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Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation. [ABSTRACT FROM AUTHOR]

Crowdsource software development has become more and more popular in recent years in the software industry. Crowdsourcing is an open‐call technique for outsourcing tasks to a broad and undefined crowd. Crowdsourcing provides numerous advantages including reduced costs, fast project completion, talent identification, diversity of solutions, top‐quality, and access to problem‐solving creativity. Despite of the benefits gained from crowdsourcing, there are numerous issues like lack of experienced workers, lack of confidentiality, copyright issues, software sustainability, and so forth. There is also less focus on the long‐term sustainability of software development because of new ideas emerging in crowdsourcing software development. Furthermore, in literature, lack of guidelines towards sustainable software crowdsourcing is highlighted as one of the limitations in the software standards. This study aims to identify the factors that influence sustainability aspects in crowdsourced software development. We have conducted a systematic literature review for identification of these factors. In this paper, we present findings of the systematic literature review in the form of a list of 11 factors extracted from a sample of 45 finally selected papers. Among these factors, six of the factors are ranked as critical factors. These critical factors are "Lack of coding standard in documentation," "Use of popular programming tools," "Crowd Lack of knowledge and awareness about sustainability," "Energy‐efficient coding," "Lack of awareness about sustainable software engineering practices," and "Lack of coordination/communication between client and crowd." [ABSTRACT FROM AUTHOR]

Airport operators have long known the importance of data collection. While many still struggle to utilise data in daily operations, best practice is emerging in the form of Connected Operations — a software-enabled approach whereby data is used proactively and flows through all operational areas. Manchester Airports Group (MAG) is committed to adopting this approach and is partnering with software company Copenhagen Optimization to help achieve its mission of operating the most intelligent airports in the world. The focus of the near two-year partnership has been the creation of an Airport Intelligence Platform. The platform is now reaching a level of maturity where, thanks to the effective use of artificial intelligence (AI) and algorithms, MAG's airports can predict what will happen in their operations, and through the use of prescriptive analytics, understand how to react. The result is a shift from siloed, short-term reactions to connected, proactive scenario planning in pursuit of strategic goals. This paper outlines the vision of the Connected Airport, reflects on the authors' shared experiences, the steps taken to make the vision a reality, and details the challenges experienced along the way. [ABSTRACT FROM AUTHOR]

Purpose: Through the lens of resource-based view (RBV), knowledge-based view (KBV) and DCV, this paper aims to investigate the relationship of entrepreneurial leadership (EL) on the project success (PS) and further examines the mediating effect of knowledge infrastructure capability (KIC), knowledge-based dynamic capability (KBDC) and Big data analytic capability (BDAC). Design/methodology/approach: The data were collected from 467 employees working on project in software companies. The data were evaluated using SMART-PLS, a structural equation modeling (SEM) tool. Findings: The study revealed a significant impact of EL on the PS, the study also found the significant mediation role of KIC, KBDC and BDAC on the EL and PS relationship. Originality/value: The research gives valuable insight into the effective role of EL as a contemporary leadership style in project-based firms. Also, this research is one of the first to examine knowledge-oriented dynamic capabilities (DC) as a knowledge fulcrum in project execution. These DC have been empirically proven to facilitate EL in achieving PS and support the firm in competing in an uncertain environment. [ABSTRACT FROM AUTHOR]

The trend toward global software development (GSD) has grown tremendously in recent years because of the rapid acceleration in information and communication technologies (ICTs). The reason for the changing trend toward GSD is to develop high‐quality software with minimum cost and time with round the clock. Despite the benefits gained from GSD, there are certain challenges associated with it. Literature reveals that software integration is an integral challenge in the GSD domain and vendors face numerous difficulties in integrating the software components to build the final product. The main aim of this research study is to develop a software integration model (SIM) that will assist GSD vendors to address the factors linked with various stages of software integration. This model is basically designed for GSD vendor organizations to assess and improve their software integration‐related activities. However, it is also beneficial for GSD client organizations in getting knowledge about the status of the GSD vendor organization's software integration capabilities. The research design is composed of well‐known research strategies including systematic literature review (SLR), questionnaire survey, and case study approach. This research study yielded the SIM. We identified nine critical success factors (CSFs) and 10 critical barriers (CBs) in software integration through SLR‐1 by extracting the data from a sample of 105 papers. Similarly, we also identified a total of 132 practices/solutions, for the identified CSFs and CBs in software integration, through SLR‐2 from a sample of 40 papers. These identified factors and practices were validated from 96 global software industry experts/practitioners through online survey. The identified CSFs, CBs, and their associated practices have been arranged adequately across the six software integration levels of SIM. We have adapted SIM structure from capability maturity model integration (CMMI), implementation maturity model (IMM), and software outsourcing vendors readiness model (SOVRM). We also conducted six case studies in the software industry for SIM applicability and validity. The participants' feedback from the six case studies in GSD vendor's organizations portray that the SIM is beneficial for GSD vendors to assess their software integration capability. [ABSTRACT FROM AUTHOR]

