Software Testing on Microservices-Based Systems: A Systematic Literature Review and Thematic Analysis

The Microservice Architecture (MSA) is gaining popularity in the industry and literature as a raising research topic. This architectural style brings advantages such as independent development, testing, and deployment. However, it is also plagued with many challenges due to its distributed nature. In the case of testing, complex dependencies, heterogeneous execution environments, and independent development teams make it difficult to test microservices using traditional methods and techniques. Our goal is to provide a collection of tests, tools, state-of-the-art solution proposals, as well as relevant challenges. To achieve this, we conducted a Systematic Literature Review following the guidelines presented by Kitchenham. We searched through search engines including IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Wiley Online Library. In total, we retrieved 13,556 articles and thoroughly analyzed them. Through this process, we identified 44 primary studies that were suitable for data extraction and synthesis. From our analysis, we identified 12 testing strategies that are applicable to the MSA. Additionally, we found proposed solutions for automated testing along with microservices benchmarks. Furthermore, we discovered a set of testing tools and challenges specifically related to MSA-based system testing. The main challenges we encountered include automated testing, performance testing, and finding realistic MSA-based systems. Our study aims to benefit software engineering practitioners and researchers interested in microservices testing by providing a comprehensive understanding of the current landscape of testing for MSA-based systems.

Literature review is a fundamental part of a research process, and systematic protocols for this activity have been used for a long time, mainly in the field of health. Specifically in the Computer Science Education area, the use of systematic literature review has grown. One of the steps in a systematic literature review (SLR) is the selection of academic databases in which to search for articles. There are several databases with academic documents that may be relevant to SLR, for example: Google Scholar, which indexes different types of documents, such as articles, dissertations, theses, and others; Scopus and Web of Science are large databases that index articles from different conferences and journals. ACM Digital Library and IEEE Xplore are also important sources of information in the field of Computer Education. These tools have different characteristics, some charge a fee, others have only information about the title and authors and do not have access to the full article, others have advanced features, with many filters. In this context, this article presents the following research questions: RQ1) What metadata can be extracted automatically from the databases?; RQ2) What kind of visualization tools are available?; RQ3) Do the documents returned by the databases cover the research topic?; RQ4) Do the databases have papers from the main CSE venues?; and RQ5) How many databases are required to perform a literature review in CSE? To answer these questions we used five academic databases: Google Scholar, Scopus, Web of Science, ACM Digital Library, and IEEE xplore. Regarding the results, Scopus and Web of Science have the best visualization of the documents and a robust query engine, however those academic databases are not free. ACM Digital library, IEEE Xplore, Scopus and Web of Science allow the automatic download of the papers’ metadata (author, title, abstract, affiliation and others). Specifically in the field of Computer Science Education, the ACM Digital Library and the IEEE Xplore have important papers from conferences (SIGCSE and FIE) and journals (ACM Transaction on Education and IEEE Transaction on Education). In this full paper, the results will be presented to help researchers to choose the most appropriate academic databases based on their requirements and available options.

Digital health interventions have become increasingly popular in recent years, expanding the possibilities for treatment for various patient groups. In clinical research, while the design of the intervention receives close attention, challenges with research participant engagement and retention persist. This may be partially due to the use of digital health platforms, which may lack adequacy for participants. This systematic literature review aims to investigate the relationship between digital health platforms and participant engagement and retention in clinical research. It will map and analyze key definitions of engagement and retention, as well as identify design characteristics that influence them. We will carry out a mixed methods systematic literature review, analyzing qualitative and quantitative studies. The search strategy includes the electronic databases PubMed, IEEE Xplore, CINAHL, Scopus, Web of Science, APA PsycINFO, and the ACM Digital Library. The review will encompass studies published between January 2018 and June 2024. Criteria for inclusion will be the presence of digital health care interventions conducted through digital health platforms like websites, web and mobile apps used by patients, and informal caregivers as research participants. The main outcome will be a narrative analysis with key findings on the definitions of participant engagement and retention and critical factors that affect them. Quality assessment and appraisal will be done through the Mixed-Methods Assessment Tool. Data analysis and synthesis will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram. Quantitative data will be qualified and integrated into qualitative data, which will be analyzed using thematic analysis and synthesis. The study expects to map and summarize critical definitions of participant engagement and retention, and the characteristics of digital health platforms that influence them. The systematic review is expected to be completed in June 2025. This systematic review will contribute to the growing discussion on how the design of digital health intervention platforms can promote participant engagement and retention in clinical research. PROSPERO CRD42024561650; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=561650. PRR1-10.2196/65099.

