Search

(1) Background: A strategic approach to managing and evolving the bus fleet is essential for optimizing public transportation, improving service efficiency, meeting future demands, and incorporating environmentally friendly technologies. However, country-specific data on fleet composition and evolution are often lacking despite their importance in evaluating technological impacts on the environment, passenger comfort, and driver working conditions. This report describes the creation of the inventory of the Swiss bus fleet and analyzes technological developments in bus vehicles from 1940 to 2022. (2) Methods: We collected the data through a comprehensive mapping study and validated it with Swiss bus companies and experts from the Federal Office of Transport. Vehicle approval forms were used to extract technical data. Buses were sorted by brand and model. For each bus model, 58 technical characteristics were documented in a database. A latent class analysis (LCA) was conducted to classify the buses according to their technological characteristics. (3) Results: The inventory comprises 891 bus models, classified into four groups representing different generations. The predominance of thermal buses and the emergence of hybrid and electric buses since 2010 were noticed. (4) Conclusion: This inventory tracks bus technology evolution and highlights potential implications for driver occupational exposure and environmental impact assessment. [ABSTRACT FROM AUTHOR]

Summary: Software architecture assists developers in addressing non‐functional requirements and in maintaining, debugging, and upgrading their software systems. Consequently, consistency between the designed architecture and the implemented software system itself is important; without this consistency the non‐functional requirements targeted may not be addressed and architectural documentation may mis‐direct maintenance efforts that target the associated code‐base. But often, when software is initially implemented or subsequently evolved, the designed architecture and software architecture become inconsistent, with the implemented structure degraded due to issues like developer time‐pressures, or ambiguous communication of the designed architecture. In such cases, Software Architecture Recovery (SAR) or consistency approaches can be applied to reconstruct the architecture of the software system and possibly to compare it to/re‐align it with the designed architecture. Many SAR approaches have been proposed in the research. However, choosing an appropriate architecture recovery approach for software systems is still an open issue. Consequently, this research aims to conduct a tertiary‐mapping study based on available secondary studies of architecture recovery approaches, to uncover important characteristics, towards the selection of appropriate SAR approaches. This research has aggregated 13 secondary studies and 10 primary studies beyond 2020 from 5 databases and, in doing so, identified 111 architecture recovery approaches. Based on these approaches, a taxonomy, containing nine main SAR‐selection categories is proposed and a framework (in the form of a supporting tool to help developers select an appropriate SAR approach) has been developed. Finally, this research identifies six potential open research gaps related to the underlying research that could be helpful for guiding research in the future. [ABSTRACT FROM AUTHOR]

Web services (WS) are the preferred approach in realizing the service-oriented computing paradigm. However, this comes with challenges such as complexity and uncertainty that hinder their practical application. Bayesian networks (BNs) are one of the techniques used to address these challenges. The objective of this mapping study was to determine what is known about the use of Bayesian networks in web services research. To do this, we identified and selected rigorously 69 articles (out of the 532 identified) published on the subject in 2001–2021. We then classified and analyzed these articles by Web service themes (Service Composition, Service Management, Service Engineering), Objectives (Description, Prediction, Prescription), Types of BN (Basic, Combined, Extended), and Evaluation methods (Proof of concept, Experiment, No evaluation). In doing so, we hope to provide a clear understanding of the subject. We also identify and suggest avenues for future research. Thus, the review results can help researchers and practitioners interested by the application of BNs in WS research. [ABSTRACT FROM AUTHOR]

Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development.

The COVID-19 outbreak has affected numerous facets of human life. It is still an ongoing epidemic with multiple variants emerged since its first outbreak. To detect COVID-19 machine learning and the Internet of Things are the most widely proposed methods in the literature showing favorable performance. This paper presents a complete mapping and assessment of recent research efforts on COVID-19 detection using both machine learning techniques and the Internet of Things devices. The objective of this research work is to identify machine learning techniques and IoT devices that are used in combination to effectively detect COVID-19 and its variants. The synthesis of the mapping study is provided as the analysis of the IoT devices, and the effectiveness of machine learning techniques in disease detection. This study will help the researchers and health practitioners in deploying an effective method for detecting COVID-19, and its variants using machine learning techniques and IoT devices. [ABSTRACT FROM AUTHOR]

Abstract Mobile apps with tested Graphical User Interface (GUI) tend to have higher downloads in the apps store. In recent years, few efforts were made to analyse the research community and research status of the literature for GUI testing on mobile apps, which brings an obstacle to characterise and understand this field. In this study, the authors propose a systematic mapping study to gain insights into the field. First, the authors conduct an extensive search of relevant literature over seven popular digital libraries. From 4427 candidate studies, 114 primary studies published between January 2011 and September 2022 were selected. Next, the authors analyse these primary studies from the perspectives of bibliometric and qualitative analysis. For the bibliometric analysis, first, the authors analyse the popular research topics and their relationships. Second, the authors study the authors' community. For the qualitative analysis, the authors analyse the objectives, approaches and evaluation metrics employed in these primary studies. Their investigation reports several major findings: (1) there are relatively more studies on two topics, that is, test case generation and the automated test; (2) the most productive authors tend to collaborate and often have relatively broad research interests; (3) the functionality is the main objective of GUI testing; the model‐based approach is the most widely used.

