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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...

Privacy by design is a new paradigm that promotes embedding privacy considerations throughout the development of information systems, to protect user privacy. Privacy engineering is the nascent field of research and practice that aims to realize this new paradigm systematically and efficiently, by delivering reusable elements such as methods, techniques, and tools that software and systems engineers can apply in their daily work. However, as a new field, its contributions are still scattered, and there is little information on their quantity or maturity. To bridge this gap, we have carried out a systematic mapping study to provide engineers and researchers with a snapshot of the reusable elements available for the systematic design of privacy-friendly software-based information systems. The results show that there is an emerging and growing interest in the field, being privacy patterns, the hottest research topic. However, the maturity of some of the contributions found is still low as they usually lack empirical evidence that demonstrates their benefits, which may hinder their adoption in practice. In this paper, we describe the most advanced research areas and discuss some of the gaps found, suggesting areas where researchers and funding institutions can focus their efforts.

Smart meters and the smart grid will allow utility companies and customers to monitor their electricity and utility usage in fine-grained detail instead of the previously common monthly or yearly measurements. With this fine-grained detail comes serious privacy concerns. One of the most promising solutions for measuring and preserving privacy loss is differential privacy. Both differential privacy and the smart grid are relatively young developments that will require more research before they can be confidently implemented worldwide. With this systematic mapping study, we will provide an overview of the current literature and attempt to determine the future directions the research may take.

Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks. However, since the renaissance of DL techniques is still very recent, we lack works that summarize and condense the most recent and relevant research conducted at the intersection of DL and SE. Therefore, in this paper, we describe the first results of a mapping study covering 81 papers about DL & SE. Our results confirm that DL is gaining momentum among SE researchers over the years and that the top-3 research problems tackled by the analyzed papers are documentation, defect prediction, and testing., 8 pages, 5 figures, 4 tables. Accepted for publication at ACM SAC 2021

Internet of things (IoT) has applications in healthcare and medicine, where the treatment of cancer (disease with high incidence and mortality rates) stands out. The goal of this study is to characterize existing IoT platforms for cancer treatment, in terms of their purposes, types of sensors used, and cancer type treated. For that, a systematic literature mapping was performed, which included and analyzed 84 primary articles. We identified and classified 7 types of IoT platforms for cancer treatment, 39 purposes sought by those platforms, and 13 types of cancer. The results show that this is an active research area, where most studies focus on treating the types of cancer with highest incidence worldwide: breast, skin, and lung cancer, in order to reduce costs associated with the diagnosis and detection of the disease.

As opposed to traditional software development, agile development encourages requirements change. Given the importance of requirements change management, there is an increasing number of studies discussing this process in both traditional and agile software developments. By examining existing works, we observed that there are many ways to improve the management of agile requirements change. Aiming to addressing this research gap, our study focus on defining a process to handle Agile Requirement Change Management (ARCM), as well as identifying practices to support ARCM process. We performed systematic study to map the main practices applied to manage agile requirements change. Our primary goal was to seek a better understanding of possible directions in the ARCM research, and foster discussions for future work. We identified 3 steps to handle the ARCM process, and grouped 11 distinct agile practices to carry out these steps. Even though agile practices might be, to some extent, for managing requirement change, we were able to identify practical challenges when adopting these practices for Agile Requirements Changes management.

Background: The adoption of the evidence-based research paradigm by software engineering researchers has created a growing knowledge base provided by the outcomes from systematic reviews.\ud \ud Aim: We set out to identify and catalogue a sample of the knowledge provided by systematic reviews, to determine what support they can provide for an evidence-informed approach to teaching about software engineering practice.\ud \ud Method: We undertook a tertiary study (a mapping study of systematic reviews) covering the period to the end of 2015. We identified and catalogued those reviews that had findings or made recommendations that were considered relevant to teaching about industry practice.\ud \ud Results: We examined a sample of 276 systematic reviews, selecting 49 for which we could clearly identify practice-oriented findings and fecommendations that were supported by the data analysis provided in the review. We have classified these against established software engineering education knowledge categories and discuss the extent and forms of knowledge provided for each category.\ud \ud Conclusion: While systematic reviews can provide knowledge that can inform teaching about practice, relatively few systematic reviews present the outcomes in a form suitable for this purpose. Using a suitable format for presenting a summary of outcomes could improve this. Additionally, the increasing number of published systematic reviews suggests that there is a need for greater coordination regarding the cataloguing of their findings and recommendations.