The Internet of Things promises transformative benefits for developing countries, ranging from fairly mundane efficiency improvements to markedly enhanced service delivery, yet actual adoption and long-term institutionalization remain slow and decidedly uneven, largely because of persistent security challenges. Privacy breaches, weak authentication, network vulnerabilities, and generally low levels of trust repeatedly emerge as decisive barriers, particularly in resource-constrained environments where even small failures can, in fact, undermine confidence quite severely. This study addresses the gap in synthesizing the security determinants that influence both the adoption and the deeper embedding of IoT technologies. A systematic literature review, guided by PRISMA, was conducted across IEEE Xplore, Scopus, Web of Science, SpringerLink, ACM Digital Library, and Taylor & Francis Online, identifying 25 peer-reviewed studies published between 2020 and 2025. Data extraction focused on security determinants, sectoral focus, regional distribution, and adoption patterns, so the analysis would retain a clear and coherent scope. Deductive coding covering privacy, authentication, and network security was combined with inductive themes related to trust and risk perception, and the findings were synthesized through frequency counts, thematic analysis, and cross-tabulation. Results highlight four dominant security clusters: privacy and confidentiality, trust and risk perception, authentication and access control, and network or infrastructure security. Privacy concerns were most frequently reported, followed quite closely by trust, authentication, and network vulnerabilities. Healthcare and education sectors appear most sensitive to privacy, while Asia dominates the evidence base, with Africa and Latin America still underrepresented. The study concludes that security concerns, while sometimes manageable in pilot phases, become critical barriers to scaling and institutionalization, so policymakers must priorities robust governance, trust-building, and capacity development to realize IoT’s potential in developing-country contexts.

Adoption of Ambient Assisted Living (AAL) technologies for geriatric healthcare is suboptimal. This study aims to present the AAL Adoption Diamond Framework, encompassing a set of key enablers/barriers as factors, and describe our approach to developing this framework. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. SCOPUS, IEEE Xplore, PubMed, ProQuest, Science Direct, ACM Digital Library, SpringerLink, Wiley Online Library and grey literature were searched. Thematic analysis was performed to identify factors reported or perceived to be important for adopting AAL technologies. Of 3717 studies initially retrieved, 109 were thoroughly screened and 52 met our inclusion criteria. Nineteen unique technology adoption factors were identified. The most common factor was privacy (50%) whereas data accuracy and affordability were the least common factors (4%). The highest number of factors found per a given study was eleven whereas the average number of factors across all studies included in our sample was four (mean = 3.9). We formed an AAL technology adoption framework based on the retrieved information and named it the AAL Adoption Diamond Framework. This holistic framework was formed by organising the identified technology adoption factors into four key dimensions: Human, Technology, Business, and Organisation. To conclude, the AAL Adoption Diamond Framework is holistic in term of recognizing key factors for the adoption of AAL technologies, and novel and unmatched in term of structuring them into four overarching themes or dimensions, bringing together the individual and the systemic factors evolving around the adoption of AAL technology. This framework is useful for stakeholders (e.g., decision-makers, healthcare providers, and caregivers) to adopt and implement AAL technologies.

