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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 FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

The software industry has widely adopted global software development (GSD) to gain economic benefits. Organizations that engage in GSD face various challenges, the majority being associated with requirements change management (RCM). The key motive of this study is to develop a requirement change management and implementation maturity model (SRCMIMM) for the GSD industry that could help the practitioners to assess and manage their RCM activities. A systematic literature review and questionnaire survey approach are used to identify and validate the critical success factors (CSFs), critical challenges (CCHs), and the related best practices of the RCM process. The investigated CSFs and CCHs are classified into five maturity levels based on the concepts of the existing maturity models in other domains, practitioners' feedback, and academic research. Every maturity level comprises different CSFs and CCHs that can help assess and manage a firm's RCM capability. To evaluate the effectiveness of the proposed model, four case studies are conducted in different GSD firms. The SRCMIMM has been developed to assist GSD organizations in improving their RCM process in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Reaction of a transiently formed terminal phosphinidene complex with triethylamine resulted in the formation of an sp3 C–H insertion product, as revealed by 31P NMR spectroscopy, which was isolated as semi-solid compound. However, if the reaction was continued for 24 h, a primary phosphane complex was obtained eventually. The compounds were characterised by NMR spectroscopy and mass spectrometry. Formation of the final products is explained by a mechanistic proposal based on DFT calculations. [ABSTRACT FROM AUTHOR]

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 an Interaction-enhanced and Time-aware Graph Convolution Network (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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We report the case of a 36‑year‑old male who was referred to us with a provisional diagnosis of ethanol‑related acute on chronic liver failure with ascites, encephalopathy, and altered liver function tests (LFTs). The patient underwent a living donor liver transplant. Posttransplant, he had an uneventful recovery till postoperative day (POD) day 7 when he developed tachycardia and low‑grade fever. The immunosuppression has to be withheld till POD 26. The patient later developed deranged LFT and an initial liver biopsy showed features of acute cellular rejection. He also had ongoing tachycardia; thus, immunosuppression was withheld. Subsequent to that patient developed rapid progression from progressive early to chronic rejection (CR) within 78 days posttransplant. The patient initially responded to antithymocyte globulin, but finally succumbed to fungal infection. CR usually occurs over 6 months posttransplant. The occurrence of CR was noted very early in the index patients. This case highlights the importance of a basic step‑wise approach to a case post liver transplant. It also highlights the importance of specialized liver histopathology aiding in early diagnosis. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]