Context: Digital twins (DTs) have become integral in diverse cyber–physical production systems (CPPS), enabling dynamic interactions between physical entities and their digital counterparts. Yet their integration into such complex ecosystems raises substantial interoperability challenges. While these challenges and associated frameworks for DTs have been extensively theorized in scholarly literature, there are limited empirical investigations that capture industrial perspectives on these aspects. Objective: This exploratory study aims to empirically investigate real-world interoperability challenges in DT deployments and assess the relevance of a layered interoperability framework as a structured approach to address these issues. Methods: We addressed this gap by conducting interviews with 12 DT practitioners from 10 companies across five European countries. Interviewees are guided through two reference models: a simplified view of the DT ecosystem and a layered framework based on the Level of Conceptual Interoperability Model (LCIM). The thematic synthesis and systematic mapping of the collected data have used Grounded Theory (GT)-based open coding. Sentiment analysis was used as an illustrative complement to the qualitative findings by capturing expert attitudes towards the LCIM for DTs. Results: The analysis identified 26 practical interoperability challenges, thematically synthesized into 7 categories. Experts’ perspectives on the LCIM for DTs revealed two key outcomes: 4 drivers of the open and closed-ended nature of interoperability layers, and 4 value propositions highlighting the framework’s relevance for DT deployments. Further, the identified challenge categories are mapped across layers, highlighting the dichotomy of open-source and proprietary approaches, the need for Dynamism and Ecosystem-oriented Interoperability. Conclusions: This work advances empirical and theoretical understandings of DT interoperability within CPPS. Our findings contribute to addressing practical interoperability challenges, provide empirical values for the layered model in cross-disciplinary approaches to DT integration, and offer guidance for researchers and practitioners. Future work could validate and adapt the layered approach through domain-specific DT applications to assess its effectiveness in digital transformation initiatives.

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.

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.

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User’s requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this article proposes a Web 3.0-enabled M icroservice R e- S cheduling approach (called MRS ), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the MRS task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4%, and 2.2% in terms of network throughput, latency, and energy consumption, respectively.

The explosive growth of the Application Programming Interfaces (APIs) economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation graph-based recommendation approaches have been designed to assist developers in discovering related and compatible API compositions for mashup creation. Unfortunately, these approaches often suffer from popularity bias issues, leading to an inequality in API usage and potential disruptions to the entire API ecosystem. To address these challenges, our research begins with a theoretical analysis of the popularity bias introduced by correlation graph-based API recommendation approaches. Subsequently, we empirically validate the presence of popularity bias in API recommendations through a data-driven study. Finally, we introduce the p opularity b ias aware w eb A PI r ecommendation ( PB-WAR ) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real-world dataset demonstrate that PB-WAR offers the optimal tradeoff between accuracy and debiasing performance compared to other competitive methods.

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.

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 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 Requirements elicitation is a process that involves gathering requirements for a given project. Several studies have been published suggesting strategies to improve the requirements gathering process. Using game‐based and crowd‐based approaches, researchers are extracting requirements that are useful for product development today. This study follows the same line of research. This research study aims to improve the understanding of the requirements gathering process by novices or students through different activities: (I) knowledge of requirements gathering method and (II) techniques or activities viable for software requirements (education). Important methods used to address the above objectives are as follows: (I) a comprehensive review of the literature to understand requirements gathering; (II) designing an activity to embed RE challenges and RE sub‐activities; and (III) experiment, survey, and observation to collect data and to assess the proposed methods' effectiveness. The suggested activity for requirement gathering is based on the game tic‐tac‐toe. The participants suggest that the design of the activity is helpful in brainstorming and is also valuable for identifying requirements; moreover, a post questionnaire has been designed to determine the learning of the participants regarding the proposed activity. We can observe simply from the coefficients that both skills and challenges (as perceived by the participants) have positive impacts on engagement, immersion, and perceived learning. The proposed activity helps novices or students gain (basic) knowledge of the requirements gathering process/technique; the outlined activity can be a way of learning requirements and gathering knowledge (basic). From this study, we conclude that the proposed activity has positive results and is helpful for participants to get a better understanding of the requirements engineering method(s).

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.