Code reviews have been used to improve code quality since the 1970s. Most practitioners in the field of software have some experience with respect to the technique. In this mapping study we illustrate what kinds of research questions are addressed in code review literature. The following themes emerged from analysis of 75 original articles: (1) description or comparison of different code review practices, (2) behavior of reviewers (e.g., eye tracking studies), (3) communication and teamwork, (4) outcomes of code reviews (e.g., what kinds of problems are identified), (5) how properties of code to be reviewed affect reviewing, and (6) reasons for conducting code reviews. About half of the studies have been conducted with students and novices. The numbers of industry papers has significantly increased when compared to the previous reviews in the field.

The education-training process and all activities related to it have the power to direct the future of societies. From this point of view, the process should be analyzed frequently in terms of input, output, and other process elements. Educational data mining is a multidisciplinary research area that develops methods and techniques for discovering data derived from various information systems used in education. It contributes to the understanding of the learning styles of learners and enables data-driven decision making to develop existing learning practices and learning materials. The number of academic and technical research on educational data mining is on the rise, and this has led to the need to systematically categorize the existing practices. This systematic mapping study was conducted to provide an overview of the current work on educational data mining and its results are based on 153 primary sources including journal papers, articles published in magazines, conference and symposium papers, theses, and others.

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.

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.

Blood glucose (BG) prediction system can help gestational diabetes mellitus (GDM) patient to improve the BG control with managing their dietary intake based on healthy food. Many techniques have been developed to deal with blood glucose prediction, especially those for recommender system. In this study, we conduct a systematic mapping study to investigate recent research about BG prediction in recommender systems. This study describes an overview of research (2014-2018) about BG prediction techniques that has been used for BG recommender system. As results, 25 studies concerning BG prediction in recommender system were selected. We observed that although there is numerous studies published, only a few studies took serious discussion about techniques used to incorporate the BG algorithms. Our result highlighted that only one study discusses hybrid filtering technique in BG recommender system for GDM even though it has an ability to learn from experience and to improve prediction performance. We hope that this study will encourage researchers to consider not only machine learning and artificial intelligent techniques but also hybrid filtering technique for BG recommender system in the future research.

This paper describes a mapping study designed to investigate the available research on bug bounty programs (BBP). Based on the 72 identified papers we conclude that the research has been focused on the organisation of bug bounties from the product owner perspective, rather than on bug hunters and the market for bugs. To understand BBPs better, in addition to the open datasets available today, datasets from more diverse types of companies (e.g. safety critical systems) should be added, and more in-depth qualitative studies conducted.

Programa Cada sem Fronteiras (Ref. 99999.009047/2013-01) Context: Software architecture design creates and documents the high-level structure of a software system. Such structure, expressed in architectural models, comprises software elements, relations among them, and properties of these elements and relations. Existing software architecture methods offer ways to derive architectural models from requirements specifications. These models must balance different forces that should be analyzed during this derivation process, such as those imposed by different application domains and quality attributes. Such balance is difficult to achieve, requiring skilled and experienced architects. Object: The purpose of this paper is to provide a comprehensive overview of the existing methods to derive architectural models from requirements specifications and offer a research roadmap to challenge the community to address the identified limitations and open issues that require further investigation. Method: To achieve this goal, we performed a systematic mapping study following the good practices from the Evidence-Based Software Engineering field. Results: This study resulted in 39 primary studies selected for analysis and data extraction, from the 2575 initially retrieved. Conclusion: The major findings indicate that current architectural derivation methods rely heavily on the architects’ tacit knowledge (experience and intuition), do not offer sufficient support for inexperienced architects, and lack explicit evaluation mechanisms. These and other findings are synthesized in a research roadmap which results would benefit researchers and practitioners. authorsversion published

This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest in the health domain? We identify 30 articles related to health chatbots from 2014 to 2018. We analyze the selected articles qualitatively and extract a triplettechnicalEnablers, competence, illnessfor each of them. This data serves to provide a first overview of chatbot-mediated behavior change on the health domain. Main insights include: nutritional disorders and neurological disorders as the main illness areas being tackled; "affect" as the human competence most pursued by chatbots to attain change behavior; and "personalization" and "consumability" as the most appreciated technical enablers. On the other hand, main limitations include lack of adherence to good practices to case-study reporting, and a deeper look at the broader sociological implications brought by this technology.

