In the rapidly evolving business landscape, blockchain technology emerges as a key innovator, enhancing trust, transparency, and security. However, the unique features of blockchain pose challenges in developing blockchain-based software (BSD) systems, demanding improvements in conventional software development processes. This study aims to identify BSD process areas and develop a success probability prediction framework, enhancing BSD process success and progression. We conducted a comprehensive literature survey and a questionnaire-based survey with practitioners to identify BSD process areas and gather training data. The study employs the Grey Wolf Optimizer (GWO) combined with the Naive Bayes Classifier to create a success probability prediction framework for BSD processes. Our research identifies 47 BSD process areas, categorized across five software process improvement (SPI) stages: initial, managed, defined, quantitatively managed, and optimizing. The GWO algorithm facilitates the design of a predictive framework, assessing the success probability of each stage, encompassing various process areas. The framework also prioritizes process areas for each stage, helping practitioners identify critical areas considering implementation cost and success probability. Organizations using BSD can leverage this framework to improve their BSD processes. This study contributes to blockchain technology applications in software development, offering a systematic, predictive approach to augment the effectiveness and success rate of BSD processes.

Serverless computing is an umbrella paradigm that encompasses multiple service categories, including Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS). It has reshaped cloud application development by abstracting infrastructure management tasks such as provisioning, scaling, and operational maintenance from developers, thereby enabling greater focus on application logic. To realize these benefits, organizations need to select among various FaaS platforms such as AWS Lambda, Google Cloud Functions, Azure Functions, and Apache OpenWhisk to develop their serverless applications. Nevertheless, the increasing number of available FaaS platforms and their heterogeneous characteristics (e.g., timeout constraints, cold-start behavior, memory and package size limits, and event-source integration) make platform selection complex and knowledge-intensive for the decision makers. The main objective of this study is to support decision-makers in selecting appropriate FaaS platforms by designing an effective and systematic decision model. The model aims to simplify the selection process, reduce time and effort, and provide deeper insights into platform suitability based on specific organizational requirements. We employed a mixed-method research design to develop a decision model for the FaaS platforms selection problem. The model contains a mapping of 219 features across 16 FaaS platforms. The model was evaluated through five real-world case studies conducted at different software development companies. It suggests and prioritizes more than one FaaS platform based on the participants’ requirements. The case study participants reported that the model offered valuable insights and significantly simplified the selection process by reducing the time and costs associated with the decision-making process. We observe in the empirical evidence that decision-makers can make more rational, efficient, and effective decisions with the decision model. Additionally, the model provides reusable insights that can support future research, such as developing new frameworks and solutions for emerging challenges in serverless computing.

ABSTRACT Background The rapid adoption of low‐code (LC) and no‐code (NC) tools has transformed the software development landscape, offering faster development cycles and enabling non‐programmers to contribute to software creation. Objective This study systematically maps the effects of LC/NC tools on developers, addressing the critical need to understand whether these tools serve as a valuable aid or a potential threat to their roles. Methods By analyzing how LC/NC tools support development processes, the challenges they present, and their potential to replace traditional developers, this research fills a key knowledge gap by performing a systematic mapping study. Results The findings reveal that while LC/NC tools simplify certain tasks, they do not replace the need for professional developers to handle complex technical challenges, change management, and system integration. Conclusions Rather than posing a threat, these tools complement and enhance developers' expertise, empowering them to focus on more advanced aspects of software creation. This study provides valuable insights for the professional developer community and the broader software industry, advocating for the thoughtful integration of LC/NC tools as powerful aids rather than competitors to professional development.

Multimodal data have emerged as a cornerstone for understanding and analyzing complex human behaviors, particularly in mental health monitoring. In this study, we propose a deep learning-driven behavioral modeling framework for intelligence of social things (IoST)-based mental health monitoring and intervention, designed to integrate and analyze multimodal data—including text, speech, and physiological signals—captured from interconnected IoST devices. The framework incorporates an adaptive attention-based fusion mechanism that dynamically adjusts the contribution of each modality based on contextual relevance, enhancing the robustness of multimodal integration. Additionally, we employ a temporal-aware recurrent neural network with an attention mechanism to capture long-term dependencies and evolving behavioral patterns, ensuring precise mental health state prediction. To validate the framework, extensive experiments were conducted using three publicly available datasets: DAIC-WOZ, SEED, and MELD. Comparative experiments demonstrate the superior performance of the proposed framework, achieving state-of-the-art accuracy of 93.5%, F1-scores of 92.9%, and AUC-ROC of 0.95 values. Ablation studies highlight the critical roles of attention mechanisms and multimodal integration, showcasing significant performance improvements over single-modality and simplified fusion approaches. These findings underscore the framework’s potential as a reliable and efficient tool for real-time mental health monitoring in IoST environments, paving the way for scalable and personalized interventions.

Context: Serverless computing has revolutionized cloud computing by enabling organizations to streamline workflows, dynamically scale operations, and eliminate the need to manage the underlying infrastructure. These promises led organizations to migrate their applications to serverless. However, the migration process is inherently complex, demanding significant architectural modifications and organizational restructuring. While existing research largely emphasizes the technical aspects of serverless computing such as improving cold start, it often overlooks the socio-technical and decision-making dimensions. Objective: This study aims to identify and map the decision-making processes involved in serverless migration along technical, and organizational dimensions. Method: Using the Socio-Technical Grounded Theory (STGT), we analyzed 22 migration cases through 25 interviews with practitioners and insights from 31 blog posts. This approach offered a thorough and nuanced insight into stakeholder decision-making throughout the migration process. Results: The study identifies 29 key decision points categorized into procedural and outcome decisions. These include assessing technical feasibility, executing the technical migration to serverless, and restructuring organizational processes to support the transition. Conclusions: The proposed decision-making framework provides practical guidance for organizations undertaking serverless migrations by integrating technical and human-centric perspectives. It emphasizes fostering stakeholder alignment, adopting iterative decision-making practices, and enhancing organizational adaptability. The framework provides practitioners with a structured roadmap for successful transitions to serverless adoption by bridging these socio-technical dimensions.

