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Research has repeatedly shown that high-quality requirements are essential for the success of development projects. While the term “quality” is pervasive in the field of requirements engineering and while the body of research on requirements quality is large, there is no meta-study of the field that overviews and compares the concrete quality attributes addressed by the community. To fill this knowledge gap, we conducted a systematic mapping study of the scientific literature. We retrieved 6905 articles from six academic databases, which we filtered down to 105 relevant primary studies. The primary studies use empirical research to explicitly define, improve, or evaluate requirements quality. We found that empirical research on requirements quality focuses on improvement techniques, with very few primary studies addressing evidence-based definitions and evaluations of quality attributes. Among the 12 quality attributes identified, the most prominent in the field are ambiguity, completeness, consistency, and correctness. We identified 111 sub-types of quality attributes such as “template conformance” for consistency or “passive voice” for ambiguity. Ambiguity has the largest share of these sub-types. The artefacts being studied are mostly referred to in the broadest sense as “requirements”, while little research targets quality attributes in specific types of requirements such as use cases or user stories. Our findings highlight the need to conduct more empirically grounded research defining requirements quality, using more varied research methods, and addressing a more diverse set of requirements types.

Machine Learning (ML) algorithms have been increasingly replacing people in several application domains—in which the majority suffer from data imbalance. In order to solve this problem, published studies implement data preprocessing techniques, cost-sensitive and ensemble learning. These solutions reduce the naturally occurring bias towards the majority sample through ML. This study uses a systematic mapping methodology to assess 9927 papers related to sampling techniques for ML in imbalanced data applications from 7 digital libraries. A filtering process selected 35 representative papers from various domains, such as health, finance, and engineering. As a result of a thorough quantitative analysis of these papers, this study proposes two taxonomies—illustrating sampling techniques and ML models. The results indicate that oversampling and classical ML are the most common preprocessing techniques and models, respectively. However, solutions with neural networks and ensemble ML models have the best performance—with potentially better results through hybrid sampling techniques. Finally, none of the 35 works apply simulation-based synthetic oversampling, indicating a path for future preprocessing solutions.

While gamification is gaining ground in business, marketing, corporate management, and wellness initiatives, its application in education is still an emerging trend. This article presents a study of the published empirical research on the application of gamification to education. The study is limited to papers that discuss explicitly the effects of using game elements in specific educational contexts. It employs a systematic mapping design. Accordingly, a categorical structure for classifying the research results is proposed based on the extracted topics discussed in the reviewed papers. The categories include gamification design principles, game mechanics, context of applying gamification (type of application, educational level, and academic subject), implementation, and evaluation. By mapping the published works to the classification criteria and analyzing them, the study highlights the directions of the currently conducted empirical research on applying gamification to education. It also indicates some major obstacles and needs, such as the need for proper technological support, for controlled studies demonstrating reliable positive or negative results of using specific game elements in particular educational contexts, etc. Although most of the reviewed papers report promising results, more substantial empirical research is needed to determine whether both extrinsic and intrinsic motivation of the learners can be influenced by gamification.

Groundbreakingly, blockchain technology (BCT) has gained widespread acceptance and importance in the last few years. Implemented in different areas of applications such as social and legal industries, finance, smart property, and supply chain networks. This technology assures immutability and integrity of data without the need of a third trusted party. Furthermore, BCT could guarantee a transparent and decentralized transaction system in businesses and industries. Even though general research has been done in the BCT, however, there is a lack of systematic analysis on current research challenges regarding how BCT is effectively applicable in supply chain management (SCM). A systematic literature review (SLR) of SCM based on blockchain does not exist yet. This work aims to explore and analyse the state-ofthe-art on the BCT applications for SCM. We synthesize existing evidence, and identify gaps, available in the literature. The survey uses a systematic mapping study (SMS) method to examine 40 extracted primary studies from scientific databases.

Over the last two decades, much attention has been paid to the area of goal-oriented requirements engineering (GORE), where goals are used as a useful conceptualization to elicit, model, and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within requirements engineering (RE) and beyond, such as agent orientation, aspect orientation, business intelligence, model-driven development, and security. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. In this work, we present a systematic mapping study, covering the 246 top-cited GORE-related conference and journal papers, according to Scopus. Our literature map addresses several research questions: we classify the types of papers (e.g., proposals, formalizations, meta-studies), look at the presence of evaluation, the topics covered (e.g., security, agents, scenarios), frameworks used, venues, citations, author networks, and overall publication numbers. For most questions, we evaluate trends over time. Our findings show a proliferation of papers with new ideas and few citations, with a small number of authors and papers dominating citations; however, there is a slight rise in papers which build upon past work (implementations, integrations, and extensions). We see a rise in papers concerning adaptation/variability/evolution and a slight rise in case studies. Overall, interest in GORE has increased. We use our analysis results to make recommendations concerning future GORE research and make our data publicly available.

Financial inclusion is an international policy agenda and can be achieved through financially literate people, who can make informed financial decisions and improve individuals' well-being. The area of Financial Literacy and Financial Inclusion is fairly highlighted in the literature; however, the collective importance of how these two areas are researched together needs scholarly attention. This paper carries out a mapping, scientometric and content analysis by compiling studies at the intersection of financial literacy and financial inclusion from a sample of 10,091 studies spread over the last 45 years and conducted on a sample of more than 850,000 individuals worldwide. We find that the number of studies increases; by fields, Finance and Economics dominate the literature; by countries, most studies come from developed countries, in particular the US; by authors, citations are skewed and by measures; studies are moving from non-functional measures to functional measures. Overall, the interest in financial literacy in bringing financial inclusion and its multifaceted role is elaborated using conceptual framework following which future research is positioned. Thus, aiding policymakers, regulators, and academicians to know the distinction of Financial literacy in Financial inclusion and to identify the potential research areas.