This national audit involving Ministry of Health (MOH) Hospitals aimed to map out the levels of development of palliative care services and essential palliative medication availability in Malaysia. An online survey coupled with manual follow up was conducted throughout all MOH hospitals in Malaysia. Data collected described elements of the palliative care service(PCS) based on the WHO public health model. Data was computed using a novel matrix to determine three key indices which were the 1) palliative care development score (PCDS), 2)essential medications availability score (EMAS) and 3) opioid availability score (OAS). These scores then allowed mapping of PCS according to scores of 1–4 (1=least developed, 4=most developed). Out of all 140 MOH hospitals 88.6% (124) completed the PCDS survey, 120(85.7%) for the EMAS survey and 140 (100%) for the OAS survey. A total of 32(25.8%) hospitals had formal PCS with 8(25%) having resident palliative physicians (RPP), 8(25%) visiting palliative physicians(VPP) and 16(50%) no palliative physician (NPP). Out of these services, 17 (53%) had dedicated palliative care beds. In the PCDS survey, hospitals with PCS had significantly higher mean PCDS of 2.59 compared to 1.02 for non-PCS hospitals (P <0.001). The EMAS survey showed 109(90.8%) hospitals had EMAS of four and the OAS survey showed that 135(96.4%) hospitals had oral morphine available. This study shows that palliative care service development in MOH hospitals is still very limited however, majority of MOH hospitals in Malaysia have all the essential medications and oral morphine available. [ABSTRACT FROM AUTHOR]

Objective: There is limited evidence for mapping clinical tools to preferencebased generic tools in the Chinese thyroid cancer patient population. The current study aims to map the FACT-H&N (Functional Assessment of Cancer Therapy-Head and Neck Cancer) to the SF-6D (Short Form Six-Dimension), which will inform future cost-utility analyses related to thyroid cancer treatment. Methods: A total of 1050 participants who completed the FACT-H&N and SF-6D questionnaires were included in the analysis. Four methods of direct and indirect mapping were estimated: OLS regression, Tobit regression, ordered probit regression, and beta mixture regression. We evaluated the predictive performance in terms of root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the correlation between the observed and predicted SF-6D scores. Results: The mean value of SF-6D was 0.690 (SD = 0.128). The RMSE values for the fivefold cross-validation as well as the 30% random sample validation for multiple models in this study were 0.0833-0.0909, MAE values were 0.0676- 0.0782, and CCC values were 0.6940-0.7161. SF-6D utility scores were best predicted by a regression model consisting of the total score of each dimension of the FACT-H&N, the square of the total score of each dimension, and covariates including age and gender. We proposed to use direct mapping (OLS regression) and indirect mapping (ordered probit regression) to establish a mapping model of FACT-H&N to SF-6D. The mean SF-6D and cumulative distribution functions simulated from the recommended mapping algorithm generally matched the observed ones. Conclusions: In the absence of preference-based quality of life tools, obtaining the health status utility of thyroid cancer patients from directly mapped OLS regression and indirectly mapped ordered probit regression is an effective alternative. [ABSTRACT FROM AUTHOR]

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection (AD). However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, a large portion of the existing systematic mapping studies on AD primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, the related literature do not cover the challenges involved in using ML for AD in industrial machinery within the context of the IoT ecosystems. Therefore, this paper presents a systematic mapping study on AD for industrial machinery using IoT devices and ML algorithms to address this gap. Our primary objective is to investigate the use of ML models for anomaly detection within an industrial setting, particularly within IoT ecosystems. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of AD research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities.

In the world of microservices, companies must be able to create systems that operate in the most efficient way. To achieve this, anti-patterns must be avoided because of their detriment to the quality of the system. Some of the most troubling anti-patterns are hard to detect because of their appearance at runtime. Effectively removing anti-patterns from a system requires dynamic analysis because of the large size of microservice-based systems. While the detection of anti-patterns is helpful, being able to visualize them offers a great benefit to companies working with microservices. Seeing how the overall system is flowing and recognizing the existence of anti-patterns can help improve microservice-based systems. In this paper, a systematic mapping study was performed to find the current state of research on visualizing anti-patterns in microservices from the dynamic perspective. Several hundred papers were examined and a total of 31 were found to be relevant to the research topic. The papers, when analyzed, revealed that there are mechanisms to detect anti-patterns at runtime in microservices, and there are also mechanisms for visualizing the architecture of a microservice-based system. This study’s findings could help to identify and remove anti-patterns that occur during runtime in microservices, as well as a means of visualizing these anti-patterns.

The advancement in blockchain applications in the smart sustainable city infrastructure is evaluated in this paper through a comprehensive mapping review. The evaluation is carried out by posing four research questions that address current developments in blockchain technology in the context of smart cities and point out areas where additional study is needed. This study also includes a scoping of blockchain applications in a smart city context to highlight the obstacles to incorporating blockchain technology into smart city infrastructure. Finally, some suggestions for overcoming the problems of incorporating blockchain technology with smart city infrastructure are offered. This research will help researchers and policymakers gain a better understanding of blockchain applications in smart cities.

