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This paper aims to close the gap between the generic concept of knowledge activities (KAs) and implementing them in the context of software engineering organisations concentrating on the non-technical aspects, such as team organisation and practices.

This qualitative research used a questionnaire with practitioners such as software developers and team leads who were asked to provide feedback on a set of team practices and measures typically used in software engineering projects and assess their relation to the activities of acquiring, codifying, storing, maintaining, transferring and creating knowledge. The obtained results were analysed using frequency analysis and further descriptive statistics yielding a matrix linking the investigated team practices and measures to KAs.

Team practices and measures commonly applied in software engineering can be facilitated to trigger particular KAs. While most of these team practices and measures originate from agile methods, they are not restricted to these. A purposeful composition can help in assembling a balanced set of KAs aimed at fostering given knowledge goals in software engineering organisations.

By bridging the communication and terminology gap between knowledge management research and software engineering practitioners, this work lays the foundation for assessing software teams’ knowledge profiles more easily and creating prerequisites for implementing knowledge management by facilitating common practices and measures often already part of their daily work. Hence, overhead can be avoided when implementing knowledge management.

To the best of the authors’ knowledge, this is the first study investigating application and relevance of KAs in the software industry by linking them to practices and measures well-accepted in software engineering, thus providing the necessary vocabulary for the implementation of knowledge management in software development teams.

This paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing mechanism; (2) User Experience collection of mobile applications are influenced by the mindset of Crowdmembers, culture/ethnicity/social background, ease of interface use and rewards, among other factors.

The authors of this paper, did a literature review first to find if Crowdsourcing was applicable and a used method to solve problems in Software Engineering. This helped us to narrow down the application of Crowdsourcing to the Requirements Engineering-Usability (User Experience) collection. User experience collection of two Malayalam language-based mobile applications, AarogyaSetu and BevQ was done as the next step. Incorporating findings from Study I, another study using AarogyaSetu and Manglish was launched as Study II. The results from both cases were consolidated and analyzed. Significant concerns relating to expectations of Crowd members with User Experience collection were unraveled and the purpose of Study was accomplished.

(1) Crowdsourcing is and can be used in Software Engineering activities. (2) Crowd members have expectations (motivating factors) of User Interface and other elements that enable them to be an effective contributor. (3) An individual’s environment and mindset (character) are influential in him becoming a contributor in Crowdsourcing. (4) Culture and social practices of a region strongly affects the crowd-participating decision of an individual.

This is purely self-done work. The value of this research work is two-fold. Crowdsourcing is endorsed significant in Software Engineering tasks, especially in User Experience collection of mobile applications. Two, the Crowd service requesters can be careful about designing the questionnaire for Crowdsourcing. They have to be aware and prepared to meet the expectations of the Crowd. This can ensure the active participation of potential contributors. Future researchers can use the results of this work to base their research on similar purposes.

This study aims to explore the alignment between strategic plans of the Portuguese public higher education institutions (HEIs) and their perception of the integration of sustainability in education and curricula.

The strategic plans from 15 institutions were selected for content analysis; data about the integration of sustainability in education and curricula, from these HEI, were collected with an online questionnaire (self-report survey). Qualitative and quantitative analyses were performed.

Strategic plans of the Portuguese public HEIs seem to not be sufficiently aligned with self-assessment integration of sustainability in education and curricula.

The classifications used in the content analysis were constructed and revised by the authors to reduce coder interpretation issues and subsequent bias in the results. However, some subjectivity could remain. The analysis of strategic plans and self-report surveys answered by top management, or a technician, does not assess the practices and sustainability implementation in education and curricula.

This study allows the self-report of already-implemented practices to be compared to the planned strategy of HEI governance in Portugal as stated in their strategic plans.

An analysis and respective insights on the lack of connection between strategic planning and self-report practices about sustainability implementation, using Portugal as a case study.