This paper explores the association between developer job satisfaction and life satisfaction on a global scale. With the increasing prominence of the software development industry and its impact on individuals' professional and personal lives, understanding the connection between job satisfaction and overall life satisfaction becomes crucial. We present a comprehensive analysis based on data collected from diverse regions and cultures, aiming to determine the magnitude of the correlation between these two constructs. Through a systematic review of existing literature, surveys, and empirical studies, we compile a robust dataset encompassing responses from developers across different countries and career stages. To assess job satisfaction, we used a worldwide survey from StackOverflow. Life satisfaction is evaluated from the World Values Survey, the Human Development Index, and the World Happiness Report. We grouped the world into logical geographic regions to discover key insights. Our findings reveal a modest correlation between developer job satisfaction and life satisfaction worldwide. Despite the significant impact of job satisfaction on one's professional life, the influence on overall life satisfaction appears to be more nuanced. While several studies have reported positive associations between the two constructs, our analysis suggests that the correlation is relatively weak, indicating that job satisfaction alone cannot fully predict an individual's level of life satisfaction. [ABSTRACT FROM AUTHOR]

Software development effort estimation is regarded as a crucial activity for managing project cost, time, and quality, as well as for the software development life cycle. As a result, proper estimating is crucial to the success of projects and to lower risks. Software effort estimation has drawn much research interest recently and has become a problem for the software industry. When results are inaccurate, an effort may be over- or under-estimated, which can disastrously affect project resources. In the sector, machine learning methods are becoming more and more prominent. Therefore, in this paper, we propose a Modified Jaya algorithm to improve the effectiveness of the estimated model; Modified JOA selects the ideal subset of components from an extensive feature collection. Then, the ensemble machine learning-based Enhanced Extreme gradient boosting algorithm and Ensemble Learning machine approach are employed to estimate the software effort. On the PROMISE SDEE repository, the proposed methodologies are empirically assessed. In this approach, applying machine learning techniques to the effort estimation process increases the likelihood that the time and cost estimates will be accurate. The proposed approach yields a greater performance. The key benefit of this approach is that it lowers the computational cost. This approach can also inspire the development of a tool that could reliably, effectively, and accurately estimate the effort required to complete different software projects. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Practical implications: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

The growing availability of applications (apps) for smart gadgets has been phenomenal in recent years. Both independent developers and multinational corporations are working to boost their app ratings in order to stay competitive in the mobile app industry. Therefore, it is crucial to consider apps from the perspective of the end user. In recent years, there has been a meteoric rise in the use of wearable apps. However, there have been surprisingly few investigations of the difficulties inherent with wearable apps. The purpose of this research is to mine user evaluations in order to get an understanding of consumer concerns about wearable apps. In this paper, fifteen app issues have been identified. Then we applied the DEMATEL (Decision Making Trial and Evaluation Laboratory) method to analyse the wearable app issues (WIs) and divide these issues into cause-and-effect groups. To begin, multiple experts assess the direct relationships between influential issues in wearable apps. The evaluation results are presented as spherical fuzzy numbers (SFN). Secondly, convert the linguistic terms into SFN. Thirdly, based on DEMATEL, the cause-effect classifications of issues are obtained. Finally, the issues in the cause category are identified as WIs in wearable apps. The outcome of the research is compared with the other variants of DEMATEL, like rough Z-number-based DEMATEL and spherical fuzzy DEMATEL, and the comparative results suggest that spherical fuzzy DEMATEL is the most suitable method to analyse the interrelationship of different issues in wearable apps. The outcome of this work definitely assists the app and software industry in the successful identification of the issues on which professionals and project managers could really focus. [ABSTRACT FROM AUTHOR]