BackgroundCare robots have been proposed in response to nursing shortages in assisted living facilities (ALFs) and the growing population of older adults. While the use of care robots may improve the general health and well-being of older adults, their introduction changes the work of nursing staff fundamentally, and it has implications for the entire health care system. In developing such technology, it is important to include end users, but so far, the nursing staff’s perspectives have largely been ignored.ObjectiveThis study aims to examine the literature on nursing staff’s attitudes, needs, and preferences related to the use of care robots in ALFs, in order to discover gaps in the literature and guide future research.MethodsThis review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 protocol. On May 12, 2023, we searched PubMed, CINAHL Plus with Full Text, PsycINFO, the IEEE Xplore Digital Library, and the ACM Digital Library using predetermined search terms. Included publications, written in English, focused on the predevelopment phase, in which information was gathered on nursing staff’s attitudes, needs, and preferences regarding care robots for ALFs. Publications were excluded if they did not provide peer-reviewed empirical data. The studies’ findings were summarized, coded, and analyzed into major themes using thematic analysis and narrative synthesis. Their quality was assessed using McGill University’s Mixed Methods Appraisal Tool and the Joanna Briggs Institute’s critical appraisal tools.ResultsThe final sample included 15 studies. Most of the studies (n=11, 73%) were rated as good quality; however, there was a general lack of reporting on important methodological decisions and sample characteristics. Nursing staff desired care robots that could assist with physically demanding tasks and reduce their workload but had mixed feelings on whether robots could or should assist with social tasks. In addition, nursing staff are concerned about the ethics of care robots, as well as about their safety, accessibility, and operability. The nursing staff’s culture, qualification, and role in the facility may influence their perspectives of care robots. The studies lacked theory-driven designs and large sample sizes. Eight (53%) studies mentioned using a participatory design approach, but a lack of established criteria for what constitutes participatory design leads to varying degrees of methodological quality.ConclusionsThere was consensus among nursing staff that care robots should serve as nursing assistants to reduce workload. Whether robots could or should assist with social tasks remains a question. Further research is needed to mitigate nursing staff’s concerns and understand the socioecological factors that influence their perspectives of care robots and their adoption in ALFs. In addition, theory-driven and large sample size study designs are necessary, as well as work to develop clear criteria for related participatory design research.

The Hyperledger Fabric (HF) framework is widely studied for securing electronic health records (EHRs) in the healthcare sector. Despite the various cross-domain blockchain technology (BCT) applications, little is known about the role of the HF framework in healthcare. The purpose of the systematic literature review (SLR) is to review the existing literature on the HF framework and its applications in healthcare. This SLR includes literature published between January 2015 and March 2023 in the ACM digital library, IEEE Xplore, SCOPUS, Springer, PubMed, and Google Scholar databases. Following the inclusion and exclusion criteria, a total of 57 articles emerged as eligible for this SLR. The HF framework was found to be useful in securing health records coming from the Internet of Medical Things (IoMT) and many other devices. The main causes behind using the HF framework were identified as privacy and security, integrity, traceability, and availability of health records. Additionally, storage issues with transactional data over the blockchain are reduced by the use of the HF framework. This SLR also highlights potential future research trends to ensure the high-level security of health records.

With the increment in software development complexity, approaches such as domain-driven design (DDD) are needed to tackle contemporary business domains. DDD is already being used in various software projects with different architectural styles. Evidence in the literature suggests using DDD to mitigate challenges in the Microservices Architecture style. Although some studies have explored the decomposition of business domains or legacy monolithic systems into microservices, there is a lack of concrete information regarding the practical implementation of DDD in this architectural style. This has led to ambiguity regarding the use of DDD in developing microservices-based systems. The paper systematizes findings on the purpose of using DDD, its patterns, associated technologies, and techniques. A systematic literature review of 35 articles was conducted. Thematic analysis was employed to identify five high-order themes and 11 themes. Based on our analysis, we have concluded that microservice identification emerges as the primary motivation behind developers' adoption of DDD. However, it is important to note that this utilization manifests with variations across the literature. Finally, our analysis found several challenges and benefits in the use of DDD in microservices development.