Quantum software engineering is advancing rapidly in parallel with equally ambitious hardware roadmaps. However, systematic evidence on how online audiences perceive these advances remains scarce. We present an exploratory baseline of Twitter sentiment toward quantum computing, using automated (silver‐standard) labels for benchmarking. Six months of English‐language tweets containing the hashtag #Quantum (December 1, 2022 and May 31, 2023) were processed, with #Quantum treated as a proxy for online discourse on quantum computing. We then applied a transparent natural language processing (NLP) methodology combining two zero‐shot lexicon‐based tools (TextBlob and the Valence Aware Dictionary and sEntiment Reasoner [VADER]) with three lightweight supervised classifiers (multinomial naïve Bayes, Rocchio, and perceptron). Following standard preprocessing and a stratified 70/30 train–test split, we do not aim to measure definitive public opinion; rather, our primary contribution is to establish a transparent and reproducible baseline for future benchmarking. In this context, the multinomial naïve Bayes classifier attained a macro F1‐score of 0.88 on the 30% hold‐out set when benchmarked against the TextBlob silver labels. This score captures internal agreement rather than accuracy against human annotation. All five methods converged on a largely—though not universally—positive sentiment orientation (≈78%–81% of nonneutral tweets, depending on the tool). Grounded in the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), we interpret our results as indicating the constructs of curiosity and perceived usefulness, rather than unequivocal adoption readiness. These constructs were not operationalized and serve only as interpretative lenses. By documenting every preprocessing step and model configuration, and making tweet identifiers and code available upon request, the study delivers a reproducible benchmark against which future work can (i) extend the query vocabulary, (ii) incorporate neutral and fine‐grained emotions, (iii) apply cross‐validation protocols, and (iv) evaluate advanced transformer models on manually annotated data. Addressing these four points is essential before making any definitive claims about public opinion.

Abstract Neural architecture search (NAS) has emerged as a promising approach for automating deep learning model design. However, its application in sports analytics faces unique challenges due to the complex interplay between biomechanical patterns, physiological adaptations, and coaching expertise. Traditional NAS methods need help to effectively capture the multifaceted nature of athletic performance, often failing to integrate qualitative coaching insights with quantitative measurements. We introduce ChampionNet, a framework incorporating NAS and large language models to enhance accuracy in predicting athletic performance and tailoring training regimens. Our approach offers three primary contributions: integrating hyperdimensional embedding to capture fine-grained biomechanical features and physiological parameters with exceptional detail, a structure-preserving graph encoding leverages to maintain crucial spatiotemporal relationships in athletic movements, and the novel comprehensiveness of the training graph that models forward performance prediction and backward physiological adaptation pathways. Our experiments on various sports demonstrate that ChampionNet outperforms other models by 2.5% in accuracy and over 61.9% in computational cost. Further insights illustrate the framework's performance with complex patterns and multi-modal data, especially for sports with advanced biomechanical needs. These findings support ChampionNet's effectiveness as an integrative athletic performance optimization solution, highlighting the need for automated architecture search tailored to sports.

Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of containerization in multi-cloud environment. We conducted the Systematic Mapping Study (SMS) on the literature published between January 2013 and July 2024. One hundred twenty one studies were selected and the key results are: (1) Four leading themes on containerization in multi-cloud environment are identified: ‘Scalability and High Availability’, ‘Performance and Optimization’, ‘Security and Privacy’, and ‘Multi-Cloud Container Monitoring and Adaptation’. (2) Ninety-eight patterns and strategies for containerization in multi-cloud environment were classified across 10 subcategories and 4 categories. (3) Ten quality attributes considered were identified with 47 associated tactics. (4) Four catalogs consisting of challenges and solutions related to security, automation, deployment, and monitoring were introduced. The results of this SMS will assist researchers and practitioners in pursuing further studies on containerization in multi-cloud environment and developing specialized solutions for containerization applications in multi-cloud environment.

ABSTRACT Context DevSecOps integrates security into the DevOps project lifecycle, uniting development, operations, and security practices. This integration, while beneficial for developing secure software, introduces complexity from a project management perspective. This study delves into this complexity by examining the 10 knowledge areas of the Project Management Body of Knowledge (PMBOK) within the context of DevSecOps project management. Objective This study aims to explore and understand the application of PMBOK's 10 knowledge areas in managing DevSecOps projects, focusing on the guidelines that are important to consider in integration of security practices throughout the development lifecycle. Method Our research approach involved two phases: Firstly, we developed a theoretical model grounded in DevSecOps guidelines identified from existing literature. Secondly, we conducted a quantitative survey targeting industry practitioners to gather insights into the practical application of the theoretical model. The study involved 138 responses from professionals, which were subsequently analyzed using correlation and Partial Least Squares (PLS) analysis to test the hypotheses posited in the theoretical model. Results The analysis reveals critical insights into the management of DevSecOps projects, highlighting the importance of adhering to specific guidelines to navigate the complexities introduced by the integration of security practices. The empirical data support the theoretical model, underscoring the relevance of PMBOK's knowledge areas in the successful management of DevSecOps projects. Conclusion For organizations committed to the DevSecOps paradigm, it is imperative to consider and implement the identified guidelines. These guidelines not only support the sustainable integration of security practices into DevOps projects but also contribute to the overall success and security of the software developed under this paradigm.

Driver fatigue detection is a critical challenge in Transportation Cyber-Physical Systems (T-CPS), where existing methods often face significant limitations. Conventional approaches typically struggle with issues such as limited accuracy, slow convergence, and high computational costs. These methods often fail to capture the complex temporal and spatial patterns inherent in multimodal physiological signals, such as EEG and EOG, leading to suboptimal performance in real-world scenarios. To address these challenges, we propose a novel approach that leverages Depthwise Separable Convolutional Neural Networks (DSCNNs) for driver fatigue detection. Our method integrates electroencephalogram (EEG) and electrooculogram (EOG) signals through an innovative feature extraction and fusion process, enhancing the system’s ability to detect fatigue with high accuracy and efficiency. The DSCNN model is designed to overcome the limitations of traditional methods by utilizing depthwise separable convolutions, which reduce computational complexity while maintaining robust performance. We validate our approach using the SEED-VIG dataset and the NTHU drowsy driver dataset, which encompasses a range of driving conditions. The DSCNN model outperforms conventional models in both accuracy and computational efficiency. Specifically, DSCNN achieves the highest F1 score and the lowest time per epoch, making it highly suitable for real-time applications in T-CPS. This advancement represents a significant improvement in driver safety by providing more timely and reliable fatigue warnings, thus advancing the capabilities of intelligent transportation systems.