DevOps has emerged as a practical approach to the interaction between development and operations. This approach has been extended to the interaction with business functions, generating the term BizDevOps. Although many proposals and tools support BizDevOps from a technical viewpoint, there has been no significant improvement in management aspects, such as evaluating the practices and processes involved in the area. This paper presents two case studies on the assessment of BizDevOps practices using MMBDO (Maturity Model for BizDevOps), a maturity model for BizDevOps based on international ICT standards. The case studies were conducted in two organizations with different profiles: a large multinational ICT company with a unit specialized in providing DevOps to the rest of the company and a small software development company that implements DevOps practices as part of its development system. The evidence we obtained in the case studies supports the adequacy of MMBDO for assessing the maturity of BizDevOps in real companies. Also, the combination of the two case studies supports the adequacy of the model for being used in companies with different sizes and levels of maturity. MMBDO is an adequate proposal for assessing the maturity of BizDevOps in software organizations and a suitable tool for process improvement. [ABSTRACT FROM AUTHOR]

Stakeholder-driven development has become significant in the software industry. As a result, product managers have taken on a crucial role in IT projects by considering the demands of stakeholders, business objectives and knowing available technological solutions. The theoretical section of this article provides an overview of the evolution and characteristics of the product manager role. This paper empirically explored the various expectations towards product managers through the example of the Hungarian software industry by the content analysis of job advertisements (n=212) from LinkedIn. The identified patterns show that the product manager role differs depending on the size of the company. The results show that as the size of a company increases, leadership and active listening skills become increasingly important. On the other hand, micro- and small enterprises demonstrate the highest level of responsibility regarding cooperation. The findings of this article can offer valuable guidance for future product managers. [ABSTRACT FROM AUTHOR]

This article describes the development of a decision support tool for a Rich Vehicle Routing Problem (R-VRP) of a major European do-it-yourself (DIY) retailer. The tool enables more comprehensive and structured planning at the retailer and an average of 8% transportation cost savings, translating to total savings of more than € 1 million per year for this specific retailer compared with the status quo. This article describes the development of a decision support tool for a Rich Vehicle Routing Problem (R-VRP) of a major do-it-yourself (DIY) retailer supplying its stores across Europe from multiple depots. The retailer uses external logistic service providers (LSPs) for the delivery to its stores and has two modes to choose from. In the first mode, the retailer proposes delivery tours to LSPs for execution. These tours are billed according to a nonlinear tariff with volume discounts depending on the delivery zones visited and the load carried. The LSPs accept the retailer's tour proposal only if tour duration and distance restrictions are kept. The latter is ensured by a relative detour limit. In the second mode, the retailer assigns single shipments to common carriers that consolidate them with shipments from other customers and bill this based on load and destination. The resulting problem represents an open VRP with two delivery modes, carrier selection and a heterogeneous fleet. Multiple delivery modes are standard in DIY retailing and constitute a general industry problem. The literature on VRPs and current software applications in the industry predominantly considers modeling and solution approaches that rely on linear distance costs, neglecting that nonlinear zone-based tariffs with volume discounts are standard in the freight forwarding business. Our work addresses this issue by developing a decision support tool for the retailer based on an exact algorithm for solving R-VRPs with a nonlinear zone-based tariff scheme and a relative detour limit. The tool is based on an innovative three-component set partitioning algorithm working on a complete set of feasible tours to solve the problem. We show that our approach optimally solves the daily distribution problem of the industry partner with up to 150 stores. Furthermore, implementing the tool enables more comprehensive and structured planning for the retailer and an average of 8% transportation cost savings, translating to total savings of more than €1 million per year for this specific retailer compared with the status quo. History: This paper was refereed. [ABSTRACT FROM AUTHOR]