The microservice architecture structures an application as a collection of small autonomous services, enhancing development practices and maintainability. It impacts system performance and scalability, which are influenced by the architectural design, the system deployment, and usage. In this paper, we present a systematic literature review (SLR) on the assessment of performance and scalability in the microservice architecture, aiming to identify research gaps and assess the current state of knowledge. The review protocol screened 801 studies, selecting 29 primary studies for detailed analysis. We perform comparisons of studies and try to identify the main gaps in current research and suggest areas for further investigation. The main conclusion is that the current research partially addresses the dimensions associated with the performance and scalability qualities of microservice systems. In particular, there is a lack of comparative studies of architectural patterns and styles or comparable assessment models and methods. Therefore, a systematic approach is lacking and the paper reports a set of open research trends to guide researchers in the area of microservice-based software architecture quality assessment. • The paper answers the following research question: How does current research on microservice architectures, performance, and scalability address the architectural dimensions used in end-to-end scalability assessment? • We performed a systematic literature review on the performance and scalability assessment of microservice architectures, where we screened 801 studies to select 29 primary studies that were used to provide an overview of the state of the art. • We introduced a systematic framework for the analysis of performance and scalability assessment methodologies, and analyzed the 29 primary studies according to this analysis framework. • We conclude that current research mainly focuses on some of the dimensions of the microservice architecture, namely the aspects associated with deployment architectures. In addition, it is difficult to compare the different studies because they address different aspects of the dimensions. • We report a set of open research trends to guide researchers in the area of microservice-based software architecture quality assessment.

Background: Software testing and software security have become one of the most important parts of an application. Many studies have explored each of these topics but there is a gap wherein the relation of software security and software testing in general has not been explored. Objective: This study aims to conduct a systematic literature review to capture the current state-of-the-art in software testing related to security. Methods: The search strategy obtains relevant papers from IEEE Xplore and ScienceDirect. The results of the search are filtered by applying inclusion and exclusion criteria. Results: The search results identified 50 papers. After applying the inclusion/exclusion criteria, we identified 15 primary studies that discuss software security and software testing. We found approaches, aspects, references, and domains that are used in software security and software testing. Conclusion: We found certain approach, aspect, references, and domain are used more often in software security testing Keywords: Software security, Software testing, Security testing approach, Security threats, Systematic literature review

Understanding and addressing quality attributes of microservices architecture: A Systematic literature review