Context: A software product line is a means to develop a set of products in which variability is a central phenomenon captured in variability models. The field of SPLs and variability have been topics of extensive research over the few past decades. Objective: This research characterizes systematic reviews (SRs) in the field, studies how SRs analyze and use evidence-based results, and identifies how variability is modeled. Method: We conducted a tertiary study as a form of systematic review. Results: 86 SRs were included. SRs have become a widely adopted methodology covering the field broadly otherwise except for variability realization. Numerous variability models exist that cover different development artifacts, but the evidence is insufficient in quantity and immature, and we argue for better evidence. SRs perform well in searching and selecting studies and presenting data. However, their analysis and use of the quality of and evidence in the primary studies often remains shallow, merely presenting of what kinds of evidence exist. Conclusions: There is a need for actionable, context-sensitive, and evaluated solutions rather than novel ones. Different kinds of SRs (SLRs and Maps) need to be better distinguished, and evidence and quality need to be better used in the resulting syntheses. (C) 2019 The Authors. Published by Elsevier Inc. Context: A software product line is a means to develop a set of products in which variability is a central phenomenon captured in variability models. The field of SPLs and variability have been topics of extensive research over the few past decades. Objective: This research characterizes systematic reviews (SRs) in the field, studies how SRs analyze and use evidence-based results, and identifies how variability is modeled. Method: We conducted a tertiary study as a form of systematic review. Results: 86 SRs were included. SRs have become a widely adopted methodology covering the field broadly otherwise except for variability realization. Numerous variability models exist that cover different development artifacts, but the evidence is insufficient in quantity and immature, and we argue for better evidence. SRs perform well in searching and selecting studies and presenting data. However, their analysis and use of the quality of and evidence in the primary studies often remains shallow, merely presenting of what kinds of evidence exist. Conclusions: There is a need for actionable, context-sensitive, and evaluated solutions rather than novel ones. Different kinds of SRs (SLRs and Maps) need to be better distinguished, and evidence and quality need to be better used in the resulting syntheses. (C) 2019 The Authors. Published by Elsevier Inc. Context: A software product line is a means to develop a set of products in which variability is a central phenomenon captured in variability models. The field of SPLs and variability have been topics of extensive research over the few past decades. Objective: This research characterizes systematic reviews (SRs) in the field, studies how SRs analyze and use evidence-based results, and identifies how variability is modeled. Method: We conducted a tertiary study as a form of systematic review. Results: 86 SRs were included. SRs have become a widely adopted methodology covering the field broadly otherwise except for variability realization. Numerous variability models exist that cover different development artifacts, but the evidence is insufficient in quantity and immature, and we argue for better evidence. SRs perform well in searching and selecting studies and presenting data. However, their analysis and use of the quality of and evidence in the primary studies often remains shallow, merely presenting of what kinds of evidence exist. Conclusions: There is a need for actionable, context-sensitive, and evaluated solutions rather than novel ones. Different kinds of SRs (SLRs and Maps) need to be better distinguished, and evidence and quality need to be better used in the resulting syntheses. (C) 2019 The Authors. Published by Elsevier Inc.

Context The ISO/IEC29110 has been studied since its release in 2011, and its impact and evaluation over the recent years have been quite diverse. This standard is structured in five parts describing the business terms, the main Very Small Entities (VSE) profile concepts, process assessment guidelines, specification of all the generic profile group, and implementation management and engineering guide for entry and basic profiles. Objective The main purpose of this work is to provide an analysis of the research carried out about the ISO/IEC 29110 during the last ten years, and the literature that has developed around it. Literature is analyzed by using the traditional mapping study of the ISO/IEC29110 and its parts. All these studies are categorized in a set of topics where authors have been contributing. This work helps us on the identification of the main research topics within the primary studies. Method The mapping study is conducted as a traditional systematic mapping with a categorization of the primary studies. The main search is enhanced with additional searches for each member of the ISO/IEC 29110 series. Results A search strategy is defined to conduct this mapping study. 184 papers were retrieved from the literature and selected as primary studies. Our study identifies the reference studies in this area, it characterizes them, and identifies which aspects have been treated. Conclusion The results of this mapping reveal that ISO/IEC 29110 has been used in a broad range of small contexts, and the main contributions are basically from research experiences during the recent last ten years. The literature around this standard is classified based on a well-known classification schema, the activity around this standard, and what types of studies have been carried out. Research topics are diverse, and we have identified the research methods used by the primary studies. As conclusion, more research and experimental outcomes are needed in order to observe how VSEs behave under specific circumstances.