Abstract Currently, the accurate prediction of social relationships can effectively reduce the decision‐making burden of users in various service platforms. However, in the big data environment, the users' data information used for the relationship prediction is highly fragmented distribution, so it is a non‐trivial challenge to integrate the users' sequence data information from different platforms while preventing sensitive information leakage. To this end, based on the microservice environment, we devise a cross‐platform social relationship prediction approach (CPSRP) to address the above problems. Briefly, the improved Simhash method aggregates similar users into the common bucket. Then the embedding technique converts the users' sparse data information into the low‐dimensional dense continuous feature vectors; the redefined Gated Recurrent Unit (r‐GRU) network and the Multilayer Perceptron (MLP) network are employed to extract the overall temporal sequence features of users. The relationship prediction is finally executed according to the users' sequential features. Extensive experiments are conducted on Epinions, and the experimental results further prove the benefits of our proposal in terms of relationship prediction while protecting users' sensitive information.

Abstract The value of data to a company means that it must be protected. When it comes to safeguarding their local and worldwide databases, businesses face a number of challenges. To systematically review the literature to highlight the difficulties in establishing, implementing, and maintaining secure databases. In order to better understand database system problems, we did a systematic mapping study (SMS). We’ve analyzed 100 research publications from different digital libraries and found 20 issues after adopting inclusion and exclusion criteria. This SMS study aimed to identify the most up-to-date research in database security and the different challenges faced by users/clients using various databases from a software engineering perspective. In total, 20 challenges were identified related to database security. Our results show that “weak authorization system”, “weak access control”, “privacy issues/data leakage”, “lack of NOP security”, and “database attacks” as the most frequently cited critical challenges. Further analyses were performed to show different challenges with respect to different phases of the software development lifecycle, venue of publications, types of database attacks, and active research institutes/universities researching database security. The organizations should implement adequate mitigation strategies to address the identified database challenges. This research will also provide a direction for new research in this area.

Society’s increasing dependence on Artificial Intelligence (AI) and AI-enabled systems require a more practical approach from software engineering (SE) executives in middle and higher-level management to improve their involvement in implementing AI ethics by making ethical requirements part of their management practices. However, research indicates that most work on implementing ethical requirements in SE management primarily focuses on technical development, with scarce findings for middle and higher-level management. We investigate this by interviewing ten Finnish SE executives in middle and higher-level management to examine how they consider and implement ethical requirements. We use ethical requirements from the European Union (EU) Trustworthy Ethics guidelines for Trustworthy AI as our reference for ethical requirements and an Agile portfolio management framework to analyze implementation. Our findings reveal a general consideration of privacy and data governance ethical requirements as legal requirements with no other consideration for ethical requirements identified. The findings also show practicable consideration of ethical requirements as technical robustness and safety for implementation as risk requirements and societal and environmental well-being for implementation as sustainability requirements. We examine a practical approach to implementing ethical requirements using the ethical risk requirements stack employing the Agile portfolio management framework.

Non peer reviewed

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.

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modelling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modelling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customise the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.

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.

Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose anInteraction-enhanced andTime-awareGraphConvolutionNetwork (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods.

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.

Abstract To improve and sustain the quality of software products, software process improvement (SPI) is needed. Currently, small and medium software enterprises (SMSEs) represent a high proportion of companies around the world and become a cornerstone in the worldwide industry economy. These companies have realized that improving their process is crucial for success, but they are facing difficulties to implement it due to limited resources, limited knowledge, and time constraints. This study aimed to identify the sustainability success factors (SSFs) that have a positive impact on implementing SPI efforts in SMSEs. Data were collected through a systematic literature review (SLR) approach and quantitatively through a survey questionnaire. A list of 44 SSFs was identified during SLR and empirical study. Results illustrate that there is a positive correlation between the ranks obtained from both dataset (rs (44) = .548, ρ = .001). Therefore, there would be significant differences between the SSFs identified in both datasets. In conclusion, the top‐ranked factors can then be used to guide the SPI coordinators on where they should focus their attention to reach the desired SPI goals, which is crucial to deliver the software products and also facilitates in development of model for SPIs in the future.