In recent years, the significant increase in enterprise data availability and the progress in Artificial Intelligence (AI) have enabled organizations to address real-world issues through Machine Learning (ML). In this regard, machine learning operations (MLOps) have been proven to be a beneficial strategy for evolving ML models from theoretical ideas to practical solutions of business sector issues. With the knowledge of MLOps being vast and scattered, this research work focuses on the application of MLOps methodologies in sophisticated operational technologies, prioritizing the enhancement of security measures. This research work also discusses the specific challenges in securing ML implementations in such settings and underscores the importance of robust MLOps strategies in ensuring effective security protocols. Moreover, it explains current practices and identified improvement areas, highlighting the importance of MLOps in overcoming obstacles and maximizing the value of ML in operational technology contexts.

DevOps (development and operations) is a set of collaborative practices that automate continuous delivery of new software versions with an aim to reduce the development life cycle and produce quality software products. Security is an important attribute of quality software. Software is secure if it does not allow the confidentiality, integrity, and availability of its data, code, or service to be compromised. In order to take full advantage of DevOps, security needs to play an integral part in the development life cycle of a software. The DevSecOps (development, security, and operations) refers to the integrating security practices within the DevOps process. DevSecOps promotes the shifting security to the early stages of a project. Traditionally, security testing is done towards the end of the software lifecycle. However, fixing issues later in the process is more costly than making sure defects do not happen in the beginning. DevSecOps goes beyond automation, continuous integration, testing and delivery processes, since it also encompasses people. In fact, DevSecOps promotes the collaboration between the development, operations, and security teams. When security comes into DevOps routines, people play an even more relevant role involving the collaboration between those teams and security team. In any organization policies, standards, procedures and code of conducts are designed for people to follow. People are executers of policies. The human factor is one of the major forces behind effectiveness, or failure of a security system. Traditionally, the organizations focus on protecting their infrastructure, from security threats and they ignore human behavior that may result in malicious activities during software development process. Human aspect is considered as one of the major reasons of security vulnerability is due to malicious human behavior, who are involved in DevSecOps process; human may make mistakes due to lack of security perceptions, skills, and knowledge.

Over recent years, DevOps has gained more attention within the software industry, owing to its pivotal role in facilitating continuous software delivery. The significant demand for DevOps practitioners necessitates substantial adjustments in conventional software engineering courses in higher educational institutions. Recent studies have highlighted DevOps principles, challenges, best practices, and tools to facilitate continuous delivery and deployment pipelines from an industrial perspective. As numerous universities are now introducing courses related to DevOps for students majoring in software engineering and computer science, this high demand for DevOps practitioners requires non-trivial adjustments in traditional software engineering courses and educational methodologies, including practices for teaching DevOps, training in DevOps, the level to which DevOps is currently taught and applied to measure the gap between teaching practices compared to industrial requirements. Our vision paper aims to highlight the crucial need for updated policies and tools to improve DevOps training in higher education, providing guidelines. By aligning academic curricula with industry standards, we can ensure students are better equipped for the demands of the workforce, fostering a seamless transition from academia to industry and promoting innovation in software engineering practices.

In the era of 6G, developing and managing software requires cutting-edge software engineering (SE) theories and practices tailored for such complexity across a vast number of connected edge devices. Our project aims to lead the development of sustainable methods and energy-efficient orchestration models specifically for edge environments, enhancing architectural support driven by AI for contemporary edge-to-cloud continuum computing. This initiative seeks to position Finland at the forefront of the 6G landscape, focusing on sophisticated edge orchestration and robust software architectures to optimize the performance and scalability of edge networks. Collaborating with leading Finnish universities and companies, the project emphasizes deep industry-academia collaboration and international expertise to address critical challenges in edge orchestration and software architecture, aiming to drive significant advancements in software productivity and market impact.

Abstract The emergence of 6G networks initiates significant transformations in the communication technology landscape. Yet, the melding of quantum computing (QC) with 6G networks although promising an array of benefits, particularly in secure communication. Adapting QC into 6G requires a rigorous focus on numerous critical variables. This study aims to identify key variables in secure quantum communication (SQC) in 6G and develop a model for predicting the success probability of 6G-SQC projects. We identified key 6G-SQC variables from existing literature to achieve these objectives and collected training data by conducting a questionnaire survey. We then analyzed these variables using an optimization model, i.e., Genetic Algorithm (GA), with two different prediction methods the Naïve Bayes Classifier (NBC) and Logistic Regression (LR). The results of success probability prediction models indicate that as the 6G-SQC matures, project success probability significantly increases, and costs are notably reduced. Furthermore, the best fitness rankings for each 6G-SQC project variable determined using NBC and LR indicated a strong positive correlation (rs = 0.895). The t-test results (t = 0.752, p = 0.502 > 0.05) show no significant differences between the rankings calculated using both prediction models (NBC and LR). The results reveal that the developed success probability prediction model, based on 15 identified 6G-SQC project variables, highlights the areas where practitioners need to focus more to facilitate the cost-effective and successful implementation of 6G-SQC projects.

Abstract Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them ( t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.

Summary End‐user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end‐user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high‐star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end‐users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end‐user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end‐users and professionals use to report fake reviews by critically analyzing the end‐user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd‐users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end‐users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end‐user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f‐measures. The accuracy of manually identifying fake reviews by the crowd‐users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.

Abstract Context Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &A forums. Objective We conducted an empirical study to analyze developers’ discussions on Q &A forums to gain insights into software development approaches in practice. Method We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings We identified 15 frequently mentioned software development approaches topics on Q &A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.

The emergence of 6 G technology heralds a groundbreaking era in digital connectivity, envisaging universal and seamless links. To address the intricate computational and security requirements of this revolution, the integration of quantum computing (QC) into these networks is perceived as a promising solution. The objective this study presents a comprehensive investigation into the potential roles and implications of QC within the context of 6 G technology. To address the objectives of this study, firstly, we have conducted literature survey to identify the key applications of using QC in 6 G technology. Secondly, we performed interview study with industry experts to identify the best practices related to the key application of QC in 6 G technology. Our study unfolds in two distinct stages: firstly, we identify 15 key applications of QC in 6 G technology and segmented into 4 core areas. Secondly, the literature findings were empirically validated by conducting interview study and identified 49 best practices related to one of the identified key applications of QC in 6 G technology. The outcomes of this research lay a solid foundation for understanding both the pivotal applications of QC in 6 G technology and the effective practices for its implementation, thus providing valuable insights to both academics and industry practitioners.