The purpose of this paper is to present an automatic approach for mesh sizing field generation of complicated  computer-aided design (CAD) models.

In this paper, the authors present an automatic approach for mesh sizing field generation. First, a source point extraction algorithm is applied to capture curvature and proximity features of CAD models. Second, according to the distribution of feature source points, an octree background mesh is constructed for storing element size value. Third, mesh size value on each node of background mesh is calculated by interpolating the local feature size of the nearby source points, and then, an initial mesh sizing field is obtained. Finally, a theoretically guaranteed smoothing algorithm is developed to restrict the gradient of the mesh sizing field.

To achieve high performance, the proposed approach has been implemented in multithreaded parallel using OpenMP. Numerical results demonstrate that the proposed approach is remarkably efficient to construct reasonable mesh sizing field for complicated CAD models and applicable for generating geometrically adaptive triangle/tetrahedral meshes. Moreover, since the mesh sizing field is defined on an octree background mesh, high-efficiency query of local size value could be achieved in the following mesh generation procedure.

How to determine a reasonable mesh size for complicated CAD models is often a bottleneck of mesh generation. For the complicated models with thousands or even ten thousands of geometric entities, it is time-consuming to construct an appropriate mesh sizing field for generating high-quality mesh. A parallel algorithm of mesh sizing field generation with low computational complexity is presented in this paper, and its usability and efficiency have been verified.

Cloud computing promises dependable services offered through next-generation data centres based on virtualization technologies for computation, network and storage. Big Data Applications have been made viable by cloud computing technologies due to the tremendous expansion of data. Disaster management is one of the areas where big data applications are rapidly being deployed. This study looks at how big data is being used in conjunction with cloud computing to increase disaster risk reduction (DRR). This paper aims to explore and review the existing framework for big data used in disaster management and to provide an insightful view of how cloud-based big data platform toward DRR is applied.

A systematic mapping study is conducted to answer four research questions with papers related to Big Data Analytics, cloud computing and disaster management ranging from the year 2013 to 2019. A total of 26 papers were finalised after going through five steps of systematic mapping.

Findings are based on each research question.

A specific study on big data platforms on the application of disaster management, in general is still limited. The lack of study in this field is opened for further research sources.

In terms of technology, research in DRR leverage on existing big data platform is still lacking. In terms of data, many disaster data are available, but scientists still struggle to learn and listen to the data and take more proactive disaster preparedness.

This study shows that a very famous platform selected by researchers is central processing unit based processing, namely, Apache Hadoop. Apache Spark which uses memory processing requires a big capacity of memory, therefore this is less preferred in the world of research.

This study aims to investigate the effects of organizational culture factors on the selection of software process development models and develops a conceptual model for selecting and adopting process development models with an organizational culture approach, using 12 criteria and their sub-criteria defined in Fey and Denison’s model (12 criteria).

The research hypotheses were investigated using statistical analysis, and then the criteria and sub-criteria were selected based on Fey and Denison’s model and the experts’ viewpoints. Afterward, the organizational culture of the selected company was measured using the data from 2016 and 2017, based on Fey and Denison’s questionnaire. Due to the correlation between the criteria, using the decision-making trial and evaluation technique, the correlation between sub-criteria were determined, and by analytical network process method and using Super-Decision software, the process development model was preferred to the 12 common models in information systems development.

Results indicated a significant and positive effect of organizational culture factors (except the core values factor) on the selection of development models. Also, by changing the value of organizational culture, the selected process development model changed either. Sensitivity analysis performed on the sub-criteria implied that by changing and improving some sub-criteria, the organization will be ready and willing to use the agile or risk-based models such as spiral and win-win models. Concerning units where the mentioned indicators were at moderate and low limits, models such as waterfall, V-shaped and incremental worked more appropriately.