Abstract User forums enable a large population of crowd‐users to publicly share their experience, useful thoughts, and concerns about the software applications in the form of user reviews. Recent research studies have revealed that end‐user reviews contain rich and pivotal sources of information for the software vendors and developers that can help undertake software evolution and maintenance tasks. However, such user‐generated information is often fragmented, with multiple viewpoints from various stakeholders involved in the ongoing discussions in the Reddit forum. In this article, we proposed a crowd‐based requirements engineering by valuation argumentation (CrowdRE‐VArg) approach that analyzes the end‐users discussion in the Reddit forum and identifies conflict‐free new features, design alternatives, or issues, and reach a rationale‐based requirements decision by gradually valuating the relative strength of their supporting and attacking arguments. The proposed approach helps to negotiate the conflict over the new features or issues between the different crowd‐users on the run by finding a settlement that satisfies the involved crowd‐users in the ongoing discussion in the Reddit forum using argumentation theory. For this purpose, we adopted the bipolar gradual valuation argumentation framework, extended from the abstract argumentation framework and abstract valuation framework. The automated CrowdRE‐VArg approach is illustrated through a sample crowd‐users conversation topic adopted from the Reddit forum about Google Map mobile application. Finally, we applied natural language processing and different machine learning algorithms to support the automated execution of the CrowdRE‐VArg approach. The results demonstrate that the proposed CrowdRE‐VArg approach works as a proof‐of‐concept and automatically identifies prioritized requirements‐related information for software engineers.

Software development processes play a key role in the software and system development life cycle. Processes are becoming complex and evolve rapidly due to the modern-day continuous software engineering (CSE) concepts, which are mainly based on continuous integration, continuous delivery, infrastructure-as-code, automation and more. The fast growing Chinese software development industry adopts various processes to achieve potential benefits offered in the international market. This study is conducted with the aim to investigate the trends of processes in practice in the Chinese industry. The survey questionnaire data is collected from 34 practitioners working in software development firms across the China and the results highlight that iterative and agile processes are extensively used in industrial setting. Furthermore, agile and traditional approaches are combined to develop the hybrid processes. Most of the participants are satisfied using the current development processes, however, they show interest to continuously improve the existing process models and methods. Finally, we noticed that majority of the software development organizations used the ISO 9001 standard for process assessment and improvement activities. The given results provide preliminary overview of processes deployed in the Chinese industry.

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 Software traceability plays a critical role in software maintenance and evolution. We conducted a systematic mapping study with six research questions to understand the benefits, costs, and challenges of using traceability in maintenance and evolution. We systematically selected, analyzed, and synthesized 63 studies published between January 2000 and May 2020, and the results show that traceability supports 11 maintenance and evolution activities, among which change management is the most frequently supported activity; strong empirical evidence from industry is needed to validate the impact of traceability on maintenance and evolution; easing the process of change management is the main benefit of deploying traceability practices; establishing and maintaining traceability links is the main cost of deploying traceability practices; 13 approaches and 32 tools that support traceability in maintenance and evolution were identified; improving the quality of traceability links , the performance of using traceability approaches , and tools are the main traceability challenges in maintenance and evolution. The findings of this study provide a comprehensive understanding of deploying traceability practices in software maintenance and evolution phase and can be used by researchers for future directions and practitioners for making informed decisions while using traceability in maintenance and evolution.

In recent years, the software release cost has been reduced dramatically due to the alteration from traditional shrink-wrapped software to software as a service. Organizations that can deliver their services continuously and with a high frequency have a higher ability to compete in the market. As a response to this, a substantial number of software companies acquired DevOps to establish a culture of effective communication and collaboration between development and operation teams and in order to enhance the production release frequency as well as to maintain the product quality. However, the DevOps environment requires a platform that aid in evaluating the performance of existing processes and provide improvement recommendations. On top of that, organizations can only achieve the perceived benefits of DevOps if their processes are mature and continuously measured. The objective of this research is to investigate the process improvement contributions made by researchers in the DevOps field. For this purpose, we performed a systematic literature review that resulted in several maturity models and best practices. Our ultimate aim is to develop a DevOps maturity model that can appraise and improve the processes in the DevOps environment.

The sequential recommendation system predicts user's future operations based on their historical interaction information and achieves good performance in recent work. However, when applying to the task of recommendation in software crowdsourcing platform, the accuracy of the previous recommendation models is significantly reduced because of the sparse interactive data and dynamic item list in the platform. The Generative Ranking based Sequential Recommendation Model (GRS) is proposed to solve the problems mentioned above. The generative layer is introduced into a translation-based recommendation model to prevent overfitting problem. By generating latent vector in feature space, the interpolation between encoded points is highly reduced and the model is adapted to achieve better performance by embedding auxiliary features into the model. The efficiency and feasibility of the model is validated by the experiment in different datasets extracted from crowdsourcing platforms.