Blockchain is currently one of the most important topics in both the academia and industry world, mainly due to the possible effects that the continuing application of this new technology could have. The adoption of this technology by FinTech companies constitutes the next step towards the expansion of blockchain and its sustainability. The paper conducts a mapping study on the research topics, limitations, gaps and future trends of blockchain in FinTech companies. A total of 49 papers from a scientific database (Web of Science Core Collection) have been analyzed. The results show a deep focus in challenges such as security, scalability, legal and regulatory, privacy or latency, with proposed solutions still to be far from being effective. A vast majority of the research is focused into finance and banking sector, obviating other industries that could play a crucial role in the further expansion of blockchain. This study can contribute to researchers as a starting point for their investigation, as well as a source for recommendations on future investigation directions regarding blockchain in the FinTech sector.

Developer social networks (DSNs) are a tool for the analysis of community structures and collaborations between developers in software projects and software ecosystems. Within this paper, we present the results of a systematic mapping study on the use of DSNs in software engineering research. We identified 255 primary studies on DSNs. We mapped the primary studies to research directions, collected information about the data sources and the size of the studies, and conducted a bibliometric assessment. We found that nearly half of the research investigates the structure of developer communities. Other frequent topics are prediction systems build using DSNs, collaboration behavior between developers, and the roles of developers. Moreover, we determined that many publications use a small sample size regarding the number of projects, which could be problematic for the external validity of the research. Our study uncovered several open issues in the state of the art, e.g., studying inter-company collaborations, using multiple information sources for DSN research, as well as general lack of reporting guidelines or replication studies., Comment: Accepted at the Journal of Systems and Software

Integrating value-oriented perspectives into the principles and practices of software engineering is critical to ensure that software development and management activities address all key stakeholders’ views and also balance short-and-long-term goals. This is put forward in the discipline of Value-Based Software Engineering (VBSE). In this paper, a mapping study of VBSE is detailed. We classify evidence on VBSE principles and practices, research methods, and the research types. This mapping study includes 134 studies located from online searches, and backward snowballing of references. Our results show that VB Requirements Engineering (22%) and VB Planning and Control (19%) were the two principles and practices mostly investigated in the VBSE literature, whereas VB Risk Management, VB People Management and Value Creation (3% respectively) were the three less researched. In terms of the research method, the most commonly employed method is case-study research. In terms of research types, most of the studies (28%) proposed solution technique(s) without empirical validation.

The year 2018 marked the tenth anniversary of the Symposium on Search Based Software Engineering (SSBSE). In order to better understand the characteristics and evolution of papers published in SSBSE, this work reports results from a mapping study targeting the ten proceedings of SSBSE. Our goal is to identify and to analyze authorship collaborations, the impact and relevance of SSBSE in terms of citations, the software engineering areas commonly studied as well as the new problems recently solved, the computational intelligence techniques preferred by authors and the rigour of experiments conducted in the papers. Besides this analysis, we list some recommendations to new authors who envisage to publish their work in SSBSE. Despite of existing mapping studies on SBSE, our contribution in this work is to provide information to researchers and practitioners willing to enter the SBSE field, being a source of information to strengthen the symposium, guide new studies, and motivate new collaboration among research groups.

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 ...

Model-Based Testing (MBT) has been successfully applied to Software Product Lines (SPL). This paper provides a panorama of state-of-the-art on MBT of SPLs. We performed a systematic mapping for answering questions related with domains, approaches, solution types, variability, test case automation, artifacts, and evaluation. We built a roadmap from 44 selected studies. Main obtained results are: Software and Automotive domains are most considered; Black-box testing is widely performed; most studies have fully-automated support; variability is considered in most studies; Finite State Machines is the most used model to test SPLs; Behavioral-based and Scenario-based are the most used models; Case Studies and Experiments are used to evaluate MBT solutions and the majority is performed in industrial environments; traceability is not widely explored for MBT solutions. Furthermore, we provide a roadmap synthesizing studies based on used models, more formal artifacts, supporting tools, variability management, (semi-)automation, and traceability. The roadmap contributes to identify related primary studies based on given artifacts, variability management, tools, automation, and traceability techniques and to identify, from a given primary study, which artifacts, tools, variability management, automation and traceability techniques are related. Therefore, the roadmap serves as a guide to researchers and practitioners on how to model-based test SPLs.