Requirements collection is difficult and a critical phase of software development life cycle, in particular in a global software development (GSD) environment. In GSD, clients and vendors are physically separated such that there exist challenges such as lack of face‐to‐face communication, language differences, culture variation, and time zone differences. The objective of current research is to identify critical challenges of requirement engineering in GSD. There is no work done yet to empirically analyze all possible challenges via questionnaire survey from software industries. This research paper empirically investigates and analyze the identified challenges from systematic literature review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. From this research, 13 challenges during requirements implementation in the context of GSD have been identified previously via SLR. These challenges were then evaluated using an empirical approach of questionnaire survey. The results from respondents were analyzed based on type of respondents, level of experience of respondents, and from client–vendor perspective. Finally, the challenges were prioritized based on its frequency of occurrences from the SLR and the questionnaire survey. The relationships between the challenges and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference. The implication of this research work lies in both fundamental and practical perspective. The prioritized set of challenges was provided based on SLR, and questionnaire survey acts as a knowledge base for both researchers and industrial practitioners. This work will help researchers to identify challenges in GSD projects or other software engineering areas. [ABSTRACT FROM AUTHOR]

Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, R2 value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets. [ABSTRACT FROM AUTHOR]

The main advantage of Agile Project Management (APM) lies in its ability to investigate and resolve issues that arise during the project period, making timely adjustments to save resources and deliver successful projects on time and at a lower cost. Customers play a crucial role in determining many of these changes, highlighting their special involvement in managing projects using an agile approach. This paper prioritizes the factors influencing challenges related to customer knowledge in APM, initially identifying three categories: individual, organizational, and technological factors. Expert opinions from the software development industry verified these factors, while the DANP method explored their causal relationships and importance. The analysis revealed organizational factors' impact on the other two categories, with individual factors ranking highest, followed by technological factors. Notable challenges related to customer knowledge include lack of time for knowledge sharing, reluctance to adopt information technology systems, ineffective communication between knowledge-holders and seekers, inadequate training on new technology, and a lack of awareness regarding knowledge benefits for project partners. These findings are presented as suggestions to project teams for effectively managing agile projects and addressing customer knowledge challenges. By implementing these recommendations, projects can achieve greater efficiency and success. [ABSTRACT FROM AUTHOR]

Technical debt (TD) poses a significant risk in the software industry, representing the compromises made during software development that result in long-term consequences. To facilitate the management of TD, it is important to explore the causes of TD and the effects of the presence of TD on software projects. While the concept of TD has been investigated in several countries, no study has explored TD in the context of Saudi Arabia. In this paper, we aim to investigate the familiarity of the TD concept within the Saudi software practitioners and identifying the common causes and effects of TD in the Saudi software industry. We replicated a global survey called InsighTD, using the same survey instrument and the analysis procedure but collecting the data from software practitioners in Saudi Arabia. A total of 48 Saudi software practitioners completed the survey. The study findings indicate that the TD concept is not well recognized among the Saudi software practitioners (50% of the survey respondents have never heard of it before). Additionally, we identify the common causes and effects of TD from the perspective of Saudi software practitioners. The top causes of TD were tight-release deadlines, work pressure, lack of experience, and inappropriate project planning. On the other hand, the top effects of TD were increased maintenance effort/cost, low software quality, and delayed delivery of new versions. In conclusion, our findings support previous research regarding the most common TD causes and effects. In addition, they highlight some important factors in the Saudi software industry. [ABSTRACT FROM AUTHOR]

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Despite potential benefits in Software Engineering, adoption of software modelling in industry is low. Technical issues such as tool support have gained significant research before, but individual guidance and training have received little attention. As a first step towards providing the necessary guidance in modelling, we conduct a systematic literature review to explore the current state of the art. We searched academic literature for guidance on model creation and selected 35 papers for full-text screening through three rounds of selection. We find research on model creation guidance to be fragmented, with inconsistent usage of terminology, and a lack of empirical validation or supporting evidence. We outline the different dimensions commonly used to provide guidance on software and system model creation. Additionally, we provide definitions of the three terms modelling method, style, and guideline as current literature lacks a well-defined distinction between them. These definitions can help distinguishing between important concepts and provide precise modelling guidance. [ABSTRACT FROM AUTHOR]