To improve the performance of bug localization, there is necessity to solve the lexical mismatch between the natural language in the bug report and the programming language in the source file. A similarity integration method for bug localization is proposed, in which the similarity between bug report and source file is calculated by information retrieval (IR) and word embedding. More specifically, IR technique is used to collect the exact matches between bug report and source file. The terms in the bug report and the potential source files of different code tokens are connected by word embedding technique, which is used to complement with IR technique. Finally, deep neural network (DNN) is utilized to integrate extracted features to get the correlation between bug reports and source files. The experimental results show that the proposed approach outperforms several existing bug localization approaches in terms of Top N Rank, MAP, and MRR.

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 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 Development and operations (DevOps) practices significantly accelerate and automate the continuous delivery and deployment of software systems. However, adopting DevOps concepts is not a straightforward job. Most organizations are not able to keep pace with the rhythm of continuous delivery and deployment, which are key DevOps attributes. Despite the significance of DevOps programs, it is still unknown why software development firms are demotivated or unable to adopt them. We tried to fill this gap by investigating, prioritizing, and developing the taxonomy of the key factors that could impact the adaptation and implementation of DevOps practices. We extracted a total of 16 factors from the available literature and empirically assessed them using the survey approach. The identified factors are further classified into three core categories of the software process improvement (SPI) manifesto. The analytical hierarchy process (AHP) approach was used to calculate the prioritization weight for each factor and present it as a taxonomy. The developed taxonomy provides a roadmap to tackle the key challenges to implementing DevOps and offers suggestions for streamlining DevOps practices.

Component-based software systems play an important role in various critical areas and reliability of those systems considered significant attention. Some software reliability assessment approaches consider the impacts of different components on the system reliability but lack the analysis of fault propagation between components. In this paper, a novel reliability evaluation method is proposed, focusing on analyzing the impacts of different components based on three parameters i.e. self-influence, failure influence and fault propagation influence. First, we use the graph theory to model the component-based software system and later identify the significance of each component based on the above three parameters. Finally, the importance of each component is used for the system reliability assessment and optimization. Three existing examples are evaluated to demonstrate the effectiveness of the proposed approach and results show that the proposed method is able to get reasonable results that could be used for reliability optimization.

The employment of design patterns is considered as a benchmark of software quality in terms of reducing the number of software faults. However, the quantification of the information about the hinder design issues such as the number of roles, type of design pattern, and their association with anti‐pattern classes is still required. The authors propose a new methodology to evaluate the impact of certain design issues on the software quality in terms of quantification of fault density. Firstly, they mine the required information about the classes of each system under study. Secondly, they describe taxonomy to group the classes. Subsequently, they used statistical techniques to formulate and benchmark the results. They include the analysis of four open source projects with six design patterns and six anti‐patterns in the case study. The main consequences are (i) the pattern participant classes are less dense in faults, (ii) the classes involved in the structural association between design patterns and anti‐patterns are denser in faults, (iii) the pattern participant classes with multi‐role and anti‐pattern smell association is denser in faults as compared to others. The significant difference between fault density distributions of groups of classes is still unclear and required further empirical investigation.

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.

Abstract Software development firms have begun adopting the practice of global software development (GSD). The main reason for the shift toward globalization is the various benefits received by software development firms. However, there are several issues faced by GSD organizations, particularly those associated with software process improvement (SPI). It has been noticed that a formal process improvement approach could assist in successfully executing development activities in GSD. The core objective of this research work is to develop a global software engineering process improvement model (GSEPIM) to assess and improve software process activities in a GSD environment. The proposed model will be developed based on existing models in other domains, an empirical study conducted with GSD practitioners, and an understanding of critical success factors and challenges of SPI. In this study, the first step in the development of GSEPIM is completed by identifying the challenges of SPI in GSD and presenting a solution in the form of a robust framework.

