Journal of Software-Evolution and Process最新文献

{"title":"Understanding Remote Work Experience: Insights Into Well-Being","authors":"Aurora Vizcaíno,&nbsp;Julio Suárez,&nbsp;Darja Šmite,&nbsp;Félix O. García","doi":"10.1002/smr.2757","DOIUrl":"https://doi.org/10.1002/smr.2757","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>After the pandemic, software engineers were forced to work remotely, in many cases without prior experience of doing so.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The objective of this work is to analyze the factors that influence engineers' motivation, stress and performance when working remotely after the pandemic, and to what level.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A significant number (around 1000) of Latin-American software development professionals from different countries who work remotely were surveyed in order to study the factors that affect them and how when they work in this manner. The data collected from the survey were then statistically analyzed using the partial least square-structural equation modeling (PLS-SEM) method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The analysis of the data made it possible to conclude that there are direct negative effects of stress on performance and direct positive effects of motivation on performance. In addition, we found that skills, experience, and teamwork behavior, such as trust, communication, and knowledge sharing, play an important role when working remotely.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strengthening Large-Scale Agile Teams: The Interplay of High-Quality Relationships, Psychological Safety, and Learning From Failures","authors":"Muhammad Ovais Ahmad","doi":"10.1002/smr.2759","DOIUrl":"https://doi.org/10.1002/smr.2759","url":null,"abstract":"<p>Agile methods have become a standard practice within software industry, with organizations increasingly adopting large-scale agile (LSA) frameworks. However, as these frameworks are implemented across multiple teams and organizational functions, new challenges emerge, particularly in maintaining alignment, coherence, and collaboration across teams. One crucial element in addressing these challenges is fostering of a culture of continuous learning and psychological safety, with the objective of optimizing team performance and ensuring project success. Despite the importance of this topic, there is a significant gap in existing literature regarding antecedents of psychological safety and its impact on team learning and performance in LSA environments. This study aims to investigate impact of high-quality relationships and psychological safety on learning from failures and, consequently, on team performance in LSA context. An online survey of 167 software professionals in Sweden was conducted to test a conceptual model that is developed based on existing literature. The hypotheses were analyzed using partial least squares structural equation modeling. The results demonstrate strong positive correlation between the presence of high-quality relationships, psychological safety, and capacity to learn from failures and team performance. Specifically, the formation of high-quality relationships has been demonstrated to significantly enhance psychological safety, which in turn facilitates learning from failures and leads to improved team performance. These findings offer valuable insights for both practitioners and researchers, highlighting the importance of cultivating relational dynamics and a psychologically safe environment in LSA projects. Furthermore, the study offers guidance for future research, regarding the scalability and generalizability of these findings.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fuzzy-AHP Decision-Making Framework for Optimizing Software Maintenance and Deployment in Information Security Systems","authors":"Rafiq Ahmad Khan,&nbsp;Ismail Keshta,&nbsp;Hussein A. Al Hashimi,&nbsp;Alaa Omran Almagrabi,&nbsp;Hathal S. Alwageed,&nbsp;Musaad Alzahrani","doi":"10.1002/smr.2758","DOIUrl":"https://doi.org/10.1002/smr.2758","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Information System Security (ISS) is the primary economic lever for the global economy. It is the cornerstone for value generation, and its absence undeniably affects technology, people, and finances. The emergence of the worldwide information society has introduced fresh economic and legal challenges attributed to the surge in Internet utilization and advancements in the digital economy. Ensuring the security of advancements within information systems has emerged as a primary concern in propelling the evolution of information processes within the software development industry. This study aims to develop and propose a Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) framework to enhance decision-making for software maintenance and deployment in ISS. This framework aims to provide a systematic, flexible method for evaluating and prioritizing multiple conflicting