Mobile apps with tested Graphical User Interface (GUI) tend to have higher downloads in the apps store. In recent years, few efforts were made to analyse the research community and research status of the literature for GUI testing on mobile apps, which brings an obstacle to characterise and understand this field. In this study, the authors propose a systematic mapping study to gain insights into the field. First, the authors conduct an extensive search of relevant literature over seven popular digital libraries. From 4427 candidate studies, 114 primary studies published between January 2011 and September 2022 were selected. Next, the authors analyse these primary studies from the perspectives of bibliometric and qualitative analysis. For the bibliometric analysis, first, the authors analyse the popular research topics and their relationships. Second, the authors study the authors' community. For the qualitative analysis, the authors analyse the objectives, approaches and evaluation metrics employed in these primary studies. Their investigation reports several major findings: (1) there are relatively more studies on two topics, that is, test case generation and the automated test; (2) the most productive authors tend to collaborate and often have relatively broad research interests; (3) the functionality is the main objective of GUI testing; the model‐based approach is the most widely used. [ABSTRACT FROM AUTHOR]

Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy. [ABSTRACT FROM AUTHOR]

The success of agile development methods in software development has raised interest in System-on-Chip (SoC) design, which involves high architectural and development process complexity under time and project management pressure. This article discovers the current state of agile hardware development with the questions (1) how well literature covers the SoC development process, (2) what agile methods and practices are applied or (3) what proposals are made to increase the agility, and (4) what is the impact for the SoC community. To answer the questions, a mapping study and literature review were performed. Seven hundred thirty papers were first studied, and eventually, after a rigorous filtering process, 25 papers were thoroughly analyzed. The results show that the popular agile SW development methods are applied in 5 cases, ideas adapted from the agile Hardware manifesto in 9 cases, and 11 cases do not define the Agile HW development method. Most of the papers address shorter development time by better methodologies and tools that indirectly shape the SoC development toward agility. The focus of agile hardware development is mostly on the SoC artifacts and methodological improvements have not been quantified. However, the literature indicates a significant impact on many academic chip prototypes. The challenges are better understood and the interest in agile methods is clearly increasing. The methodological gaps in the prevalent situation encourage further research and more accurate reporting of the development in addition to the SoC artifacts. [ABSTRACT FROM AUTHOR]

Ubiquitous healthcare is an emerging area that employs ubiquitous technologies to enable technology oriented environment for healthcare professionals for provision of efficient and effective healthcare services. In past years, research community has proposed various technological solutions in different healthcare areas such as chronic disease monitoring, gait analysis, mood and fall detection, neuropathic monitoring, physiological and vital signs monitoring, pulmonogical monitoring, etc. However, in-depth analysis of these proposed solutions is required to analyze the form of proposed ubiquitous healthcare solutions; the extent ubiquitous technologies are integrated in these solutions; the type of real problem addressed; and how far these solutions are evaluated in real world settings? The addressal of these questions is critical to understand and evaluate the progress made in the area of ubiquitous healthcare and identify the challenges that are hindering the progress in this area. Therefore, in this research, a systematic research technique in the form of mapping study (also known as scoping study) is employed for in-depth analysis of evidences available on ubiquitous healthcare. The mapping study adopts a systematic approach to construct chain of evidences related to a particular topic and is a well-defined research technique in evidence based software engineering. This study identified a total of 103 primary studies, published between 2007 and 2018, for analysis of area under investigation. The study findings reveal that research trend in ubiquitous healthcare is horizontally spread by involving broad range of healthcare areas. The proposed solutions largely fall under the category of validation studies where experiments are conducted in laboratory settings rather real world environment. Another interesting finding is the lack of involvement of relevant healthcare community in proposed solution design. The challenges such as context awareness, data ownership, privacy and security, usability and trust are limiting the adoption of proposed solutions. Therefore, more extensive studies are required to first evaluate the applicability of proposed solutions in their respective environment, second, engagement and ownership of relevant community in solution design need to be considered. Third, the broad coverage of healthcare areas does not provide significant clusters of similar research in any particular area therefore future research should focus on strengthening these areas by conducting evaluation based longitudinal studies. In this way, the effects of proposed solutions can only be measured objectively and can be added to the body of knowledge. Finally, this research provides a thorough insight into the research on ubiquitous healthcare and offers an opportunity to conduct further research in this area. [ABSTRACT FROM AUTHOR]

Understanding and improving education are critical goals of learning analytics. However, learning is not always mediated or aided by a digital system that can capture digital traces. Learning in such environments can be studied by recording, processing, and analyzing different signals, including video and audio, so that traces of actors’ actions and interactions are captured. Multimodal Learning Analytics refers to analyzing these signals through the use and integration of these multiple modes. However, a need exists to evaluate how research is conducted in the emerging field of multimodal learning analytics to aid and evaluate how these systems work. With the growth of multimodal learning analytics, research trends and technologies are needed to support its development. We conducted a systematic mapping study based on established systematic literature practices to identify multimodal learning analytics research types, methodologies, and trending research themes. Most mapped papers presented different solutions and used evaluation-based research methods to demonstrate an increasing interest in multimodal learning analytics technologies. In addition, we identified 14 topics under four themes––learning context, learning process, systems and modality, and technologies––that can contribute to the growth of multimodal learning analytics.

Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development., Competing Interests: The authors declare that they have no competing interests., (© 2024 Massenon et al.)

