Pub Date : 2021-03-28DOI: 10.1109/ICSE-Companion52605.2021.00046
Mehdi Keshani
As a rich source of data, Call Graphs are used for various applications including security vulnerability detection. Despite multiple studies showing that Call Graphs can drastically improve the accuracy of analysis, existing ecosystem-scale tools like Dependabot do not use Call Graphs and work at the package-level. Using Call Graphs in ecosystem use cases is not practical because of the scalability problems that Call Graph generators have. Call Graph generation is usually considered to be a "full program analysis" resulting in large Call Graphs and expensive computation. To make an analysis applicable to ecosystem scale, this pragmatic approach does not work, because the number of possible combinations of how a particular artifact can be combined in a full program explodes. Therefore, it is necessary to make the analysis incremental. There are existing studies on different types of incremental program analysis. However, none of them focuses on Call Graph generation for an entire ecosystem. In this paper, we propose an incremental implementation of the CHA algorithm that can generate Call Graphs on-demand, by stitching together partial Call Graphs that have been extracted for libraries before. Our preliminary evaluation results show that the proposed approach scales well and outperforms the most scalable existing framework called OPAL.
{"title":"Scalable Call Graph Constructor for Maven","authors":"Mehdi Keshani","doi":"10.1109/ICSE-Companion52605.2021.00046","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00046","url":null,"abstract":"As a rich source of data, Call Graphs are used for various applications including security vulnerability detection. Despite multiple studies showing that Call Graphs can drastically improve the accuracy of analysis, existing ecosystem-scale tools like Dependabot do not use Call Graphs and work at the package-level. Using Call Graphs in ecosystem use cases is not practical because of the scalability problems that Call Graph generators have. Call Graph generation is usually considered to be a \"full program analysis\" resulting in large Call Graphs and expensive computation. To make an analysis applicable to ecosystem scale, this pragmatic approach does not work, because the number of possible combinations of how a particular artifact can be combined in a full program explodes. Therefore, it is necessary to make the analysis incremental. There are existing studies on different types of incremental program analysis. However, none of them focuses on Call Graph generation for an entire ecosystem. In this paper, we propose an incremental implementation of the CHA algorithm that can generate Call Graphs on-demand, by stitching together partial Call Graphs that have been extracted for libraries before. Our preliminary evaluation results show that the proposed approach scales well and outperforms the most scalable existing framework called OPAL.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128220316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-27DOI: 10.1109/ICSE-Companion52605.2021.00116
Michael Alexander Trols, A. Mashkoor, Alexander Egyed
Consistency checking of interdependent heterogeneous engineering artifacts, such as requirements, specifications, and code, is a challenging task in large-scale engineering projects. The lack of team-oriented solutions allowing a multitude of project stakeholders to collaborate in a consistent manner is thus becoming a critical problem. In this context, this work proposes an approach for team-oriented consistency checking of collaboratively developed heterogeneous engineering artifacts.
{"title":"Team-Oriented Consistency Checking of Heterogeneous Engineering Artifacts","authors":"Michael Alexander Trols, A. Mashkoor, Alexander Egyed","doi":"10.1109/ICSE-Companion52605.2021.00116","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00116","url":null,"abstract":"Consistency checking of interdependent heterogeneous engineering artifacts, such as requirements, specifications, and code, is a challenging task in large-scale engineering projects. The lack of team-oriented solutions allowing a multitude of project stakeholders to collaborate in a consistent manner is thus becoming a critical problem. In this context, this work proposes an approach for team-oriented consistency checking of collaboratively developed heterogeneous engineering artifacts.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115022192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-25DOI: 10.1109/ICSE-Companion52605.2021.00057
Zehao Wang
To process data more efficiently, big data frameworks provide data abstractions to developers. However, due to the abstraction, there may be many challenges for developers to understand and debug the data processing code. To uncover the challenges in using big data frameworks, we first conduct an empirical study on 1,000 Apache Spark-related questions on Stack Overflow. We find that most of the challenges are related to data transformation and API usage. To solve these challenges, we design an approach, which assists developers with understanding and debugging data processing in Spark. Our approach leverages statistical sampling to minimize performance overhead, and provides intermediate information and hint messages for each data processing step of a chained method pipeline. The preliminary evaluation of our approach shows that it has low performance overhead and we receive good feedback from developers.
