten-year most influential paper awards and lists the papers presented at the MODELS conference. The article concludes by expressing appreciation to the reviewers for their contributions. Additionally, a separate document is a message of gratitude and recognition from the software and systems modeling community. The community expresses their appreciation to all the reviewers who have assisted the modeling community and announces the recipients of the Best Reviewers of 2023 award. The document also provides a list of all the reviewers who contributed their expertise to the journal over the past year. The content of the current issue is briefly mentioned, and the community wishes everyone a joyful New Year and encourages readers to explore the journal's archive. [Extracted from the article]

Models are the key tools humans use to manage complexity in description, development, and analysis. This applies to all scientific and engineering disciplines and in particular to the development of software and data-intensive systems. However, different methods and terminologies have become established in the individual disciplines, even in the sub-fields of Informatics, which raises the need for a comprehensive and cross-sectional analysis of the past, present, and future of modeling research. This paper aims to shed some light on how different modeling disciplines emerged and what characterizes them with a discussion of the potential toward a common modeling future. It focuses on the areas of software, data, and process modeling and reports on an analysis of the research approaches, goals, and visions pursued in each, as well as the methods used. This analysis is based on the results of a survey conducted in the communities concerned, on a bibliometric study, and on interviews with a prominent representative of each of these communities. The paper discusses the different viewpoints of the communities, their commonalities and differences, and identifies possible starting points for further collaboration. It further discusses current challenges for the communities in general and modeling as a research topic in particular and highlights visions for the future. [ABSTRACT FROM AUTHOR]

Automated software transformations and the process of automated program repair and improvement are an important aspect of modern software engineering practices. In this paper, we describe a system which uses a graph-based deep learning model that can be trained to automatically transform and improve computer programs. By operating on language-agnostic, universal graph-like structures easily extractable from source code files (abstract syntax trees), the deep learning agent learns which transformations should be effectively applied to various structures recognized in the source code in order to improve it. By defining a metric which we want to improve and introducing an optimization task—a reinforcement learning setting, an agent learns to automatically apply a chain of transformations to the program, drastically improving it. While similar program improvement processes exist, they exclusively use exhaustive search algorithms to try all the possible code transformations which is a long process susceptible to local optimum issues. Our solution aims to model and embed structural knowledge about the programs being transformed which greatly helps the agent to choose best possible code transformations to apply. Elements of the approach we present in this paper are further applicable not just to automatic software improvement tasks, but also to other code-related tasks. [ABSTRACT FROM AUTHOR]

Software quality is becoming a momentous challenge in software engineering processes, and software testing has a pivotal role in its measurements. Nature-inspired metaheuristic methods play an essential role in software testing, in which various studies have been conducted in this field; however, due to a lack of wide-ranging papers reviewing these methods, conducting a comprehensive systematic review to examine an array of crucial mechanisms in this field has become a necessity. This study aims to present a detailed analysis and taxonomically classifies the metaheuristic methods inspired by nature. This paper compromises a systematic literature review of 65 chosen studies published between 2015 and 2022. Genetic algorithm-based, hybrid, ant colony optimization-based, cuckoo search-based, firefly algorithm-based, artificial bee colony-based, and other metaheuristic methods constitute this systematic study's stratification. Evaluation methods, applied tools, merits, and demerits of each reviewed article are investigated. Additionally, future directions and open issues are addressed. This conducted paper not only expounds on software testing strengths, open issues, and future works, but also recognizes the quest for optimizing the insufficient metrics in software testing, such as mutation score, complexity, and scalability, which would be the propulsion of the testing process if consummated. [ABSTRACT FROM AUTHOR]

An inherent problem in software engineering is that competing prediction systems have been found to produce conflicting results. Yet accurate prediction is crucial because the complexity and quality of software requirements have dramatically changed in recent years, and consumers have become considerably more demanding in terms of the cost, timeframe, and quality of software solutions. Moreover, these variables may also be in direct conflict and can only be resolved by the optimum development of software by using reliable software engineering strategies. In this paper, a novel method based on the integration of the mine blast algorithm (MBA) and the simulated annealing (SA) algorithm is used to create input connection weights and biases for a back propagation neural network (BPNN) for the purpose of addressing the software fault prediction problem (SFP). The aim of hybridizing the MBA and SA is to find a way to efficiently explore and manipulate the search space. The proposed MBA-SA was tested on 18 datasets for SFP. The results indicated that the MBA-SA outperformed the MBA on all datasets. These results were subjected to additional statistical validity, boxplot distribution, and convergence analysis. Furthermore, a comparative evaluation of MBA-SA against twenty state-of-the-art methods for various output metrics was performed, and the result indicated that the hybrid MBA-SA outperformed most other state-of-the-art methods in the majority of datasets. [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]

Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world problems, thanks to recent breakthroughs in artificial intelligence (AI) and machine learning (ML). However, machine learning meets software engineering not only with promising potentials but also with some inherent challenges. Despite some recent research efforts, we still do not have a clear understanding of the challenges of developing ML-based applications and the current industry practices. Moreover, it is unclear where software engineering researchers should focus their efforts to better support ML application developers. In this paper, we report about a survey that aimed to understand the challenges and best practices of ML application development. We synthesize the results obtained from 80 practitioners (with diverse skills, experience, and application domains) into 17 findings outlining challenges and best practices for ML application development. Practitioners involved in the development of ML-based software systems can leverage the summarized best practices to improve the quality of their system. We hope that the reported challenges will inform the research community about topics that need to be investigated to improve the engineering process and the quality of ML-based applications. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a research topic that gained momentum in the Industry 4.0 era. The goal of DT is to create a virtual real-time intelligent system that is a typical twin of the physical system. DT provides analysis, prognosis, planning, and rapid response when needed. Digital shadow (DS) is an artifact concept of DT that provides a real-time replica of the physical system. Information in a DS is passed in one direction only, from the physical system to the virtual one. While in DT, the information goes in both directions. The definition and roles of DS and DT are overlapping. However, DS can be defined as the main component of a DT system. In this paper, DS roles are specified. Based on these roles, A DS framework architecture is proposed, and the communication system between its components. The proposed framework design is built using distributed system concepts such as event-driven architecture, microservices, and containerization. These concepts are well defined and utilized in the software engineering domain. The originality of the proposed framework is the definition of a systematic approach for designing and integrating digital models (DMs) from different vendors and domains. An experiment is designed to prove the framework's ability to shadow a physical system in real-time. Multiple DMs are implemented and deployed on the proposed DS framework. These DMs are used to shadow a natural gas compressor system. Experimental results prove the practicality of our proposed DS framework to operate in real-time. [ABSTRACT FROM AUTHOR]

Model-driven software engineering (MDSE) is an established approach for developing complex software systems. The unified modelling language (UML) is one of the most used model languages for applying the MDSE approach. UML has 14 diagram types that describe different perspectives of a software system under development. These diagrams are strongly dependent on each other and must be consistent with one another. The main objectives of this paper are as follows: (1) to understand (i) how aware experts are of model consistency issues and (ii) how relevant these issues are to experts, in order to understand model consistency in the MDSE/UML contexts, and more importantly, (2) to validate a set of 116 UML consistency rules that was systematically collected from the literature, so as to identify the rules that should always be enforced. We conducted a personal opinion survey with 106 experts in SE and MDSE, by means of an online questionnaire. The survey results describe an overview of how the topic of MDSE/UML consistency is handled by experts in the field. In addition, this survey identified a set of 52 UML consistency rules which should always be checked in every UML diagram. The majority of these 52 rules were understood by the majority of respondents and are general-purpose rules that are involved in the Design software development phase. This subset of 52 rules could be considered to be (1) added to the UML standard, (2) used as a reference to researchers who study UML/MDSE, and (3) used as a practical example for teaching purposes. [ABSTRACT FROM AUTHOR]

This special issue of I Formal Methods in System Design i is dedicated to the memory of Edmund Melson Clarke, Jr., who sadly passed away on December 22, 2020. The authors of the six papers included in this special issue have been at some point Ed's students, postdocs, or close collaborators: Sagar Chaki was a PhD student with Ed; Natasha Sharygina was Ed's close collaborator while she was based at Carnegie Mellon's Software Engineering Institute; Armin Biere was a postdoc with Ed; Doron Peled was a co-author with Ed and Orna Grumberg, co-editor of this special issue, of the first and most popular book on model checking; Somesh Jha was a PhD student with Ed; Finally, Alessandro Cimatti was a co-developer with Ed, Biere et al., on bounded model checking and a part of the Trento-Carnegie Mellon collaboration to develop the NuSMV model checker. Ed was instrumental in creating a research community in the field of formal verification. [Extracted from the article]

App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis. [ABSTRACT FROM AUTHOR]