Quantum computing (QC) holds the potential to revolutionize computing by solving complex problems exponentially faster than classical computers, transforming fields such as cryptography, optimization, and scientific simulations. To unlock the potential benefits of QC, quantum software development (QSD) enables harnessing its power, further driving innovation across diverse domains. To ensure successful QSD projects, it is crucial to concentrate on key variables. This study aims to identify key variables in QSD and develop a model for predicting the success probability of QSD projects. We identified key QSD variables from existing literature to achieve these objectives and collected expert insights using a survey instrument. We then analyzed these variables using an optimization model, i.e., Genetic Algorithm (GA), with two different prediction methods the Naïve Bayes Classifier (NBC) and Logistic Regression (LR). The results of success probability prediction models indicate that as the QSD process matures, project success probability significantly increases, and costs are notably reduced. Furthermore, the best fitness rankings for each QSD project variable determined using NBC and LR indicated a strong positive correlation (rs=0.945). The t-test results (t = 0.851, p = 0.402>0.05) show no significant differences between the rankings calculated by the two methods (NBC and LR). The results reveal that the developed success probability prediction model, based on 14 identified QSD project variables, highlights the areas where practitioners need to focus more in order to facilitate the cost-effective and successful implementation of QSD projects.

Abstract Software industry is continuously exploring better ways to develop applications. A new phenomenon to achieve this is cloud‐based global software development (CGSD), which refers to the adoption of cloud computing services by organizations to support global software development projects. The CGSD approach affects the strategic and operational aspects of the way projects are managed. The objective of the study is to identify the success factors which contribute to management of CGSD projects. We carried out a multivocal literature review (MLR) to identify the success factors from the state‐of‐the‐art and the state‐of‐the‐practice in project management of CGSD projects. We identified 32 success factors that contribute to the management of CGSD projects. The findings of MLR indicate that time to market, continuous development, financial restructuring, and scalability are the most critical success factors for CGSD. Moreover, the findings of the study show that there is a positive correlation between the success factors reported in both formal literature and industry based gray literature. The findings of this study can assist the practitioners to develop the strategies needed for effective project management of CGSD projects.

Abstract Effective management of development projects is crucial to delivering high‐quality software within time and budget constraints. However, organizing geographically distributed software development activities presents unique challenges, including difficulties in face‐to‐face interaction and coordination. To assist the global software development community in updating and developing new project management techniques, identifying and prioritizing best practices is essential. This study aims to develop a taxonomy based on the prioritization of management practices for software development, drawing from empirical data. Fifty‐five best practices associated with the Project Management Body of Knowledge (PMBOK) fields were identified from the existing literature. The study also empirically examines the acceptability and relevance of these practices within the industry. An analytic hierarchy process within a fuzzy system is employed to prioritize these practices based on their importance in managing global software development.

Abstract Technological advancement makes the world a global village. Security is an evergreen and everlasting area, because of the continuous threat from Hackers and Crackers. The immense use of software systems has modernized human society in every aspect. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing the costs and shortening the time to market. Considering the significance of software security, it is important to consider the security practices from the early phase of the software development life cycle (SDLC), that is, requirements engineering (RE). Hence, this study aims to identify and categorize RE practices important to apply for secure software development (SSD) in a geographically distributed development environment. To study the RE practices concerning SSD, we conducted a questionnaire survey with industrial experts in the global software development (GSD) context. Furthermore, the interpretive structure modeling (ISM) approach was applied to evaluate the relationship between the RE security practice core categories. This paper identifies 70 practices and classifies them into 11 fundamental dimensions (categories) to assist GSD organizations in specifying the requirements for SSD. The ISM results show that the “Awareness of Secure Requirement Engineering (SRE)” category has the most decisive influence on the other 10 core categories of the identified RE security practices. With the help of empirical evidence and the ISM approach, this work attempts to identify potential security practices and to give a set of secure RE practices that can be used to improve the security of the software development process.

Abstract Software security has become increasingly important because the malicious attack and other hacker risks of a computer system have grown popularity in the last few years. As a result, several researchers have examined security solutions as early as the requirement engineering phase. With the growth of the software business and the internet, there is a need to understand the security risks against each phase of the software development life cycle (SDLC). This study aims to empirically investigate and prioritize the risks that could negatively impact the software security aspects of SDLC in the context of global software development (GSD). To achieve the study objectives, we conducted an industrial empirical study to determine the impact of software security threats against each phase of SDLC. Furthermore, the fuzzy analytical hierarchy process (FAHP) was used to prioritize the list of software security risks against the SDLC. The results and analysis of this study provide a ranked‐based decision‐making framework, which assists the practitioners in considering the most critical security risks on priority. The results show “improper plan for secure requirement identification, inception, authentication, authorization, and privacy,” “lack of threat models updating,” “lack of output validation,” “lack of certification in the final release and archive,” and “spoofing” as the top‐ranked security risks of SDLC in GSD. In addition, the application of FAHP is novel in this domain as it is helpful to address multicriteria decision‐making problems.

Abstract Software development projects depend on collaborative teams. In the past 50 years of research, various studies have explored the effect of software engineer's personality traits and cultural values on team performance. These studies have led to better understand these relationships; however, how the personality traits and cultural values influence the team effectiveness is still far away in the literature. This research aims to investigate the relationships between social and psychological complexities (including personality traits and cultural values), team coordination, team motivation, and team success (which comprises team effectiveness and team climate) to explore the impact of social and psychological issues on software team success. An online survey targeting software development professionals and unstructured interviews were followed for data collection. We received 112 responses from software developers working in different countries. Findings indicate that personality traits and cultural values, that is, consciousness, openness, harmony, and autonomy, have positive relationship with team coordination effectiveness, while other factors such as neuroticism, embeddedness, hierarchy, and mastery were found to be related negatively with it. These negative relationships can be mitigated by motivating team members appropriately. Based on our research findings, we conclude that the negative impact caused by different personality and cultural traits could be reduced by improving team coordination effectiveness using effective motivation.