The majority of organisations are globalising their software development activities by following the ideas of global software development (GSD). The motivation behind the adoption of GSD phenomena are the list of benefits gained by the software industry. However, there are different challenges face by the GSD organisations, particularly the issues related to software process improvement (SPI). The aim of this study is the identification and classification into categories of the success factors that can impact SPI initiatives taken in GSD organisations. The systematic literature review (SLR) method has been used to extract the success factors from the literature. SLR phases, ‘planning, conducting, and reporting the review’ have been followed to perform this study. Totally, 15 success factors were identified and classified into the six main categories. The authors have also reported the critical success factors of SPI, i.e. management commitment, staff involvement, roles and responsibilities, communication, and resources allocation. This article also reported the similarities and differences between the success factors classified on the bases of client‐vendor organisation and size of the organisation. The identified factors can contribute towards the implementation of SPI programme in both client and vendor GSD organisations because these factors represent key areas of process improvement.

Abstract Software organizations are increasingly combining agile methodologies and distributed software development (DSD) for efficiently and effectively built software products. There are numerous challenges associated with scaling agile methods in a distributed environment. Our study is intended to explore and prioritize the challenges for scaling agile practices in the DSD environment. This study was divided into 3 stages. In the first stage, 22 challenges were identified from literature review and grouped into 4 categories: management, team, technology, and process based on discussion with experts. In the second stage, an online questionnaire study was conducted to validate the identified challenges. Finally, analytic hierarchy process method was used to prioritize challenges and their categories based on their relative importance. The results highlighted that management is a most significant category as compared with the other categories. Similarly, lack of management commitments, lack of effective communication, lack of knowledge sharing, etc are identified as the most significant challenges that need to be focused by the organizations for scaling agile methodologies. On the basis of the research findings, we could conclude that the identified challenges along with their categories provide a robust framework to scale agile methodologies in the DSD environment.

The structural complexity of design components (e.g. Classes) is proportional to design quality at the system level and is quantified via the object-oriented metrics. The frequent use of design patterns causes of too much abstraction and can increase the structural complexity of design components. Though, in our previous work, we have empirically investigated the impact of use intensity of design pattern on the system level quality attributes. However, the empirical investigation of the effect of usage of design patterns on the design properties is still required. In this regard, we conduct an empirical study and perform a case study which includes the analysis 1) the existence of a correlation between design pattern usage and design metrics, 2) the confounding effect of system size (number of classes) on the correlation, and 3) how the change in number of employed design pattern instances affects the structural complexity in the subsequent releases of a system. The result of this study suggests that structural complexity associated with aggregation, coupling, functional abstraction design properties has a significant relationship with the employed instances of Template, Adapter-Command, Singleton, and Factory Method design patterns.

Context: In the plethora of studies, it has been empirically investigated that the incidence of design pattern instances can be considered as an indicator to elaborate the software design. The developers, who have more concern with design quality, are interested to know the effect of use intensity of design patterns on the system level design quality attributes. Goal: The objective of our study is to empirically investigate the effect of the frequent use of the Gang-of-Four (GoF) design patterns on the design quality attributes. Method: We perform a case study which includes three analyses in order to investigate, 1) the existence of a correlation between design pattern usage and design quality attributes, 2) the confounding effect of system size (number of classes) on the correlation, and 3) how the change in number of employed design pattern instances affects the design quality in the subsequent releases of a system. Results: The result of this study suggests that the reusability, flexibility and understandability have a significant relationship with the employed instances of Template, Adapter-Command, Singleton and State-Strategy design patterns, however, it is affected by the confounding effect of system size. Subsequently, in the subsequent releases of an open source project named velocity, we observed the use intensity of Singleton, Adapter-Command, and State-Strategy design patterns can improve the design quality in term of reusability and flexibility attributes.

Trend of software development is changing rapidly most of the software development organizations are trying to globalize their activities throughout the world. This trend leads towards a phenomenon called Global Software Development (GSD). The main reason behind the software globalization is its various benefits. Besides these benefits, software organizations are facing various challenges. One of these challenges is communication which is considered a big challenge in GSD and it becomes more complicated during the Requirements Change Management (RCM) process due to three factors, they are Geographical, Socio-cultural and Temporal distances. This paper presents a framework which shows the effect of these factors on communication during RCM process in GSD. Communication is the core function of collaboration which allows information to be exchanged between the team members. A pilot study has been conducted in three GSD organizations. A quantitative research method has been used to collect data. The findings from the survey data show that these three factors have a strong negative impact on communication process in GSD.