While many studies were performed in comparing development models and investigating their strengths and weaknesses, and the impact of organizational culture on the success of information technology projects, literature indicated that the impact of organizational sub-culture prevailing in the selection of development process models has not been investigated. In this study, new factors and indicators were addressed affecting the selection of development models with a focus on organizational culture. Correlation among the factors and indicators was also investigated and, finally, a conceptual model was proposed for proper adoption of the models and methodologies of system development.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Usage of learning management systems (LMSs) has become widespread with the disruption of face-to-face educations after the COVID-19 pandemic. There are several software products, usually named as LMS to enable and support distance education. However, selection of a suitable LMS is a complex multiple criteria decision making (MCDM) problem that requires consideration of many criteria and inputs from different parties like students, academicians, education managers, etc. Usability evaluation of LMS is one of the critical steps in deciding which LMS system to be adapted. There are several studies related to usability evaluation of LMS in the literature, but utilization of MCDM methods and real life case studies are very rare. Based on this motivation, perceived usability evaluation of SAKAI-LMS that is in use at an academic department is performed by employing axiomatic design procedure (ADP). This paper aims to discuss the aforementioned issues.

ADP is considered as a suitable MCDM method for perceived usability evaluation as it allows an easy approach to data fusion and setting performance targets for decision makers. A questionnaire is developed to collect data from three types of system users about predetermined usability criteria and their importance. After detailed statistical analyses and weighting criteria via analytical hierarch process (AHP), ADP is carried out to evaluate usability of the LMS.

It is found that the proposed ADP based approach is easy to apply in practical circumstances and able to quantify perceived usability of the LMSs.

The proposed approach provides an easy and practical evaluation of perceived usability of the LMSs for decision makers who are responsible for the implementation of LMSs. The developed novel and practical MCDM-based perceived usability approach for LMS in this study has been verified through a real life case study at an academic department. Perceived usability results, therefore, reflects only the views of this focus group and are not generalizable.

First time in the literature, a comprehensive ADP based MCDM approach is proposed based on the analyses of the related literature and information gathered from the system users.

The purpose of this paper is to explore how gamification may be used for project stakeholder engagement.

The paper presents the results of a systematic literature review of extant research concerning the gamification of projects. Based on this, an agenda for future studies is outlined.

Extant research on the gamification of projects is scarce and scattered among various disciplines, but the engineering fields dominate. The research performed does indicate that gamification may be used for involving stakeholders in projects, primarily by promoting learning, but also by engaging them, motivating action and solving problems.

In several cases, extant research display poor quality in research design and a lack in cross-disciplinary perspectives, which means that more research is needed. The users’ perspective is often lacking. Furthermore, the ideas gamification might be “hidden” within other technologies.

The findings of this research may assist project management practitioners in the endeavor of adopting gamification principles to better involve stakeholders.

The study fills a gap in summarizing the research on how gamification may be used to promote project stakeholder engagement. Based on this, it proposes a research agenda for future research on the use of gamification to promote project stakeholder engagement.

This paper aims to provide a literature review on the cloud-based platforms for the education sectors. The several aspects of cloud computing adoption in education, remote/distance learning and the application of cloud-based design and manufacturing (CBDM) have been studied and theorised.

A four-step methodology was adopted to analyse and categorise the papers obtained through various search engines. Out of 429 research articles, 72 papers were shortlisted for the detailed analysis.

Many factors that influence cloud computing technology adoption in the education sector have been identified in this paper. The research findings on several research items have been tabulated and discussed. Based on the theoretical research done on cloud computing for education, cloud computing for remote/distance learning and CBDM, cloud computing could enhance the educational systems in mainly developing countries and improve the scope for remote/distance learning.

This study is limited to papers published only in the past decade from 2011 to 2020. Besides, this review was unable to include journal articles published in different languages. Nevertheless, for the effective teaching and learning process, this paper could help understand the importance and improve the process of adopting cloud computing concepts in educational universities and platforms.

This study is a novel one as a research review constituting cloud computing applications in education and extended for remote/distance learning and CBDM, which have not been studied in the existing knowledge base.