Abstract Development and operations (DevOps) refer to the collaboration and multidisciplinary organizational effort to automate continuous delivery of information systems (IS) development project with an aim to improve the quality of the IS. The flexibility and quality production of software projects motivated the organizations to adopt DevOps paradigm. Organizations face several complexities while management of DevOps process. This study aims to explore and analyze the factors that could positively impact the management of DevOps process. Firstly, literature review was performed and identified 36 success factors that are related to 10 knowledge areas of DevOps project management. Secondly, a questionnaire survey study was conducted to get the insight of industry experts concerning the success factors of DevOps project. Finally, the fuzzy‐AHP was applied for ranking the success factors and examining the relationship between 10 knowledge areas of identified success factors. The results of this study will serve as a body of knowledge for researchers and practitioners to consider the highest priority success factors and develop the effective policies for the successful execution of DevOps project management.

Abstract The significance of artificial intelligence (AI) trustworthiness lies in its potential impacts on society. AI revolutionizes various industries and improves social life, but it also brings ethical harm. However, the challenging factors of AI trustworthiness are still being debated. This research explores the challenging factors and their priorities to be considered in the software process improvement (SPI) manifesto for developing a trustworthy AI system. The multivocal literature review (MLR) and questionnaire‐based survey approaches are used to identify the challenging factors from state‐of‐the‐art literature and industry. Prioritization based taxonomy of the challenges is developed, which reveals that lack of responsible and accountable ethical AI leaders, lack of ethics audits, moral deskilling & debility, lack of inclusivity in AI multistakeholder governance, and lack of scale training programs to sensitize the workforce on ethical issues are the top‐ranked challenging factors to be considered in SPI manifesto. This study's findings suggest revising AI‐based development techniques and strategies, particularly focusing on trustworthiness. In addition, the results of this study encourage further research to support the development and quality assessment of ethics‐aware AI systems.

Ethics are essential in DevOps for promoting transparency, trust, security, quality, continuous improvement, and user-centricity, as they guide individuals and organizations to make responsible choices and develop high-quality, secure, and reliable software applications. To consider the ethics of the DevOps teams, there is a need to develop standards and a code of conduct. The objective of this research work is to develop robust guidelines to assist software development organizations to consider ethics in the DevOps teams. The empirical study will be performed with industry practitioners, and legislation experts to explore the important areas and practices that need to be followed by the software organizations' work ethics for the DevOps teams. We believe the developed robust guidelines will assist the organization to follow the work ethics while working in a DevOps culture.

Abstract Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.

CT vertebral segmentation plays an essential role in various clinical applications, such as computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch‐based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)‐module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high‐level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high‐level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI‐Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F‐score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model’s performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.

Artificial intelligence (AI) solutions and technologies are being increasingly adopted in smart systems contexts; however, such technologies are concerned with ethical uncertainties. Various guidelines, principles, and regulatory frameworks are designed to ensure that AI technologies adhere to ethical well-being. However, the implications of AI ethics principles and guidelines are still being debated. To further explore the significance of AI ethics principles and relevant challenges, we conducted a survey of 99 randomly selected representative AI practitioners and lawmakers (e.g., AI engineers and lawyers) from 20 countries across five continents. To the best of our knowledge, this is the first empirical study that unveils the perceptions of two different types of population (AI practitioners and lawmakers) and the study findings confirm that transparency, accountability, and privacy are the most critical AI ethics principles. On the other hand, lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are found to be the most common AI ethics challenges. The impact analysis of the challenges across principles reveals that conflict in practice is a highly severe challenge. Moreover, the perceptions of practitioners and lawmakers are statistically correlated with significant differences for particular principles (e.g. fairness and freedom) and challenges (e.g. lacking monitoring bodies and machine distortion). Our findings stimulate further research, particularly empowering existing capability maturity models to support ethics-aware AI systems’ development and quality assessment.

Recently, blockchain and DevOps have received attention from the software industry as both offer number of benefits individually. Automation, measurement, and sharing are the core pillars of DevOps, and to securely manage these; blockchain can play an important role. This study aims to understand the role of blockchain technology in the DevOps paradigm. This study presents factors that influence the adoption of blockchain in DevOps. Furthermore, this study suggests a framework that assists in merging the characteristics of blockchain in the DevOps paradigm. The findings of this study give a knowledge base for industry experts and the research community to develop the roadmap and guidelines for the adoption of blockchain in the DevOps paradigm.

The outbreak of the covid-19 pandemic has devastated many sectors of each country and led to the development of contact tracing applications for controlling its spread. Contact tracing apps have been promoted to track infected contacts. However, contact tracing has gained significant debate due to its security and privacy concerns. The goal of this study is to examine the most popular contact tracing apps, their impact on pandemic control, as well security and privacy concerns. The multivocal literature review (MLR) brings the results from the state-of-the-art literature. We extracted 23 studies from both formal and grey literature to achieve the research objectives and found several security and privacy threats in the existing contact tracing applications. Additionally, the best practices to address these threats were also identified. We further proposed a preliminary structure of a secure global contact tracing app using blockchain technology

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.

DevOps is a culture-oriented software development and operations methodology, and software organizations increasingly adopting it due to its flexibility and good business gains. Especially, in Covid situations DevOps give a good support to software development organizations to carry development and operations activities through cloud-native DevOps in work from home environment and starting new businesses. The technological advancement makes the world a global village, though, there is dire need of guidelines and standards for the adoption of DevOps across the borders and out of office. Considering the significance of DevOps in current era of software business, we plan to develop a roadmap aiming to assist the software development organizations to adopt DevOps process over the globe and in work-from-home format. In this study, we are proposing an initial idea towards the development of robust and comprehensive guide for DevOps adoption in geographically distributed environment.

Background: DevOps is currently one of the main trends in software development. Low-Code is also an emerging tendency that, combined with DevOps, may offer significant value to software businesses by improving the process. However, how DevOps practices and low-code are combined is little known. Aim: This study aims to understand the practitioner's perspectives on low-code trends. Method: Twelve interviews with IT professionals who deal with low-code in the context of DevOps were conducted. Then, a grounded theory approach was used to theme the interview quotes into emergent categories. Results: The main result of this exploratory study reveals that such an approach is the most common response to the skill shortages of software professionals. Conclusion: This study suggests the emergence of DevOps and low-code could significantly contribute to the development of quality products with low-cost and time.