Numerous studies in different science fields have examined the various aspects of educational interventions designed to facilitate learning outcomes in students with dyslexia. The aim of this study was to present an overview of empirical research related to interventions aimed at primary school students with dyslexia. Mapping was conducted to identify studies on this topic published from 2015 to 2019 and included in the Scopus database. The descriptive data from 57 studies were analyzed to present a classification scheme of research design, type of educational intervention, implementation of technology in educational interventions, and aim of the intervention. The results indicated an increasing trend in the last 5 years in the publication of papers in researched areas related to educational interventions for students with dyslexia, with uneven representation among countries. The analyzed empirical studies were mostly analytic, using different designs, with a slightly higher prevalence of (quasi) experimental versus descriptive designs. As expected, the goal of most educational interventions was directed toward the development of (pre)reading skills, with a wider prevalence of structured language-based programs, followed by software specialized to support learning, whereas adaptations of instructional materials and innovative information and communication technology (ICT) were less used. Mapping indicated the frequent use of ICT in all educational interventions, and more evidence is needed for complex rigorous experimental designs and interventions related to teaching in the classroom, as well as interventions aimed at facilitating the self-regulation of students with dyslexia. [ABSTRACT FROM AUTHOR]

Additive manufacturing has been gaining ground both in the industry and in the academic world due to its flexibility, easy operation, and the possibility of making products in the most varied formats. Also, it can be observed that magnets are largely produced using a sintering process, although it results in high (BH)max, this process is very limited in relation to the shapes of the magnets, thus, additive manufacturing can be used to produce complex shaped magnets. The aim of this article is to demonstrate the state of the art regarding the applied methods to produce permanent magnets and materials for magnet production. Considering this, a literature review was conducted using the methodology called mapping study to select the articles to be used, thus, demonstrating that the main compound used as the magnetic particle is NdFeB alloy powder. Finally, it can be concluded that the magnets produced by additive manufacturing have lower (BH)max than those produced by sintering processes, which despite having high filling capacity of magnetic particles increases the magnet's magnetic capacity, limits their shape, those by manufacturing magnets can be produced with complex shapes, the heating temperature may reduce the maximum energy, and the coercive force improves it. [ABSTRACT FROM AUTHOR]

In the past decade, unmanned aerial vehicles (UAVs) have become a major research topic in robotics, despite their existence since 1915. UAVs can perform various activities efficiently and effectively; therefore, involving a group of UAVs in performing a certain task has become a growing research trend. Research involving multiple UAVs has been carried out in various areas such as force protection, warfare in the military, remote sensing, disaster response activities, and surveillance. While performing critical tasks, efficiency and robustness are important considerations that can be achieved by involving heterogeneous UAV teams with effective communication and coordination techniques. Communication among heterogeneous UAVs is an interesting and critical research area that needs to be thoroughly explored. In this research, an evidence-based approach is adopted to explore research carried out on multiple heterogeneous UAVs and their communication patterns while performing various activities. A mapping study research technique is employed to systematically collect, analyze, and assess the evidence available on the topic under discussion. The time period defined for this study was set from 2005 to 2019, and 46 primary studies were considered for thematic analysis. The findings show that research studies fall under the category of validation research by constructing simulations to provide a proof-of-concept implementation of the proposed solutions. The communication patterns among heterogeneous UAVs involve various components of UAV communication, networking, formation, and path planning. The application areas that were largely focused on were search, rescue, monitoring, and surveillance missions. The overall trend showed that interest in multiple heterogeneous UAV usage increases over time with a focus on UAV networks, formations, and path planning while considering communication as an implicit part of all these structures. This research has conducted an in-depth analysis of existing research on heterogeneous UAVs and provides classification and trends of various themes emerging within this research area.

Developing interactive systems is a challenging task. It involves concerns related to human-computer interaction (HCI), such as usability and user experience. Therefore, HCI design must be addressed when developing such systems. HCI design often involves people with different backgrounds, which makes communication and knowledge transfer a challenging issue. In this scenario, knowledge management can support understanding concepts from different knowledge areas and help learn from previous experiences. Aiming at investigating how knowledge management has supported HCI design and contributed to the development of interactive systems, we performed a mapping study in the literature and analyzed 15 publications reporting the use of knowledge management in HCI design. Following that, we conducted a survey with 39 HCI design professionals to find out how knowledge has been managed in their HCI design practice. In this paper, we present the studies and discuss their main findings. In summary, the results indicate that knowledge management has been used in HCI design mainly to improve product quality and reduce the effort and time spent on design activities. However, there is a need for simpler and more practical knowledge-based solutions to support HCI design. Such approaches would be capable of reaching more HCI design practitioners that could benefit from them. [ABSTRACT FROM AUTHOR]

This study reviews prevailing trends in “for-profit” business-related gamification. It examines the current literature, focusing on gamification elements, industries and variables that is of interest to researchers in different business environments. A systematic mapping approach was applied to this study. Articles were selected from different databases in a two-step screening process, subject to sets of inclusion and exclusion criteria. A total of 25 articles were further for: (1) represented industries, (2) orientation of the gamified system, (3) types of implementation, (4) gamification elements analysed, (5) impact on companies, and (6) company variables analysed. Results confirmed that the number of empirical studies on gamification in for-profit organisations is growing. Researchers have placed greater emphasis on analysing customer-related gamification environments than on employee-oriented gamification. This finding is consistent with the prevailing trend of increasing demand from practitioners to gamify customer-related processes. This is likely due to the potential for higher positive impact on the performance of companies. Most frequently deployed gamification elements are badges, rewards, and leader boards. The literature suggests that over all, gamification has a positive effect on various company variables, such as motivation, engagement of employees, brand loyalty, and customer experience. This paper highlights the particular areas of business-related gamification that have already been examined and possible future directions.