Aim/Purpose. This study addresses the research question: “What are the developmental phases of Information Technology in the industry?” Existing research has explored the impact of Information Technology (IT) on specific industries. However, it is essential to understand the evolution of IT within industries, its influence on the workforce, and technological advancements. Addressing this knowledge gap will enhance future workforce development and IT integration across diverse sectors. Background. IT can significantly transform industries and drive innovation to meet client demands. Understanding IT phases in industry through literature helps governments and businesses worldwide recognize its importance. This knowledge can guide strategies to address the shortage of highly skilled workers by prioritizing education and training programs to meet future demands. Methodology The methodology involved a systematic literature review of 110 IEEE Xplore, ACM Digital Library, and Google Scholar articles. Thematic analysis was used to understand the development of IT in distinct phases since the 1990s. This development has resulted in a continuous demand for new workforce skills and evolving customer expectations. Contribution. This study aims to fill the knowledge gap by enhancing our understanding of how evolving IT influences the industry and shapes IT jobs and skills. It provides a historical perspective, illustrating how IT advancements have led to new applications to meet changing needs. Additionally, the study identifies patterns in the evolving IT skill requirements due to technological advancements and discusses implications for curriculum development and higher education. Findings. The study identified three significant phases through a systematic literature review and thematic analysis. The first phase, “Advent of Industry IT” (1990-2000), established the digital framework and built essential systems and infrastructure. The second phase, “Connectivity & Information Revolution” (2000-2010), saw exponential internet growth, transforming information access and communication. The third phase, “Emerging Industry IT” (2010-present), focuses on artificial intelligence, automation, and data-driven insights, continuing to disrupt and transform industries. Recommendations for Practitioners. The changing phases of IT within the industry should inform the development of innovative programs. These programs should address diverse skill sets across eras, preparing the workforce for evolving job roles in various sectors, such as healthcare in North America, automotive manufacturing in Japan, telecommunications in Africa, and innovations in other parts of the world. Recommendations for Researchers. Researchers can conduct longitudinal studies to explore the ongoing evolution of IT, tracking its trajectory beyond current delineated phases to understand future trends. Comparative studies across various industries can assess how IT evolution varies among sectors and delve deeper into its practical implications. Researchers can also conduct impact assessment studies to determine how various IT phases directly affect organizational strategy, worker dynamics, and organizational structures across industries. Examples include logistics in the Netherlands, retail in the United Kingdom, and agriculture in Brazil. Impact on Society. Policymakers and planners can use knowledge of these phases to predict technological shifts and industry trends. This knowledge helps develop strategies and policies supporting entrepreneurship, education and training alignment, technical innovation, economic growth, and job creation in line with the changing IT landscape. Examples of policies include Singapore’s Smart Nation initiative, Germany’s Industry 4.0 strategy, Ghana’s digitization efforts, and India’s Digital India campaign. Future Research. Future research can provide a thorough understanding of the evolutionary patterns of IT within sectors by validating the study through various datasets and conducting in-depth examinations of individual industries. This will contribute to a deeper understanding of sector-specific IT evolution and their varying impact on societal interactions and industry dynamics. Comparative studies across various sectors, such as logistics in the Netherlands, retail in the United Kingdom, and agriculture in Brazil, can assess how IT evolution varies.

Aim/Purpose. This study addresses the research question: “What are the developmental phases of Information Technology in the industry?” Existing research has explored the impact of Information Technology (IT) on specific industries. However, it is essential to understand the evolution of IT within industries, its influence on the workforce, and technological advancements. Addressing this knowledge gap will enhance future workforce development and IT integration across diverse sectors. Background. IT can significantly transform industries and drive innovation to meet client demands. Understanding IT phases in industry through literature helps governments and businesses worldwide recognize its importance. This knowledge can guide strategies to address the shortage of highly skilled workers by prioritizing education and training programs to meet future demands. Methodology The methodology involved a systematic literature review of 110 IEEE Xplore, ACM Digital Library, and Google Scholar articles. Thematic analysis was used to understand the development of IT in distinct phases since the 1990s. This development has resulted in a continuous demand for new workforce skills and evolving customer expectations. Contribution. This study aims to fill the knowledge gap by enhancing our understanding of how evolving IT influences the industry and shapes IT jobs and skills. It provides a historical perspective, illustrating how IT advancements have led to new applications to meet changing needs. Additionally, the study identifies patterns in the evolving IT skill requirements due to technological advancements and discusses implications for curriculum development and higher education. Findings. The study identified three significant phases through a systematic literature review and thematic analysis. The first phase, “Advent of Industry IT” (1990-2000), established the digital framework and built essential systems and infrastructure. The second phase, “Connectivity & Information Revolution” (2000-2010), saw exponential internet growth, transforming information access and communication. The third phase, “Emerging Industry IT” (2010-present), focuses on artificial intelligence, automation, and data-driven insights, continuing to disrupt and transform industries. Recommendations for Practitioners. The changing phases of IT within the industry should inform the development of innovative programs. These programs should address diverse skill sets across eras, preparing the workforce for evolving job roles in various sectors, such as healthcare in North America, automotive manufacturing in Japan, telecommunications in Africa, and innovations in other parts of the world. Recommendations for Researchers. Researchers can conduct longitudinal studies to explore the ongoing evolution of IT, tracking its trajectory beyond current delineated phases to understand future trends. Comparative studies across various industries can assess how IT evolution varies among sectors and delve deeper into its practical implications. Researchers can also conduct impact assessment studies to determine how various IT phases directly affect organizational strategy, worker dynamics, and organizational structures across industries. Examples include logistics in the Netherlands, retail in the United Kingdom, and agriculture in Brazil. Impact on Society. Policymakers and planners can use knowledge of these phases to predict technological shifts and industry trends. This knowledge helps develop strategies and policies supporting entrepreneurship, education and training alignment, technical innovation, economic growth, and job creation in line with the changing IT landscape. Examples of policies include Singapore’s Smart Nation initiative, Germany’s Industry 4.0 strategy, Ghana’s digitization efforts, and India’s Digital India campaign. Future Research. Future research can provide a thorough understanding of the evolutionary patterns of IT within sectors by validating the study through various datasets and conducting in-depth examinations of individual industries. This will contribute to a deeper understanding of sector-specific IT evolution and their varying impact on societal interactions and industry dynamics. Comparative studies across various sectors, such as logistics in the Netherlands, retail in the United Kingdom, and agriculture in Brazil, can assess how IT evolution varies.