Software developers usually adopt free trial strategies to promote their products in the software industry. Limited-time, limited-function, and hybrid have been becoming prevalent free trial strategies in reducing consumers' uncertainty about software quality. How to choose the optimal free trial strategy is a core issue facing software developers. This paper discusses the optimal software free trial strategy, as well as optimal pricing, optimal free quality, and optimal free time length based on network effects in a platform supply chain. The results show that hybrid is always optimal and the intensity of network effects plays a key role between limited-time and limited-function. Limited-time is superior to limited-function for a medium intensity of network effects, whereas limited-time is inefficient and limited-function is optimal for a weak/strong intensity of network effects. [ABSTRACT FROM AUTHOR]

Agile development methods have become a standard in the software industry, including in large-scale projects. These methods share a set of underlying assumptions that distinguish them from more traditional plan-driven approaches. In this article, we adopt Alvesson and Sandberg's problematization approach to challenge three key assumptions that are prevalent in the large-scale agile literature: (1) agile and plan-driven methods are mutually exclusive; (2) self-managing and hierarchically organized teams are mutually exclusive; and (3) agile methods can scale through simple linear composition. Using a longitudinal case study of large-scale agile development, we describe a series of trigger events and episodes whereby the agile approach was tailored to address the needs of the large-scale development context, which was very much at odds with these fundamental assumptions. We develop a set of new underlying assumptions which suggest that agile and plan-driven practices are mutually enabling and necessary for coordination and scaling in large-scale agile projects. We develop nine propositions for large-scale agile projects based on these new alternative underlying assumptions. Finally, we summarize our theoretical contribution in a generic process model of continuously adjusting agile and plan-driven practices in order to accommodate process challenges in large-scale agile projects. [ABSTRACT FROM AUTHOR]

API design refers to the process of developing Application Programming Interfaces (APIs) that expose the functionality of software applications to their clients. This paper provides a performance and availability-related analysis of three modern API design techniques, namely Representational State Transfer, Google’s Remote Procedure Call Framework, and Apache Thrift to practically show which of these techniques should be used in the implementation of the API Gateways for performance and availability considering that the gateways undertake a crucial role as they are the backbone of communication in Microservices Architecture and the performance and availability are two critical factors that determine how well API gateways can perform. For our analysis, an API Gateway prototype for each of the three API design techniques was developed by using Microsoft’s .NET framework, and load-tests against each of these three prototypes were performed and analyzed. The results of this paper are expected to be helpful for both researchers and practitioners in the software industry who need to choose from different industrial API design techniques for API Gateway development. [ABSTRACT FROM AUTHOR]

The proliferation of malware in recent years has posed a significant threat to the security of computers and mobile devices. Detecting malware, especially on the Android platform, has become a growing concern for researchers and the software industry. This paper proposes a new method for detecting Android malware based on unbalanced heterogeneous graph embedding. First of all, most malware datasets contain an imbalance of malicious and benign samples, since some types of malware are scarce and difficult to collect. Thus, as a result of this problem, the classification algorithm is unable to analyze the minority samples through sufficient data, resulting in poor downstream classifier performance, in light of the fact that adversarial generation networks possess the characteristic of completing data, an algorithm for generating graph structure data is presented, in which nodes are generated to simulate the distribution of minority nodes within a network topology. Then, considering that heterogeneous information networks have the characteristics of retaining rich node semantic features and mining implicit relationships, heterogeneous graphs are used to construct models for different types of entities (i.e. Apps, APIs, permissions, intents, etc.) and different meta-paths. Finally, a new method is introduced to alleviate the over-smoothing phenomenon of node information in the propagation of deep network. In the deep GCN, we first sample the leader nodes of each layer node, and then add a residual connection and an identity map in order to determine the characteristics of the high-order leader. In this paper, a self-attention-based semantic fusion method is also applied to adaptively fuse embedded representations of software nodes under different meta-paths. The test results demonstrate that the proposed IHODroid model effectively detects malicious software. In the DREBIN dataset, which consists of 123,453 Android applications and 5,560 malicious samples, the IHODroid model achieves an accuracy of 0.9360 and an F1 score of 0.9360, outperforming other state-of-the-art baseline methods. • A new generative adversarial network model has been proposed for balancing data. • Heterogeneous graphs are used for modeling malware detection. • A new method is introduced to alleviate the over-smoothing phenomenon. [ABSTRACT FROM AUTHOR]