criteria under conditions of uncertainty. The study initially adopts an empirical survey to identify software security maintenance and deployment risks and their practices for ISS organizations. Then adopts the Fuzzy-AHP method to handle the imprecision of expert judgments and organizes decision-making into a hierarchical structure. The framework is applied to evaluate key criteria related to software maintenance and deployment, including security risks, system performance, operational costs, and compliance requirements. Data from 50 ISS experts were collected and used to validate the framework. The paper identifies 52 security risks in maintenance and deployment (SRMD) processes in ISS and also identified 139 best practices for ensuring security, including regular updates, patch management, and adherence to industry-standard security protocols. The Fuzzy-AHP framework effectively structured the decision-making process by prioritizing criteria and sub-criteria. The results demonstrated that the framework helps mitigate the subjective biases in expert judgment and provides a more balanced assessment of maintenance and deployment strategies. Prioritizing security risks and compliance emerged as key factors in the decision-making process. The proposed Fuzzy-AHP framework provides an innovative and adaptable solution for optimizing ISS organizations' software maintenance and deployment decisions. It addresses the complexity and uncertainty involved in such decisions, offering a transparent and structured approach that improves the accuracy and reliability of outcomes. Future research should focus on empirical validation of the framework in real-world case studies and expand its application to other industries with similar decision-making needs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Literature Quality Assessment Skill in Novice Researchers: A Collaborative Card-Based Learning Approach","authors":"Affan Yasin,&nbsp;Rubia Fatima,&nbsp;Ira Puspitasari,&nbsp;Zheng JiangBin,&nbsp;Zhi Li","doi":"10.1002/smr.2753","DOIUrl":"https://doi.org/10.1002/smr.2753","url":null,"abstract":"<div>\u0000 \u0000 <p>Assessing the quality and credibility of research is crucial across disciplines. However, training early career scholars in systematic quality appraisal poses challenges. The rise of online grey literature increases the need for nuanced evaluation capabilities. This study aims to impart basic literature quality assessment knowledge in early career software engineering researchers using an interactive card-based learning activity. The PRISMA abstract quality checklist was adapted into a physical card deck. Sixteen novice researchers participated in a session using the cards to collaborate, discuss, and analyze a sample review paper abstract based on structured criteria. Quantitative feedback was gathered. Survey results indicated the card activity positively enhanced perceived understanding of quality principles, engagement, and assessment skills. Open-ended feedback highlighted cards improved focus, interactivity, and peer exchanges. This preliminary study provides encouraging evidence that a customized card-based approach can effectively instill foundational skills for assessing abstract quality while increasing motivation and enjoyment. Further research should evaluate long-term retention and optimal instructional design parameters.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Perspective of Agile Software Developers on Data Privacy","authors":"Mariana Peixoto,&nbsp;Tony Gorschek,&nbsp;Daniel Mendez,&nbsp;Carla Silva,&nbsp;Davide Fucci","doi":"10.1002/smr.2755","DOIUrl":"https://doi.org/10.1002/smr.2755","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent studies have shown that many software developers do not have sufficient knowledge and understanding of how to develop a privacy-friendly system. This may become a challenge in developing systems complying with data protection laws. To address this issue, we investigated the factors that influence developers' decision-making when developing privacy-sensitive systems. We conducted an empirical study by means of a survey with 109 practitioners. Our data analysis is based on the principles of social cognitive theory, which includes personal, behavioral, and external environmental factors. We identified six personal, five behavioral, and five external environment factors that affect how developers make decisions regarding privacy, including confusion between privacy and security and reliance on informal practices and organizational support gaps. These findings contribute to understanding how practitioners and companies consider privacy, showing improvements in formal training and structured support over previous studies yet highlighting persistent challenges in consistent privacy integration.