This research full paper characterizes the literature on academic initiatives to foster computational thinking and programming (CT&P) in Brazilian K-12 education. Context: Mapping and analyzing the diversity of experiences and studies that address CT&P in K-12 education can bring valuable data to researchers. This work delimits such study to the Brazilian scenario to allow a more in-depth view, given the Brazilian context. Previous surveys and systematic mapping studies present recent publications in major Brazilian computing journals and conferences. Although they offer important contributions, they do not comprehensively cover the Brazilian literature on CT&P in K-12 education, since they focus the search in the Brazilian Computer Society publications alone. Objective: This work proposes to characterize the literature on CT&P in K-12 education in Brazil. Results: through a systematic mapping study, we collected information on year, venue, type, K-12 education stage and modality, methodological contexts, and used tools and programming languages from 338 selected primary studies from 2001 until 2016. Conclusions: there is a significant increase in the number of Brazilian studies in the latest years, showing a growing interest on this research area as well as several trends and gaps to be addressed by both researchers and practitioners.

The aim of this study is to perform a Systematic Literature Mapping (SLM) about methodologies, methods, techniques and tools used on the development of Systematic Literature Reviews (SLR). As a result, on the mapping, we expected to find and classify methodologies, methods, techniques and tools commonly used on SLR. In addition, we have considered other contexts such as, Medicine or Education with the purpose of getting multiple methodologies, methods, techniques and tools that allow performing SLR on efficient ways. It is mainly expected to identify techniques related to research questions formulation and the methods used for building search strings in order to get the higher number of studies associated to the research topic. In our study, we found multiple methodologies, methods, techniques and tools already implemented for performing SLRs On the mapping we describe some of them to highlight the most used and referenced studies.

Reuse has been recognized as an important practice in software engineering. The use of patterns makes it easier to reuse successful solutions, speeds up the development process, and promotes the application of good practices. Related patterns can be organized in a Pattern Language (PL), which represents the patterns and their relations, and provides guidance on how to select, reuse and integrate them. Visual notations are often used to provide a graphical representation to PLs. Aiming to investigate how PLs related to software have been visually represented, we carried out a systematic mapping. We identified and analyzed 64 PLs. As a result, we noticed a lack of consensus on the elements that should be represented in a PL and the symbols used to represent them. Moreover, most PLs have ambiguous or inexpressive visual representations.

The teaching-learning process of Software Engineering (SE) has been widely discussed by several Brazilian authors. This concern reflects the demand for qualified professionals, since quality in teaching can contribute to the improvement of software development. To do so, there is a need to move beyond the expository format to incorporate new approaches and techniques. From a systematic mapping of the literature in Brazil, which included an automatic search in Google Scholar, manual searches in two journals and four important Brazilian events in the area of computing education, complemented by snowballing, articles with proposals for games, tools, teaching methodologies and integration between disciplines were identified and analyzed to support the software engineering education. The results contribute to the evaluation of the state of the art in the area.

The Blackbox project has been collecting programming activity data from users of BlueJ (a novice-targeted Java development environment) for nearly five years. The resulting dataset of more than two terabytes of data has been made available to interested researchers from the outset. In this paper, we assess the impact of the Blackbox project: we perform a mapping study to assess eighteen publications which have made use of the Blackbox data, and we report on the advantages and difficulties experienced by researchers working with this data, collected via a survey. We find that Blackbox has enabled pieces of research which otherwise would not have been possible, but there remain technical challenges in the analysis. Some of these -- but not all -- relate to the scale of the data. We provide suggestions for the future use of Blackbox, and reflections on the role of such data collection projects in programming research.

Objective: This study updates Haaland’s 1999 dental hygiene mapping study. By identifying core journals and estimating database coverage, it characterizes changes in dental hygiene research and aids librarians in collection development and user education.Method: Cited references from a three-year (2015–2017) sample of core dental hygiene journals were collected, categorized into five formats, and analyzed by format and publication year according to Bradford’s Law of Scattering. CINAHL Complete, MEDLINE, and EMBASE were surveyed to determine the indexing coverage of cited journals.Results: The number of cited journal titles increased from 389 in 1999 to 1,675 in 2018. Core Zone 1 titles increased from 5 to 11. Journal article citations increased from 69.5% of all citations in 1999 to 78.4% in the present study, whereas book citations decreased from 18.1% to 5.1%. A newly added category, “Internet sources,” accounted for 8.4% of citations. Overall, 68.6% of citations were 10 years or younger versus 71.4% in 1999. Most Zone 1 and Zone 2 journals were specific to dentistry or dental hygiene.Conclusion: Notable changes since 1999 were an increased volume of literature and a shift from print to online sources, reflecting improved accessibility of the literature and greater Internet use. From 1999 to 2018, citations to journal articles increased, books decreased, websites appeared, and government publications increased slightly. These findings indicate that dental hygiene research is growing and that indexing coverage for this field has improved dramatically in the past two decades.