Context: Using student subjects in empirical studies has been discussed extensively from a methodological perspective in Software Engineering (SE), but there is a lack of similar discussion surrounding ethical aspects of doing so. As students are in a subordinate relationship to their instructors, such a discussion is needed. Objective: We aim to increase the understanding of practices and perceptions SE researchers have of ethical issues with student participation in empirical studies. Method: We conducted a systematic mapping study of 372 empirical SE studies involving students, following up with a survey answered by 100 SE researchers regarding their current practices and opinions regarding student participation. Results: The mapping study shows that the majority of studies does not report conditions regarding recruitment, voluntariness, compensation, and ethics approval. In contrast, the majority of survey participants supports reporting these conditions. The survey further reveals that less than half of the participants require ethics approval. Additionally, the majority of participants recruit their own students on a voluntary basis, and use informed consent with withdrawal options. There is disagreement among the participants whether course instructors should be involved in research studies and if they should know who participates in a study. Conclusions: It is a positive sign that mandatory participation is rare, and that informed consent and withdrawal options are standard. However, we see immediate need for action, as study conditions are under-reported, and as opinions on ethical practices differ widely. In particular, there is little regard in SE on the power relationship between instructors and students. [ABSTRACT FROM AUTHOR]

Mobile computing has fast emerged as a pervasive technology to replace the old computing paradigms with portable computation and context-aware communication. Existing software systems can be migrated (while preserving their data and logic) to mobile computing platforms that support portability, context-sensitivity, and enhanced usability. In recent years, some research and development efforts have focused on a systematic migration of existing software systems to mobile computing platforms. To investigate the research state-of-the-art on the migration of existing software systems to mobile computing platforms. We aim to analyze the progression and impacts of existing research, highlight challenges and solutions that reflect dimensions of emerging and futuristic research. We followed evidence-based software engineering (EBSE) method to conduct a systematic mapping study (SMS) of the existing research that has progressed over more than a decade (25 studies published from 1996–2017). We have derived a taxonomical classification and a holistic mapping of the existing research to investigate its progress, impacts, and potential areas of futuristic research and development. The SMS has identified three types of migration namely Static, Dynamic, and State-based Migration of existing software systems to mobile computing platforms. Migration to mobile computing platforms enables existing software systems to achieve portability, context-sensitivity,and high connectivity. However, mobile systems may face some challenges such as resource poverty, data security, and privacy. The emerging and futuristic research aims to support patterns and tool support to automate the migration process. The results of this SMS can benefit researchers and practitioners-by highlighting challenges, solutions, and tools, etc., -to conceptualize the state-of-the-art and futuristic trends that support migration of existing software to mobile computing. [ABSTRACT FROM AUTHOR]

Developing interactive systems is a challenging task. It involves concerns related to human-computer interaction (HCI), such as usability and user experience. Therefore, HCI design must be addressed when developing such systems. HCI design often involves people with different backgrounds, which makes communication and knowledge transfer a challenging issue. In this scenario, knowledge management can support understanding concepts from different knowledge areas and help learn from previous experiences. Aiming at investigating how knowledge management has supported HCI design and contributed to the development of interactive systems, we performed a mapping study in the literature and analyzed 15 publications reporting the use of knowledge management in HCI design. Following that, we conducted a survey with 39 HCI design professionals to find out how knowledge has been managed in their HCI design practice. In this paper, we present the studies and discuss their main findings. In summary, the results indicate that knowledge management has been used in HCI design mainly to improve product quality and reduce the effort and time spent on design activities. However, there is a need for simpler and more practical knowledge-based solutions to support HCI design. Such approaches would be capable of reaching more HCI design practitioners that could benefit from them. [ABSTRACT FROM AUTHOR]

Background: Chemical exposure leading to ototoxicity is a fresh challenge for occupational healthcare in South Africa. Objectives: The critical question is: ‘what is known about occupational ototoxic chemicals with or without noise exposure in South Africa?’ Method: This qualitative, mapping study was completed with published (peer-reviewed) and grey literature from 1979-2019. Data was analysed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses: extension for Scoping Reviews and the Nursing and Allied Health Resources Section subcommittee on Mapping the Literature of Nursing and Allied Health (adapted). Numerical analysis of article type was completed, but the primary focus was on capturing patterns/trends using thematic analysis and ideology critique. Results: The African Journal of Disability, African Journal of Primary Health Care and Family Medicine, South African Medical Journal, The South African Journal of Communication Disorders [SAJCD] and Health SA Gesondheid) were included with the SAJCD containing one relevant item and seventeen other items were analysed. Research focusses on the mining sector (gold) in Gauteng, and ototoxic medication (tuberculosis and/or human immunodeficiency virus) take precedence. In KwaZulu-Natal, the focus is on commerce and industry across formal and informal sectors. There are no governmental policies that refer to chemical ototoxicity. Occupational hearing loss is configured exclusively on the meme that noise exposure is the only toxin. Conclusion: Chemical exposures are only just beginning to be recognised as ototoxic in South Africa. Hearing conservation programmes should always serve the workers’ interests and never bow down to the econometric interests of employers.