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Networks-Based Software Development Effort Estimation: A Systematic Literature Review","authors":"Fatima Ezzahra Boujida,&nbsp;Fatima Azzahra Amazal,&nbsp;Ali Idri","doi":"10.1002/smr.2756","DOIUrl":"https://doi.org/10.1002/smr.2756","url":null,"abstract":"<div>\u0000 \u0000 <p>Software development effort estimation (SDEE) is a key task in managing software projects. Among the existing SDEE models, artificial neural networks (ANN) have garnered considerable attention from the software engineering community because of their ability to learn from previous data and yield acceptable estimates. However, to the best of the authors' knowledge, no systematic literature review (SLR) has been carried out with focus on the use of ANNs in SDEE. This work aims to analyze ANN-based SDEE studies from five view-points: estimation accuracy, accuracy comparison, estimation context, impact of combining ANN-based SDEE models with other techniques, and ANNs parameters. To find relevant ANN-based SDEE studies, we carried out an automated search using four electronic databases. The quality of the relevant papers was assessed to determine the set of papers to include in our review. We identified 65 papers published in the period 1993–2023 with acceptable quality score. The results of our systematic review revealed that ANN-based SDEE models perform better than 11 machine learning (ML) and non-ML SDEE models. Further, the estimation accuracy is improved when neural networks are used in combination with other techniques such as fuzzy clustering techniques. This study found that the use of ANN models in SDEE is promising to get accurate estimates. However, the application of ANN models in industry is still limited. Therefore, it is recommended that practitioners cooperate with researchers to encourage and facilitate the application of ANN models in industry.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Serious Game Approach to Introduce the Code Review Practice","authors":"Baris Ardic,&nbsp;Eray Tuzun","doi":"10.1002/smr.2750","DOIUrl":"https://doi.org/10.1002/smr.2750","url":null,"abstract":"<p>Code review is a widely utilized practice that focuses on improving code via manual inspections. However, this practice is not addressed adequately in a typical software engineering curriculum. We aim to help address the code review practice knowledge gap between the software engineering curricula and the industry with a serious game approach. We determine our learning objectives around the introduction of the code review process. To realize these objectives, we design, build, and test the serious game. We then conduct three case studies with a total of 280 students. We evaluated the results by comparing the student's knowledge and confidence about code review before and after case studies, as well as evaluating how they performed in code review quizzes and game levels themselves. Our analysis indicates that students had a positive experience during gameplay, and an in-depth examination suggests that playing the game also enhanced their knowledge. We conclude that the game had a positive impact on introducing the code review process. This study represents a step taken toward moving code review education from industry starting positions to higher education. The game and its auxiliary materials are available online.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Software Project Effort Estimation: Leveraging a NIVIM for Enhanced Preprocessing","authors":"Syed Sarmad Ali,&nbsp;Jian Ren,&nbsp;Ji Wu,&nbsp;Kui Zhang,&nbsp;Liu Chao","doi":"10.1002/smr.2745","DOIUrl":"https://doi.org/10.1002/smr.2745","url":null,"abstract":"<div>\u0000 \u0000 <p>Software development effort estimation (SDEE) is essential for effective project planning and relies heavily on data quality affected by incomplete datasets. Missing data (MD) are a prevalent problem in machine learning, yet many models treat it arbitrarily despite its significance. Inadequate handling of MD may introduce bias into the induced knowledge. It can be challenging to choose optimal imputation approaches for software development projects. This article presents a <i>novel incomplete value imputation model (NIVIM)</i> that uses a variational autoencoder (VAE) for imputation and synthetic data. By combining contextual and resemblance components, our approach creates an SDEE dataset and improves the data quality using contextual imputation. The key feature of the proposed model is its applicability to a wide variety of datasets as a preprocessing unit. Comparative evaluations demonstrate that NIVIM outperforms existing models such as VAE, generative adversarial imputation network (GAIN), <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>-nearest neighbor (K-NN), and multivariate imputation by chained equations (MICE). Our proposed model NIVIM produces statistically substantial improvements on six benchmark datasets, that is, ISBSG, Albrecht, COCOMO81, Desharnais, NASA, and UCP, with an average improvement in RMSE of <i>11.05%</i> to <i>17.72%</i> and MAE of <i>9.62%</i> to <i>21.96%</i>.