The OMG standard Systems Modeling Language (SysML) has been on the market for about thirteen years. This standard is an extended subset of UML providing a graphical modeling language for designing complex systems by considering software as well as hardware parts. Over the period of thirteen years, many publications have covered various aspects of SysML in different research fields. The aim of this paper is to conduct a systematic mapping study about SysML to identify the different categories of papers, (i) to get an overview of existing research topics and groups, (ii) to identify whether there are any publication trends, and (iii) to uncover possible missing links. We followed the guidelines for conducting a systematic mapping study by Petersen et al. (Inf Softw Technol 64:1–18, 2015) to analyze SysML publications from 2005 to 2017. Our analysis revealed the following main findings: (i) there is a growing scientific interest in SysML in the last years particularly in the research field of Software Engineering, (ii) SysML is mostly used in the design or validation phase, rather than in the implementation phase, (iii) the most commonly used diagram types are the SysML-specific requirement diagram, parametric diagram, and block diagram, together with the activity diagram and state machine diagram known from UML, (iv) SysML is a specific UML profile mostly used in systems engineering; however, the language has to be customized to accommodate domain-specific aspects, (v) related to collaborations for SysML research over the world, there are more individual research groups than large international networks. This study provides a solid basis for classifying existing approaches for SysML. Researchers can use our results (i) for identifying open research issues, (ii) for a better understanding of the state of the art, and (iii) as a reference for finding specific approaches about SysML.

Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders because it provides brain biomarkers. However, only highly trained doctors can interpret EEG signals due to its complexity. Machine learning has been successfully trained with EEG signals for classifying mental disorders, but a time consuming and disorder-dependant feature engineering (FE) and subsampling process is required over raw EEG data. Deep Learning (DL) is positioned as a prominent research field to process EEG data because (i) it features automated FE by taking advantage of raw EEG signals improving results and (ii) it can be trained over the vast amount of data generated by EEG. In this work, a systematic mapping study has been performed with 46 carefully selected primary studies. Our goals were (i) to provide a clear view of which are the most prominent study topics in diagnosis and prognosis of mental disorders by using EEG with DL, and (ii) to give some recommendations for future works. Some results are: epilepsy was the predominant mental disorder present in around half of the studies, convolutional neural networks also appear in approximate 50% of the works. The main conclusions are (i) processing EEG with DL to detect mental disorders is a promising research field and (ii) to objectively compare performance between studies: public datasets, intra-subject validation, and standard metrics should be used. Additionally, we suggest to pay more attention to ease the reproducibility, and to use (when possible) an available framework to explain the results of the created DL models.

Domain-specific languages (DSL) are programming or modeling languages devoted to a given application domain. There are many tools used to support the implementation of a DSL, making hard the decision-making process for one or another. In this sense, identifying and mapping their features is relevant for decision-making by academic and industrial initiative on DSL development. Objective: The goal of this work is to identify and map the tools, Language Workbenches (LW), or frameworks that were proposed to develop DSLs discussed and referenced in publications between 2012 and 2019. Method: A Systematic Mapping Study (SMS) of the literature scoping tools for DSL development. Results: We identified 59 tools, including 9 under a commercial license and 41 with non-commercial licenses, and analyzed their features from 230 papers. Conclusion: There is a substantial amount of tools that cover a large number of features. Furthermore, we observed that usually, the developer adopts one type of notation to implement the DSL: textual or graphical. We also discuss research gaps, such as a lack of tools that allow meta-meta model transformations and that support modeling tools interoperability.

ContextCode review is a widely used technique of systematic examination of code changes which aims at increasing software quality. Code reviews provide several benefits for the project, including finding bugs, knowledge transfer, and assurance of adherence to project guidelines and coding style. However, code reviews have a major cost: they can delay the merge of the code change, and thus, impact the overall development process. This cost can be even higher if developers do not understand something, i.e., when developers face confusion during the code review.ObjectiveThis paper studies the phenomenon of confusion in code reviews. Understanding confusion is an important starting point to help reducing the cost of code reviews and enhance the effectiveness of this practice, and hence, improve the development process.MethodWe conducted two complementary studies. The first one aimed at identifying the reasons for confusion in code reviews, its impacts, and the coping strategies developers use to deal with it. Then, we surveyed developers to identify the most frequently experienced reasons for confusion, and conducted a systematic mapping study of solutions proposed for those reasons in the scientific literature.ResultsFrom the first study, we build a framework with 30 reasons for confusion, 14 impacts, and 13 coping strategies. The results of the systematic mapping study shows 38 articles addressing the most frequent reasons for confusion. From those articles, we found 13 different solutions for confusion proposed in the literature, and five impacts were established related to the most frequent reasons for confusion.ConclusionsBased on the solutions identified in the mapping study, or the lack of them, we propose an actionable guideline for developers on how to cope with confusion during code reviews; we also make several suggestions how tool builders can support code reviews. Additionally, we propose a research agenda for researchers studying code reviews.