This study reviews prevailing trends in “for-profit” business-related gamification. It examines the current literature, focusing on gamification elements, industries and variables that is of interest to researchers in different business environments. A systematic mapping approach was applied to this study. Articles were selected from different databases in a two-step screening process, subject to sets of inclusion and exclusion criteria. A total of 25 articles were further for: (1) represented industries, (2) orientation of the gamified system, (3) types of implementation, (4) gamification elements analysed, (5) impact on companies, and (6) company variables analysed. Results confirmed that the number of empirical studies on gamification in for-profit organisations is growing. Researchers have placed greater emphasis on analysing customer-related gamification environments than on employee-oriented gamification. This finding is consistent with the prevailing trend of increasing demand from practitioners to gamify customer-related processes. This is likely due to the potential for higher positive impact on the performance of companies. Most frequently deployed gamification elements are badges, rewards, and leader boards. The literature suggests that over all, gamification has a positive effect on various company variables, such as motivation, engagement of employees, brand loyalty, and customer experience. This paper highlights the particular areas of business-related gamification that have already been examined and possible future directions. [ABSTRACT FROM AUTHOR]

This mapping study is the first bibliometric analysis of literature on Guillain–Barre Syndrome (GBS). By identifying core journals, analyzing cited references, categorizing journals into Zones, and estimating database coverage, the results of this study inform neurology specialists of the most relevant journals related to GBS to assist in current awareness efforts; it also assists librarians who make collection development decisions in the field of neurology. This study examined references during a three-year period (2016–2018) from a sample core of neurology journals using the mapping protocol from the Nursing and Allied Health Resources Section (NAHRS) of the Medical Library Association (MLA). [ABSTRACT FROM AUTHOR]

The concept of eServices originated in the early 2000s in the field of business and commerce. However, in recent years, eServices are being applied in many domains. Therefore, a thorough study on eServices is required to identify the areas in which eServices have been applied till date and to what extent. The main objective of this research is to perform a mapping study to provide an extensive review, gather trends, and identify the state of the art in the research on eServices to answer the research questions designed to conduct this research. A mapping study has been conducted employing an automatic search in digital repositories by developing a mapping protocol. Mapping studies are useful for categorizing and classifying the existing information concerning a particular research question in an unbiased manner. The search procedure identified 806 studies of which 318 were selected for full analysis during the years 2000 and 2016 in the field of computer science. No study was published before this time period. Research on eServices were recorded and classified into tabulated spread sheets, and finally analyzed. According to the study, the range of eService service and application domains is quite wide. Most studies conducted have focused on eService composition and eService Adoption. However, the most common application domains identified were eGovernment, eBusiness, eHealth, and eLearning. The study findings show that the research on eService composition, design, provision, and adoption is increasing with the passage of time. The literature not only discusses various domains of eServices but also provides the in-depth classification, review and trend of eService studies over time.

Abstract: Digital games are no longer seen as a form of entertainment detrimental to health. They have become an important tool to improve the treatment of patients, ranging from those who are experiencing a serious illness, such as cancer, to those requiring milder procedures such as physiotherapy. However, there are people who are physically disabled and have difficulty playing digital games. In this line of reasoning, this work presents a systematic review on the use of digital games for people with physical disabilities in the upper limbs. The sources of research were the following indexed databases: IEEE Xplore, Portal of Periodicals of the Coordination of Improvement of Personnel of Higher Level (CAPES), PubMed, Scielo, Science Direct, Scopus and Web of Science. It should be noted that the application of Systematic Mapping in the elaboration of a bibliographical review allowed to identify the main gaps for the development of new research, and to redirect to the main publications related to the study. Finally, the results shows that it is an area that is constantly expanding.

Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided. [ABSTRACT FROM AUTHOR]

Context: Measurement is essential to reach predictable performance and high capability processes. It provides support for better understanding, evaluation, management, and control of the development process and project, as well as the resulting product. It also enables organizations to improve and predict its process's performance, which places organizations in better positions to make appropriate decisions. Objective: This study aims to understand the measurement of the software development process, to identify studies, create a classification scheme based on the identified studies, and then to map such studies into the scheme to answer the research questions. Method: Systematic mapping is the selected research methodology for this study. Results: A total of 462 studies are included and classified into four topics with respect to their focus and into three groups based on the publishing date. Five abstractions and 64 attributes were identified, 25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the most measured process attributes, while effort and performance were the most measured project attributes. Goal Question Metric and Capability Maturity Model Integration were the main methods and models used in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently identified research contexts. [ABSTRACT FROM AUTHOR]

There has been considerable interest in Building Information Modeling (BIM) research and development during the last decade. BIM has established itself as a field of research in (and beyond) scientific communities interested in information technologies in construction. Interestingly, the contours of BIM as a scientific field are still not clearly identified. Several studies have recently tried to analyze different aspects of this issue, without providing a systematic and comprehensive methodological approach to accurately define the major themes and clusters of the BIM domain. This paper uses a systematic literature review approach to map the BIM research themes and clusters over ten years of scientific publications. 1244 articles published in peerreviewed journals between 2007 and 2016 were selected and the associated metadata analyzed in order to highlight co-occurrences in author's keywords. It appears that a few "big players" dominate the keywords, while most of the keywords used by authors are much less cited. Seven core clusters are identified using modularity optimization techniques: industry foundation classes, information technology, facility management, building, collaboration, computer aided design, and laser scanning. [ABSTRACT FROM AUTHOR]