Progress in software engineering requires (1) more empirical studies of quality, (2) increased focus on synthesizing evidence, (3) more theories to be built and tested, and (4) the validity of the experiment is directly related with the level of confidence in the process of experimental investigation. This paper presents the results of a qualitative and quantitative classification of the threats to the validity of software engineering experiments comprising a total of 92 articles published in the period 2001-2015, dealing with software testing of Web applications. Our results show that 29.4% of the analyzed articles do not mention any threats to validity, 44.2% do it briefly, and 14% do it judiciously; that leaves a question: these studies have scientific value?

Internet of Things is a fairly new paradigm adopted by the industry, which offers the connectivity, via wireless systems, of all the devices that surround us. One of the challenges of IoT relates to the required resources to store and compute the huge amount of data resulted from devices' connections. Cloud computing is a solution to the IoT challenges; it provides on-demand resources in an easy-to-access manner. Another trend in the enterprise world is the usage of microservice architectures. Being a newly developed paradigm, and although its principles are defined, it is difficult to have a vision of the existing microservice-based research solutions. This paper, through the mapping study methodology, provides an overview of the current state-of-the-art and -practice regarding the usage of microservice architectures by IoT and cloud computing solutions. More specifically, we synthesize the data from 364 selected studies and describe the research types, number of publications and their main venues.

Smart Technologies include hardware, software and communications systems that are capable of automatically adapting and modifying their behavior (parameters) generating information and knowledge to support decision making. This paper focuses on identifying trends in the subject of intelligent technologies, through systematic mapping to answer questions about the fields, research areas and types of intelligent technologies present in the literature. The mapping study was conducted using a review protocol and analyzing 367 scientific database records, using specialized bibliometric analysis software. The results can help to understand the prevailing needs and trends, identifying points that still require further research, since important aspects related to particular points such as the conceptual development of this topic and the emerging areas in the application of the same are important given the increase in publications and relevance of this topic in recent years.

Background. Continuous experimentation (CE) has recently emerged as an established industry practice and as a research subject. Our aim is to study the application of CE and A/B testing in various industrial contexts. Objective. We wanted to investigate whether CE is used in different sectors of industry, by how it is reported in academic studies. We also wanted to explore the main topics researched to give an overview of the subject and discuss future research directions. Method. We performed a systematic mapping study of the published literature and included 62 papers, using a combination of database search and snowballing. Results. Most reported software experiments are done online and with software delivered as a service, although varied exemptions exist for e.g., financial software and games. The most frequently researched topics are challenges to conduct experiments and statistical methods for software experiments. Conclusions. The software engineering research on CE is still in its infancy. There are future research opportunities in evaluation research of technical topics and investigations of ethical experimentation. We conclude that the included studies show that A/B testing is applicable to a diversity of software and organisations.

Data mining techniques (DMT) provide powerful tools to extract knowledge from data helping in decision making. Medicine, like many other fields, is using DM in diabetes, cardiology, cancer and other diseases. In this paper, we investigate the use of DMT in diabetes, in particular in diabetes self-management (DSM). The purpose is to conduct a systematic mapping study to review primary studies investigating DMT in DSM. This mapping study aims to summarize and analyze knowledge on: (1) years and sources of DSM publications, (2) type of diabetes that took most attention, (3) DM tasks and techniques most frequently used, and (4) the considered functionalities. A total of 57 papers published between 2000 and April 2017 were selected and analyzed regarding four research questions. The study shows that prediction was largely the most used DM task and Neural Networks were the most frequently used technique. Moreover, T1DM is largely the type of diabetes that is most concerned by the studies so as the Prediction of blood glucose.

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

Usability evaluation is currently considered critical for the success of mobile interactive applications. This paper presents a Systematic Mapping Study (SMS) that has been conducted to investigate the literature related to the Mobile Usability Evaluation (MUE) techniques. This mapping study builds on the followings classification criteria for the selection of studies: research approaches, research types, research domains, Usability evaluation methods, data collection tools, types of questionnaires that have been used in the empirical evaluations of these studies, and software quality (SQ) models. Publication channels and trends were also identified and 81 papers of MUE were selected.