Development and Operations (DevOps) is a methodology that aims to establish collaboration between programmers and operators to automate the continuous delivery of new software to reduce the development life cycle and produce quality software. Development, Security, and Operations (DevSecOps) is developing the DevOps concept, which integrates security methods into a DevOps process. DevSecOps is a software development process where security is built in to ensure application confidentiality, integrity, and availability. This paper aims to identify and prioritize the challenges associated with implementing the DevSecOps process. We performed a multivocal literature review (MLR) and conducted a questionnaire-based survey to identify challenges associated with DevSecOps-based projects. Moreover, interpretive structure modeling (ISM) was applied to study the relationships among the core categories of the challenges. Finally, we used the fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) to prioritize the identified challenges associated with DevSecOps projects. We identified 18 challenges for the DevSecOps process and mapped them to 10 core categories. The ISM results indicate that the “standards” category has the most decisive influence on the other nine core categories of the identified challenges. Moreover, the fuzzy TOPSIS indicates that “lack of secure coding standards,” “lack of automated testing tools for security in DevOps,” and “ignorance in static testing for security due to lack of knowledge” are the highest priority challenges for the DevSecOps paradigm. Organizations using DevOps should consider the identified challenges in developing secure software.

Abstract Healthcare systems face various issues related to complex networks of intermediaries and a lack of transaction traceability. The most critical issues are the fragmentation of healthcare data, obstacles in providing efficient research and services, lack of clinical trial reporting, high cost and mismanagement of the drug supply chain, patient data security, and fake drugs. Blockchain technology has the potential to address these criticalities as it has in build traceability mechanisms and promises new business models by enabling incentive structures. This potential of blockchain gathers a high interest in the health industry. However, the implementation of blockchain in healthcare faces various issues as well. Currently, there are no practice‐oriented maturity models to improve such an implementation. In this paper, we present a roadmap to develop a maturity model for blockchain in healthcare (MMBH) based on critical barriers (CBs), critical success factors (CSFs), and the best practices for blockchain implementation in healthcare systems. As a first step to develop the MMBH, in this paper, we present the initial results of a systematic literature review (SLR) to identify critical success factors for implementing blockchain in healthcare systems. We also applied fuzzy technique order preference by similarity to ideal solution (TOPSIS) to prioritize the identified CSFs.

Abstract Testing is a complex phase in DevOps process due to need of an automated process that provides feedback at different strategies of continuous development and operations pipeline. Software organization face several challenges during the testing phase due to lack of understanding on testing best practices for the DevOps paradigm. The objective of this study is to prioritize DevOps best testing practices, which can facilitate the selection of testing practices during DevOps process. To perform this research, we have extended the work done by Hornbeek, using the 15 DevOps testing practices discussed in his study. First, we categorize the test practices against culture, automation, lean, measurement, and sharing (CALMS) pillars of DevOps adoption principles. Next, a questionnaire‐based survey was conducted to collect feedback from industry practitioners on the DevOps test practices and their categorization against CALMS criteria. Finally, we applied Interpretive Structure Modeling (ISM) to find the interrelationship between CALMS criteria, and fuzzy TOPSIS was used to prioritize the DevOps test practices that will assist practitioners to better manage the testing activities during DevOps process.

Abstract Software industry is adopting global software development (GSD) due to its potential to produce quality products at a lower cost. However, the GSD firms face many challenges that make development activities more complicated, especially related to the requirements engineering (RE) process. The objectives of this article are to investigate and prioritize the barriers faced by the GSD organizations during the RE process. First, we identified 17 barriers related to the RE process in the GSD projects. Next, the identified barriers were further validated with real‐world GSD practitioners using a questionnaire survey. Finally, we applied the analytical hierarchy process to prioritize the investigated barriers with respect to their significance for the RE process in the GSD domain. The results show that coordination is the most significant barrier category for the RE process in GSD projects. Lack of standard and procedure for RE in GSD, lack of synchronized communication infrastructure, and lack of mutual understanding between the overseas RE teams are also high‐ranked barriers for the RE process in GSD. The authors believe that the findings of this study will assist practitioners and researchers in developing effective strategies and plans for the successful implementation of the RE process in the GSD context.

Abstract Managing the cloud‐based software development outsourcing (CSDO) activities across the geographically distributed development sites are much challenging. This study aims to identify the success factors (SFs) for CSDO and prioritize them based on their significance. To achieve this objective, we conducted a systematic literature review (SLR) and survey study with industrial and academic experts. Finally, we applied the analytical hierarchy process (AHP) to develop the framework based on the prioritization of the identified SFs. We believe that the findings of this study will assist the industry practitioners and researchers in developing effective strategies for the successful implementation of CSDO activities.

Abstract Planning and managing of requirement change management (RCM) process in global software development (GSD) are a complicated task, but the RCM plays a significant role in developing the quality software within time and budget. The key aim of this study is to prioritize the factors that could positively influence the RCM program in GSD context. To achieve the study objective, the questionnaire survey study was conducted to get the feedback of the practitioners concerning the success factors of RCM in GSD context. Moreover, the fuzzy analytical hierarchy process (FAHP) was applied. The application of FAHP is novel in this research domain as it has been effectively applied previously in various other research areas, for example, supplier selection, electronics and electrical, personnel selection, and agile software development. The results of this study will provide the prioritization‐based taxonomy of RCM success factors and also contribute by introducing the novel FAHP approach in the research domain of RCM in GSD. The FAHP approach assists the practitioners to reduce the uncertainty and vague opinions of RCM experts.

Abstract DevOps is a new software engineering paradigm adopted by various software organizations to develop the quality software within time and budget. The implementation of DevOps practices is critical, and there are no guidelines to assess and improve the DevOps activities in software organizations. Hence, there is a need to develop a readiness model for DevOps (RMDevOps) with an aim to assist the practitioners for implementation of DevOps practices in software firms. To achieve the study objective, we conducted a systematic literature review (SLR) study to identify the critical challenges and associated best practices of DevOps. A total of 18 challenges and 73 best practices were identified from the 69 primary studies. The identified challenges and best practices were further evaluated by conducting a survey with industry practitioners. The RMDevOps was developed based on other well‐established models in software engineering domain, for example, software process improvement readiness model (SPIRM) and software outsourcing vendor readiness model (SOVRM). Finally, case studies were conducted with three different organizations with an aim to validate the developed model. The results show that the RMDevOps is effective to assess and improve the DevOps practices in software organizations.