The terms “Oracle Problem” and “Non-testable system” interchangeably refer to programs in which the application of test oracles is infeasible. Test oracles are an integral part of conventional testing techniques; thus, such techniques are inoperable in these programs. The prevalence of the oracle problem has inspired the research community to develop several automated testing techniques that can detect functional software faults in such programs. These techniques include N-Version testing, Metamorphic Testing, Assertions, Machine Learning Oracles, and Statistical Hypothesis Testing. This paper presents a Mapping Study that covers these techniques. The Mapping Study presents a series of discussions about each technique, from different perspectives, e.g. effectiveness, efficiency, and usability. It also presents a comparative analysis of these techniques in terms of these perspectives. Finally, potential research opportunities within the non-testable systems problem domain are highlighted within the Mapping Study. We believe that the aforementioned discussions and comparative analysis will be invaluable for new researchers that are attempting to familiarise themselves with the field, and be a useful resource for practitioners that are in the process of selecting an appropriate technique for their context, or deciding how to apply their selected technique. We also believe that our own insights, which are embedded throughout these discussions and the comparative analysis, will be useful for researchers that are already accustomed to the field. It is our hope that the potential research opportunities that have been highlighted by the Mapping Study will steer the direction of future research endeavours. [ABSTRACT FROM AUTHOR]

Background and objective Datamining (DM) has, over the last decade, received increased attention in the medical domain and has been widely used to analyze medical datasets in order to extract useful knowledge and previously unknown patterns. However, historical medical data can often comprise inconsistent, noisy, imbalanced, missing and high dimensional data. These challenges lead to a serious bias in predictive modeling and reduce the performance of DM techniques. Data preprocessing is, therefore, an essential step in knowledge discovery as regards improving the quality of data and making it appropriate and suitable for DM techniques. The objective of this paper is to review the use of preprocessing techniques in clinical datasets. Methods We performed a systematic map of studies regarding the application of data preprocessing to healthcare and published between January 2000 and December 2017. A search string was determined on the basis of the mapping questions and the PICO categories. The search string was then applied in digital databases covering the fields of computer science and medical informatics in order to identify relevant studies. The studies were initially selected by reading their titles, abstracts and keywords. Those that were selected at that stage were then reviewed using a set of inclusion and exclusion criteria in order to eliminate any that were not relevant. This process resulted in 126 primary studies. Results Selected studies were analyzed and classified according to their publication years and channels, research type, empirical type and contribution type. The findings of this mapping study revealed that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade. A significant number of the selected studies used data reduction and cleaning preprocessing tasks. Moreover, the disciplines in which preprocessing have received most attention are: cardiology, endocrinology and oncology. Conclusions Researchers should develop and implement standards for an effective integration of multiple medical data types. Moreover, we identified the need to perform literature reviews. [ABSTRACT FROM AUTHOR]

Requirements engineering (RE) being a foundation of software development has gained a great recognition in the recent era of prevailing software industry. A number of journals and conferences have published a great amount of RE research in terms of various tools, techniques, methods, and frameworks, with a variety of processes applicable in different software development domains. The plethora of empirical RE research needs to be synthesized to identify trends and future research directions. To represent a state-of-the-art of requirements engineering, along with various trends and opportunities of empirical RE research, we conducted a systematic mapping study to synthesize the empirical work done in RE. We used four major databases IEEE, ScienceDirect, SpringerLink and ACM and Identified 270 primary studies till the year 2012. An analysis of the data extracted from primary studies shows that the empirical research work in RE is on the increase since the year 2000. The requirements elicitation with 22 % of the total studies, requirements analysis with 19 % and RE process with 17 % are the major focus areas of empirical RE research. Non-functional requirements were found to be the most researched emerging area. The empirical work in the sub-area of requirements validation and verification is little and has a decreasing trend. The majority of the studies (50 %) used a case study research method followed by experiments (28 %), whereas the experience reports are few (6 %). A common trend in almost all RE sub-areas is about proposing new interventions. The leading intervention types are guidelines, techniques and processes. The interest in RE empirical research is on the rise as whole. However, requirements validation and verification area, despite its recognized importance, lacks empirical research at present. Furthermore, requirements evolution and privacy requirements also have little empirical research. These RE sub-areas need the attention of researchers for more empirical research. At present, the focus of empirical RE research is more about proposing new interventions. In future, there is a need to replicate existing studies as well to evaluate the RE interventions in more real contexts and scenarios. The practitioners’ involvement in RE empirical research needs to be increased so that they share their experiences of using different RE interventions and also inform us about the current requirements-related challenges and issues that they face in their work. [ABSTRACT FROM AUTHOR]

Customer loyalty is a topic of great interest for marketing scholars due to its importance in gaining sustainable competitive advantages and financial outcomes. Literature is prolific of works regarding customer loyalty and brand management. In order to improve the quantity and quality of research publications, research themes mapping of published studies is required. The aim of this paper is to provide scholars with a systematization and mapping of the contributions on this topic to develop an effective research road map for future research. A bibliometric analysis and a mapping study have been conducted on 337 publications on customer loyalty and brand management from 2000 to 2018. The results present the most cited works on the topic, an evaluation map showing the most frequent and cited words and six clusters of words based on their co-occurrence. From the analysis of the clusters, the most relevant research findings, trends, and issues emerge, suggesting interesting theoretical and practical implications.