Purpose: The growing burden of mental disorder demands new strategies for diagnosis, monitoring, and treatment. This systematic literature review (SLR) critically examines peer-reviewed studies on the applications of AI technologies in mental health to address the main research question: "What AI technologies, sources of data, and outcomes are prevalent in current AI-based mental health interventions, and with which mental disorders are they primarily concerned?". Methods: This SLR reviewed 78 studies published after 2020 from major academic databases such as PubMed, IEEE Xplore, ScienceDirect, Springer, and ACM Digital Library. Inclusion/exclusion criteria and a strict protocol with structured data extraction were used to identify AI methods, data sources, targeted mental disorders, and outcomes reported. A common use of supervised machine learning, deep learning (e.g., CNNs, RNNs, LSTMs, transformer models), and multimodal approaches were identified by the review. Results: Key results indicate that classic machine learning techniques averaged between 75% and 89%, deep learning and transformer models having high diagnostic accuracy, with a few LLM-based models having an accuracy of up to 90.2% and multimodal models having F1-scores above 90%. The target disorders most frequently identified were depression and anxiety, and the sources of data used were mostly based on social media, clinical interviews, EEG, and multimodal datasets. Conclusions: Despite the encouraging results limitations like data imbalance, privacy issues, the requirement of more heterogeneous data, and ethical issues remains. This review highlights these challenges and proposes directions to enhance AI’s effective use in mental healthcare.

Everyday thousands of cardiac surgeries are performed worldwide. This article reviews numerous studies on the use of Convolutional Neural Networks and their impact on vascular surgery. The development of computer systems supported by convolutional neural networks that contribute to vascular surgeries represents an important deviation from traditional approaches to the performance of surgical interventions, either through computed tomography verification or the method in which a surgeon ceases to participate in this type of surgical interventions. A systematic literature review (SLR) regarding convolutional neural networks for use in vascular surgeries was conducted for research from 2016 to 2021. The search strategy identified 15,505 papers from various search sources such as ACM Digital Library, EBSCOhost, Google Scholar, IEEE Xplore, MDPI, Microsoft Academic, ProQuest, ScienceDirect, Scopus, Springer, Web of Science, and Wiley Online Library, from which only 70 papers were considered (selected) based on exclusion criteria. The SLR focused on recent studies on convolutional neural networks where their use in vascular surgeries has been identified. This SLR provides readers with a mapping of all identified findings, making a comparison by relevance in regard to their own settings and possible scenarios. Therefore, it is hoped that this research will help other researchers to understand the current status of Convolutional Neural Networks and their application in Vascular Surgeries.