Majority of the software development organizations are motivated to transform their development activities from collocated development to offshore software development outsourcing (OSDO) environment. The adoption of OSDO is complicated due to geographical distance between development teams. The requirements engineering (RE) in the context of OSDO needs special consideration as it needs more communication and collaboration. The objective of this study is to investigate the RE barriers in the domain of OSDO. This study have two main objectives (i) identification of barriers for RE process in OSDO from the existing literature and (ii) prioritizing the identified barriers by using analytical hierarchy approach (AHP). However, in this study a total of 14 barriers of RE process were identified. The investigated barriers were further categorized into three core categories i.e. coordination, project administration and human resources management.The results demonstrated that Lack of 3Cs (communication coordination and control), Strict time schedule by customer, and Incompatibility with client are the barriers having most priority in RE process in OSDO context. Moreover, we noted that the coordination is the most significant category of investigated barriers. We are confident that the findings of this study provide a model which is significant to assess and improve the RE activities in the domain of OSDO.

The economic and strategic gain attracts the software industry to adopt the global software development (GSD) phenomenon. Besides the significant advantages, the GSD firms also face certain critical challenges mainly related to security and management perspective. Blockchain is the new revolutionary technology that offers over the globe secure storage via distributed databases. The objective of this study is to investigate how the adoption of blockchain is beneficial for the GSD project. We have conducted interviews with academic researchers and case studies with industry practitioners to identify the impact of blockchain on GSD projects. We have identified ten key critical areas of GSD the effectively addressed by using the blockchain technology in GSD context. A comparative analysis is conducted with the finding of academic researcher and industry practitioners, and the results ((W=0.86, p=0.005) shows that there is a strong agreement between the understanding of academic researchers and industry practitioners. Besides, we have developed a hypothetical model based on the findings of both studies that shows the positive relationship of blockchain implementation in the GSD domain. We believe that the findings of this study will encourage the practitioners and researchers to develop the new plan and strategies for the adoption of blockchain in GSD.

In the current era, the auto and reliable recommendation system plays a significant role in human life. The code recommender systems are being used in various source code databases to recommend the most suitable source code to the user. While code recommendation, the code analysis concerning 'code quality' and 'code implementation' is important to recommend the most reliable code by considering the objective of the user. The ultimate aim of this research work is to propose a code recommendation and implementation model using the characteristics of DevOps that assist in extracting, analyzing, implementing, and updating the recommender system continuously. The current study presents an initial framework of the proposed code recommender model. The design of the model is based on the data collected through literature review and by conducting an empirical study with experts. We believe that the proposed model will assist the researchers and practitioners to recommend the most secure and suitable source code according to their requirement.

It is very crucial for an organization to encapsulate the requirements in its early stage when they are intending to build a novel system such as the internet of things (IoT), particularly when it comes to capturing privacy and security requirements to gain the public confidence. The proposed research is focused to develop a secure IoT-requirement implementation maturity model (SIOT-RIMM). The proposed model will assist the software development organizations to improve and modify their requirement engineering processes in terms of security and privacy of IoT. The SIOT-RIMM model will be developed based on the existing IoT literature pertaining to security and privacy, industrial empirical study and understanding of the challenges that could negatively influence the implementation of security and privacy in IoT. To develop the maturity levels of SIOT-RIMM, we will consider the concepts of existing maturity models of other software engineering domains. In this preliminary study, 19 challenges were identified using the SLR approach that might have a negative impact on the IoT requirements engineering process. The identified challenges will contribute to the development of SIOT-RIMM maturity levels.

DevOps is a new software engineering paradigm adopted by various software organizations to develop an environment of continuous deployment and delivery within time. Numerous experts are offering their services to help organizations, how to implement DevOps activities in software organization. Though, still there are various issues for software organizations to adopt DevOps activities. To overcome such issues, there must be an approach that could assist software organizations towards better adoption of DevOps activities. The core objective of this research is to design a Readiness Model for DevOps (RMDevOps) to improve the adoption of DevOps activities in a software organization. Based on existing models in other fields of software engineering, we will develop this model. We have conducted a systematic literature review and empirical study on DevOps, for understanding the impact of the success factors of DevOps in the real world and literature. This study covers the first step of development of RMDevOps model, by identifying the success factors of DevOps and presenting the outcomes in the form of robust framework.

Security in DevOps is a challenging feature because traditional existing methods of security are not fulfilling the exact requirements of DevOps. To make DevOps activities successful for organizations this study will help to identify concerns about DevOps security in real world by interviewing the practitioners having DevOps working experience. The Classical grounded theory approach has been used to build our theory. We interviewed 13 practitioners working across five companies from different regions. We contributed to identify security concerns in DevOps and try to present a theoretical model to improve understanding and guidance of DevOps adoption. The security concerns were marked as functional and nonfunctional based upon the interviews concepts to help practitioners having understanding about particular concerns. Therefore, this theory will highlight security concerns that are hindering the adoption of DevOps successfully.

Vehicle to everything (V2X) is the new and revolutional phenomena of the recent era. In V2X environment, the data security of data and information is the most significant parameter, as the information is shared on the shared network. To secure the information in V2X environment; the blockchain technology can play a critical role as it consists of the powerful data encryption and decryption algorithms. To implement the characteristics of blockchain in V2X environment, the practitioners face various challenges. The key aim of this study is to develop a hypothetical framework that renders the impact of challenging factors on the implementation of blockchain in V2X paradigm. We have conducted systematic literature review approach and identified the 10 challenges that are critical to address for the successful implementation of blockchain technology in V2X network. We believe that the findings of this study serves as knowledge base for the researchers and practitioners to develop the effective strategies for the successful implementation of blockchain characteristics in V2X environment.

Requirements change management (RCM) is an important and complicated phase in the agile for the software development process. The objective of this study was to identify and analyse the factors that can positively affect the agile RCM (ARCM) process in the context of global software development (GSD). To this end, a questionnaire survey was carried out with researcher and practitioner participants working in the field of GSD. From the 56 survey respondents, a total of 20 factors that can positively influence the ARCM activities in GSD were identified. The authors also performed a client–vendor‐based analysis to investigate the significance of the considered factors for different organisation types. Additionally, they compared the data collected from researchers and practitioners. The comparison results ( r s (20) = 0.076) revealed that there exists a positive correlation between the rankings in each data set. The investigated factors were mapped to ten project management book of knowledge areas, and the mapping results revealed that human resource management is the most significant knowledge area amongst the investigated factors. The findings of this study provide a robust framework to assist GSD firms in implementing ARCM activities.