Context: In experimental software engineering (ESE), experimental replication is applied to validate results of an experiment. Much information and one version of the experimental materials are required for replication. Prior to the replication execution, all or part of the materials may require changes, producing new or modified versions of these, which should be incorporated into the material of the original experiment. There is a direct relationship between the increase in the number of replications with the increase of versions of the experimental material, which commonly causes confusion and disorder for experimental administrators. Objective: The aim of this paper is to conduct a mapping study to locate articles about the use of experimental material management tools in experimental replication in ESE. Method: We applied the mapping study to search, analyze and select published papers from reported replications. Results: We analyzed a total of 592 articles published from 1998 to 2014, 24 of them have been pre-selected and 4 have finally been selected. Conclusion: The results show the limited existence of articles on this subject. In addition, our analysis has allowed to identify that most of them suffer from problems in versions management for both replication and experimental material. These data provide information of interest to start a research about adoption of the paradigm of software configuration management inside the management of the experimental material in ESE.

Clinical databases often comprise noisy, inconsistent, missing, imbalanced and high dimensional data. These challenges may reduce the performance of DM techniques. Data preprocessing is, therefore, essential step in order to use DM algorithms on these medical datasets as regards making it appropriate and suitable for mining. The objective is to carry out a systematic mapping study in order to review the use of preprocessing techniques in clinical datasets. As results, 110 papers published between January 2000 and March 2017 were, selected, analyzed and classified according to publication years and channels, research type and the preprocessing tasks used. This study shows that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade and a significant number of the selected studies used data reduction and cleaning preprocessing tasks.

Software ecosystems (SECOs) and open innovation processes have been claimed as a way forward for the software industry. A proper understanding of requirements is as important for these IT-systems as for more traditional ones. This paper presents a mapping study on the issues of requirements engineering and quality aspects in SECOs and analyzes emerging ideas. Our findings indicate that among the various phases or subtasks of requirements engineering, most of the SECO specific research has been accomplished on elicitation, analysis, and modeling. On the other hand, requirements selection, prioritization, verification, and traceability has attracted few published studies. Among the various quality attributes, most of the SECOs research has been performed on security, performance and testability. On the other hand, reliability, safety, maintainability, transparency, usability attracted few published studies. The paper provides a review of the academic literature about SECO-related requirements engineering activities, modeling approaches, and quality attributes, positions the source publications in a taxonomy of issues and identifies gaps where there has been little research., Comment: Journal of Information Technology Research (JITR) 11(1)

Context: Model comparison plays a central role in many software engineering activities. However, a comprehensive understanding about the state-of-the-art is still required. Goal: This paper aims at classifying and performing a thematic analysis of the current literature. Method: For this, we have followed well-established empirical guidelines to define and perform a systematic mapping study. Results: Some studies (14 out of 40) provide generic model comparison techniques, rather than specific ones for UML diagrams. Conclusion: Fine-grained techniques are still required to support ever-present and complex model comparison tasks during the evolution of design models.

Background: Mining Software Process (MSP) helps distill important information about software process enactment from software data repositories. An increasing amount of research effort is being dedicated to MSP. These studies differ in various aspects (e.g., topics, data, and techniques) of MSP. Objective: We aim to study the state of the art on MSP from following aspects, i.e., research topics, data sources, data types, mining techniques, and mining tools. Method: We conducted a systematic mapping study on the research relevant to MSP at both microprocess and macroprocess levels. Results: Our mapping study identified 40 relevant studies that can be grouped into microprocess and macroprocess levels. The identified mining techniques have been mapped onto the associated mining tools that fall into four types. Driven by the three research questions which represented in a meta-model, the findings revealed the correlations among the research topics, data sources, data types, mining techniques, and mining tools. Conclusion: It is observed that in order to discover the software process model or map, the main data source is from industrial project. Current mining techniques for microprocess research are mostly business process mining or sequence mining techniques used to recover descriptive software process. In addition, various machine learning algorithms and novel proposed methods are used to improve the accuracy of macroprocess level factors (e.g., software effort estimation).