Non-communicable diseases burden is well-known and care for these diseases goes beyond patients’ engagement, extending to their family, friends, and acquaintances. The ability of social relations in alleviating the harmful effects of health risks is known as social support. Computing can be used to promote social support to enhance the care of non-communicable diseases. However, it is unclear how computing obtains such enhancement. This paper presents a systematic review, in the form of a mapping study, aiming to answer how computing enhances non-communicable diseases care by using social data and by promoting social support. It also looks for available computing models focused on social support promotion in non-communicable diseases care. The study was guided by a two-phase process review, resulting in 38 reviewed papers from journals, conferences, and chapters in the period from 2010 to 2016. In general, the reviewed papers focus on controlled trials, frameworks and systems, knowledge discovery, simulation models or social media usage analysis. Knowledge discovery was the predominant subject, followed by social media usage analysis, and frameworks and systems. [ABSTRACT FROM AUTHOR]

This study focuses on the context of graphic representation technologies and digital design on Architectural teaching in Latin America. From categories proposed by Oxman and Kotnik and through a mapping study framed by a systematic review in CumInCAD database, it is presented a panorama of the state-of-art of the digital design on Architectural teaching in the region, between 2006 and 2015. The results suggest a context of coexistence of representational interaction and parametric interaction, as well as a transition from one to another and the emergence of the first experiments in algorithmic interaction. As this mapping shows an ongoing movement toward Digital Architectural Design in Latin America in the last decade, and points out its dynamics in space in time, it could contribute to strengthen a crowdthinking network on this issue in the region and with other continents. [ABSTRACT FROM AUTHOR]

A review of the qualitative research methods discussed in papers that study software engineering teams showed most of those papers did not follow a systematic process during the qualitative analysis. This finding is concerning as this deficiency in research analysis procedure may reduce the validity and/or completeness of the qualitative results. Such a lack of rigor may be a result of qualitative research not being as firmly established in software engineering as quantitative research methodologies. In engineering research, quantitative methods are typically more prevalent and qualitative analysis is part of a mixed-method analysis process. However, when researching teams, where human activity is abundant, qualitative analysis may need to take precedence. In this paper, we focus on the qualitative analysis method called content analysis with the goal of presenting a rigorous process for content analysis in the context of software engineering. We then present and demonstrate the use of that content analysis process for software engineering researchers using two examples. An analysis of 215 articles that were a result of a mapping study on software engineering team research is presented. Those papers were analyzed to determine which utilized a qualitative data analysis method in their research in addition to the rigor and type of qualitative analysis performed. We ultimately included 23 papers in this study. We present a mapping study and a content analysis process that include a straightforward way to select, code, and present data in both inductive and deductive studies. We demonstrated the process using the keywords from the papers included in this study as well as on a second data set that utilized responses from structured interview transcripts from practicing software engineers. The first dataset also resulted in a taxonomy to categorize software engineering team research. We presented and demonstrated a content analysis process in terms of software engineering in order to improve future qualitative software engineering research that would benefit from systematic content analysis. [ABSTRACT FROM AUTHOR]

This commentary sums the findings of a series of papers on a study that mapped the global research agenda for maternal health. The mapping reviewed published interventional research across low- and middle-income countries (LMICs) from 2000 to 2012, specifically focusing on investigating the topics covered by this research, the methodologies applied, the funding landscape and trends in authorship attribution.The overarching aim underpinning the mapping activities was to evaluate whether research and funding align with causes of maternal mortality, and thereby highlight gaps in research priorities and governance. Fifteen reviewers from 8 countries screened 35,078 titles and abstracts, and extracted data from 2292 full-text articles.Over the period reviewed, the volume of publications rose several-fold, especially from 2004 to 2007. The methodologies broadened, increasingly encompassing qualitative research and systematic review. Malaria and HIV research dominated over other topics, while sexually-transmitted infection research progressively diminished. Health systems and health promotion research increased rapidly, but were less frequently evaluated in trials or published in high-impact journals. Relative to disease burden, hypertension had double the publications of haemorrhage. Many Latin American countries, China and Russia had relatively few papers per billion US dollars Gross Domestic Product. Total LMIC lead authorships rose substantially, but only a quarter of countries had a local first author lead on >75% of their research, with levels lowest in sub-Saharan Africa. The median Impact Factor of high-income country led papers was 3.1 and LMIC-led 1.8. The NIH, USAID and Gates Foundation constituted 40% of funder acknowledgements, and addressed similar topics and countries.The commentary notes that increases in outputs and broadening of methodologies suggest research capacity has expanded considerably, allowing for more nuanced, systems-based and context-specific studies. However, funders seemingly duplicate efforts, with topics and countries either receiving excessive or little attention. Better coordinated funding might reduce duplication and allow researchers to develop highly-specialised expertise. Repeated scrutiny of research agendas and funding may foment shifts in priorities. Building leadership capacity in LMICs and reconsidering authorship guidelines is needed. [ABSTRACT FROM AUTHOR]