Offshore software development outsourcing (OSDO) is one of the popular development paradigms in the software industry. There are a number of challenges associated with OSDO including the challenges related to requirements engineering (RE) process. The objective of this study is to identify success factors (SFs) associated with RE processes in the OSDO context. A total of 25 SFs were identified using systematic literature review (SLR) and the identified SFs were further validated with practitioners using a questionnaire-based survey. The authors also examined the identified RE challenges with respect to their relevance for different organisation types (client and vendor) and sizes (small, medium, and large) with an aim to provide a clear understanding of the RE process and its SFs in the context of various OSDO organisations. They also conducted a comparative analysis between the SLR and questionnaire findings and the results indicate that there is a moderately positive correlation between the data sets. They believe that the findings of this study will provide a comprehensive model to help OSDO organisations assess and improve the effectiveness and efficiency of their RE activities.

Global software development (GSD) organisations are currently adopting agile frameworks in order to efficiently develop a software product. The main objective of this study is to identify the success factors (SFs), which could possibly have a positive impact on scaling agile practices in a GSD environment and develop their taxonomy based on their prioritisation using the analytic hierarchy process (AHP) approach. This study is conducted in four stages: problem identification and goal of the study (1), identification of SFs and their categorisations (2), validation of the SFs using questionnaire survey (3), and application of AHP to prioritise the SFs and develop the taxonomy of the SFs and their respective categories (4). The results of this study indicated that ‘technology’ is the most significant category as compared to the other categories of the SFs. Similarly, rich technological infrastructure is identified as a most important factor. Based on the findings of this study, authors can conclude that the contribution of this study is not only limited to development of the taxonomy of the SFs, but also their proper prioritisation by introducing AHP approach, which assists software organisations to scale agile methods effectively in the GSD environment.

Abstract Majority of software development firms are adopting the concepts of global software development (GSD) in order to develop high‐quality and low‐cost products. However, the requirements change management (RCM) becomes a significant challenge in the GSD environment because of the unavailability of proper RCM framework and taxonomy. The objective of this study is to develop a taxonomy of the challenging factors of the RCM process in GSD. The taxonomy is developed based on the results of the data collected during the survey study and the implementation of the analytical hierarchy process (AHP). Total 25 challenging factors are identified and mapped into four core categories, ie, “organizational management,” “team,” “technology,” and “process.” Moreover, the AHP analysis is performed to prioritize the challenging factors and their categories. The prioritization process highlight that “process” is the most significant category of the RCM challenging factors. The taxonomy developed in this study provides a robust framework to tackle problems associated with RCM activities in GSD environment, which is significant to the success and progression of GSD firms.

Abstract Software development is a complex task, and the introduction of multi‐site development teams spread across the globe makes it even harder. Requirement changes during the software development process are inevitable, and failing to manage evolving requirements is one of the contributors to project failures. Requirements change management (RCM) becomes difficult in global software development (GSD) projects due to the need to communicate and coordinate between stakeholders in a distributed environment. This research work aims to identify the factors that have positive impacts on RCM activities in GSD. We conducted a multi‐vocal literature review (MLR) and a questionnaire survey study to identify the RCM success factors. The results of the t‐test (ie, t = 0.0347 and P = .700 > .05) and correlation (rs (25) = 0.573, P = .003) indicates that there is no significant difference between the findings of MLR and the questionnaire survey. The identified factors were further analyzed in the context of client and vendor organizations to better understand the RCM success factors in both types of GSD firms. The findings of this study can provide a useful framework for tackling problems associated with RCM in a GSD environment that is significant to the progression of GSD firms.

Abstract Requirements Change Management (RCM) is one of the challenges faced by Global Software Development (GSD) organisations as requirements evolution is inevitable due to dynamic business and operating environments. GSD organisations face issues when dealing with RCM because many organisations embark on GSD without understanding their readiness to undertake such an initiative. Currently, there is no readiness model to assess the RCM process in the context of GSD. The objective of this study is to develop a requirements change management readiness model (RCMRM) for GSD organisations. A Systematic Mapping Study (SMS) was conducted to identify the primary studies related to RCM in the GSD projects. By using SMS, 109 primary studies were selected and 73 RCM practices were identified which were used to design the readiness levels of the proposed RCMRM. To validate the RCMRM, initially, two case studies were conducted in two GSD organisations. Based on the suggestions and recommendations of the case study participants,the RCMRM was further modified. The updated version of RCMRM was further validated by two different GSD organisations. The results of the second case study indicate that RCMRM is effective in assessing the readiness of the RCM process in the context of GSD.

Abstract Most software development firms have adopted the concepts of global software development (GSD) to develop high‐quality and low‐cost products. However, GSD is not a straightforward process. It is associated with many challenges that are mostly related to requirements change management (RCM). In this study, we explore the motivators that contribute to managing RCM activities in the GSD environment. We extracted a total of 25 motivators using the systematic literature review (SLR) approach and conducted a survey study to empirically evaluate the findings of the SLR. The results of the applied Spearman's statistical test show that the findings of the SLR and survey study had a positive moderate correlation (rs(25) = 0.566). We further analyzed the reported motivators according to organization size. Finally, we developed taxonomies of the identified motivators based on the framework proposed by Ramasubbu and the project management body of knowledge. We believe that the taxonomies provide a robust framework for tackling the challenges of RCM, which is significant for the success and evolution of GSD firms.

Abstract The phenomenon of global software development (GSD) has been adopted by a majority of the software development firms to achieve the significant benefits it offers. However, there are many challenges faced by the GSD organizations, which are mainly related to requirements change management (RCM). The key objective of this study is to identify the challenges of RCM process in GSD domain. The systematic literature review (SLR) approach has been used to investigate the challenges of RCM activities, and a total of 30 challenges were identified. We have further classified the identified challenges in the domain of client and vendor GSD organizations, aiming to provide a clear understanding of the RCM process and its challenges in the context of both types of GSD organizations. The identified challenges were also categorized into three core types according to the organization size (small, medium sized, or large), which highlights the significance of each challenge for a specific organizational size. In addition, the criticality of the identified challenges was assessed using the criteria of challenges having a frequency greater than or equal to 50%. According to the findings of this study, a framework is provided that could help GSD organizations address the problems related to RCM in a GSD environment.