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Deep Network for Secured Refactoring Framework by Predicting Code-Bad Smells in Software Projects","authors":"T. Pandiyavathi,&nbsp;B. Sivakumar","doi":"10.1002/smr.2749","DOIUrl":"https://doi.org/10.1002/smr.2749","url":null,"abstract":"<div>\u0000 \u0000 <p>In modern times, refactoring is one of the significantly utilized approaches for enhancing the software's quality like understandability, testability, and maintainability. Moreover, the refactoring effect on its security has been underrated. In addition to that, there are only a few studies that offer the classification over refactoring approaches depending on the effect over the quality attributes that help the designer to attain certain objectives by choosing the most significant approach and it is applied in the right places based on the specified software quality attributes. The contradictory outcomes are attained by considering the quality of the software creates limitations for the developers while performing the software refactoring process. In this paper, a secured deep learning-based software refactoring approach is designed. At first, software projects collected from online sources are offered as input for this software refactoring process to detect the security metrics in the projects. After detecting the security metrics, refactoring is applied in the software projects to change the internal design. Then, the security metrics of the refactored projects are detected again. Further, the security metrics computed before and after refactoring are compared with the software projects. The projects are labeled based on security, needs, and refactoring level. Then, the Ensemble Attention-based Deep Network (EA-DNet) is developed, which is designed with the Recurrent Neural Network (RNN), Deep Temporal Convolution Network (DTCN), and Bi-directional Long Short Term Memory (Bi-LSTM). This network is trained to get better results in the prediction of code-bad smells in software projects. The prior software refactoring approaches are compared with the proposed code-bad smells-based software refactoring process.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing the Efficacy of Rapid Review With a Systematic Review in the Software Engineering Field","authors":"Carolline Pena,&nbsp;Bruno Cartaxo,&nbsp;Igor Steinmacher,&nbsp;Deepika Badampudi,&nbsp;Deyvson da Silva,&nbsp;Williby Ferreira,&nbsp;Adauto Almeida,&nbsp;Fernando Kamei,&nbsp;Sérgio Soares","doi":"10.1002/smr.2748","DOIUrl":"https://doi.org/10.1002/smr.2748","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Context</h3>\u0000 \u0000 <p>Rapid Reviews are secondary studies aiming to deliver evidence to experts in a more timely manner and with lower costs than traditional literature reviews. Previous studies have shown that experts and researchers are positive toward Rapid Reviews. However, little is known about how Rapid Reviews differ from traditional Systematic Reviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The goal of this paper is to compare a Rapid Review with a Systematic Review in terms of their methods (e.g., search strategy, study selection, quality assessment, and data extraction) and findings to understand how optimizing the traditional Systematic Review method impacts what we obtain with Rapid Review.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>To achieve this goal, we conducted a Systematic Review with the same research questions answered by a pre-existing Rapid Review and compared those two studies. Also, we surveyed experts from industry and academia to evaluate the relevance of the findings obtained from both the secondary studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The Rapid Review lasted 6 days, while the Systematic Review took 1 year and 2 months. The main bottlenecks we identified in the Systematic Review are (i) executing the search strategy and (ii) selecting the procedure. Together, they took 10 months. The researchers had to analyze the information from 11,383 papers for the Systematic Review compared with 1973 for the Rapid Review. Still, most (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation>$$ sim $$</annotation>\u0000 </semantics></math> 78%) of the papers included in the Systematic Review were returned by the Rapid Review search, and some papers that could be included were unduly excluded during the Rapid Review's selection procedure. Both secondary studies identified the same number of pieces of evidence (30), but the pieces of evidence are not the same.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The Rapid Review and Systematic Review results are inherently different and complementary. The time and cost to conduct a Systematic Review can be prohibitive in experts' contexts. Thus, at least in such situations, a Rapid Review may be an adequate choice. Moreover, a Rapid Review may be executed in the experts' context as a previous low-cost step before deciding to invest in a high-cost Systematic Review.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}