The mining of software repositories has provided significant advances in a multitude of software engineering fields, including defect prediction. Several studies show that the performance of a software engineering technology (e.g., prediction model) differs across different project repositories. Thus, it is important that the project selection is replicable. The aim of this paper is to present STRESS, a semi-automated and fully replicable approach that allows researchers to select projects by configuring the desired level of diversity, fit, and quality. STRESS records the rationale behind the researcher decisions and allows different users to re-run or modify such decisions. STRESS is open-source and it can be used used locally or even online (www.falessi.com/STRESS/). We perform a systematic mapping study that considers studies that analyzed projects managed with JIRA and Git to asses the project selection replicability of past studies. We validate the feasible application of STRESS in realistic research scenarios by applying STRESS to select projects among the 211 Apache Software Foundation projects. Our systematic mapping study results show that none of the 68 analyzed studies is completely replicable. Regarding STRESS, it successfully supported the project selection among all 211 ASF projects. It also supported the measurement of 100 projects characteristics, including the 32 criteria of the studies analyzed in our mapping study. The mapping study and STRESS are, to our best knowledge, the first attempt to investigate and support the replicability of project selection. We plan to extend them to other technologies such as GitHub.

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.

There is a growing interest of using technologies to propose solutions for healthcare issues. One of such issues is the incidence of chronic diseases, which are responsible for a considerable proportion of worldwide mortality. It is possible to prevent the development of such diseases using tools and methods that instruct the population. To achieve this, mobile games provide a powerful environment for teaching different subjects to user, without them actively knowing that they are learning new concepts. Despite the growing interest of using mobile games in healthcare, more specifically by patients with chronic diseases, in the best of our knowledge there are no studies that address the current research being published in the area. To close this gap, we carried out a systematic mapping study to synthesize an overview of the area. Five databases were searched and more than 1200 studies were analyzed and filtered. Among them, 17 met the the inclusion and exclusion criteria defined in this work. The results show that there is still room for research in this area, since the studies focus on a younger audience rather than proposing solutions for all ages. Furthermore, the number of chronic conditions being addressed is still small, obesity and diabetes are prevalent. Besides, the full capacity of game features that foster learning through games are not being employed, the majority of games proposed by the articles encompass less than half of these features.

Search phase is considered as one of the most important steps in conducting secondary studies such as systematic literature reviews and mapping studies. In recent times, automatic search in digital libraries and academic search engines has been the preferred method of search phase execution for most software engineering related secondary studies. However, there are no previous studies that report or evaluate the secondary study relevant search features of these electronic data sources. We perform a feature analysis (screening mode) based evaluation of five widely used digital libraries (IEEE Xplore, ACM DL, SpringerLink, ScienceDirect and Wiley) in terms of their respective features required to support the search phase of secondary studies. We identify a total of 68 search related features and conduct a comprehensive exploration into their execution behaviors. The overall work presents a useful caution guide for systematic reviewers who plan to use the identified features for executing various search phase steps of their secondary studies.

Support to the quality attribute (QA) & metric selection process.Maintainability is the most studied QA for most domains and development phases.Quality attributes are usually assessed through a correlation to a single metric.Metrics are validated in empirical settings and may lack theoretical validity. Developing a plan for monitoring software quality is a non-trivial task, in the sense that it requires: (a) the selection of relevant quality attributes, based on application domain and development phase, and (b) the selection of appropriate metrics to quantify quality attributes. The metrics selection process is further complicated due to the availability of various metrics for each quality attribute, and the constraints that impact metric selection (e.g., development phase, metric validity, and available tools). In this paper, we shed light on the state-of-research of design-time quality attributes by conducting a mapping study. We have identified 154 papers that have been included as primary studies. The study led to the following outcomes: (a) low-level quality attributes (e.g., cohesion, coupling, etc.) are more frequently studied than high-level ones (e.g., maintainability, reusability, etc.), (b) maintainability is the most frequently examined high-level quality attribute, regardless of the application domain or the development phase, (c) assessment of quality attributes is usually performed by a single metric, rather than a combination of multiple metrics, and (d) metrics are mostly validated in an empirical setting. These outcomes are interpreted and discussed based on related work, offering useful implications to both researchers and practitioners.

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.

Context-of-use is a vital consideration when evaluating the usability of mobile applications. Thus, when defining sets of heuristics for the usability evaluation of mobile applications, a common practice has been to include one or more heuristics that consider context-of-use. Yet, most evaluations are conducted within usability labs. Consequently, the aim of this research is to question the utility of attempting to include inherently complex areas of context-of-use within limited sets of mobile application usability heuristics. To address this, a mapping study uncovered six sets of heuristics that can be applied to mobile application usability evaluations. A within-subjects empirical test with six Human-Computer Interaction practitioners evaluated a well-known travel mobile application using three sets of the mapped heuristics. The study found that the common practice of including context-of-use within mobile application usability heuristics is an ineffective approach.