{"title":"Understanding the Challenges and Assisting Developers with Developing Spark Applications","authors":"Zehao Wang","doi":"10.1109/ICSE-Companion52605.2021.00057","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00057","url":null,"abstract":"To process data more efficiently, big data frameworks provide data abstractions to developers. However, due to the abstraction, there may be many challenges for developers to understand and debug the data processing code. To uncover the challenges in using big data frameworks, we first conduct an empirical study on 1,000 Apache Spark-related questions on Stack Overflow. We find that most of the challenges are related to data transformation and API usage. To solve these challenges, we design an approach, which assists developers with understanding and debugging data processing in Spark. Our approach leverages statistical sampling to minimize performance overhead, and provides intermediate information and hint messages for each data processing step of a chained method pipeline. The preliminary evaluation of our approach shows that it has low performance overhead and we receive good feedback from developers.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121623558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-25DOI: 10.1109/ICSE-Companion52605.2021.00058
Junjie Li
Static bug detection tools help developers detect code problems. However, it is known that they remain underutilized due to various reasons. Recent advances to incorporate static bug detectors in modern software development workflows can better motivate developers to fix the reported warnings on the fly. In this paper, we study the effectiveness of the state-of-the-art (SOA) solution in tracking warnings by static bug detectors and propose a better solution based on our analysis of the insufficiencies of the SOA solution. In particular, we examined four large-scale open-source systems and crafted a data set of 3,452 static code warnings by two static bug detectors. We manually uncover the ground-truth evolution status of the selected warnings: persistent, resolved, or newly-introduced. Moreover, upon manual analysis, we identified the critical reasons behind the insufficiencies of the SOA matching algorithm. Finally, we propose a better approach to improve the tracking of static warnings over software development history. Our evaluation shows that our proposed approach provides a significant improvement in the precision of the tracking, i.e., from 66.9% to 90.0%.
{"title":"A Better Approach to Track the Evolution of Static Code Warnings","authors":"Junjie Li","doi":"10.1109/ICSE-Companion52605.2021.00058","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00058","url":null,"abstract":"Static bug detection tools help developers detect code problems. However, it is known that they remain underutilized due to various reasons. Recent advances to incorporate static bug detectors in modern software development workflows can better motivate developers to fix the reported warnings on the fly. In this paper, we study the effectiveness of the state-of-the-art (SOA) solution in tracking warnings by static bug detectors and propose a better solution based on our analysis of the insufficiencies of the SOA solution. In particular, we examined four large-scale open-source systems and crafted a data set of 3,452 static code warnings by two static bug detectors. We manually uncover the ground-truth evolution status of the selected warnings: persistent, resolved, or newly-introduced. Moreover, upon manual analysis, we identified the critical reasons behind the insufficiencies of the SOA matching algorithm. Finally, we propose a better approach to improve the tracking of static warnings over software development history. Our evaluation shows that our proposed approach provides a significant improvement in the precision of the tracking, i.e., from 66.9% to 90.0%.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114324317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-24DOI: 10.1109/ICSE-Companion52605.2021.00054
Deyu Tian
Mobile applications (apps) often suffer from failure nowadays. Developers usually pay more attention to the failure that is perceived by users and compromises the user experience. Existing approaches focus on mining large volume logs to detect failure, however, to our best knowledge, there is no approach focusing on detecting whether users have actually perceived failure, which directly influence the user experience. In this paper, we propose a novel approach to detecting user-perceived failure in mobile apps. By leveraging the frontend user traces, our approach first builds an app page model, and applies an unsupervised detection algorithm to detect whether a user has perceived failure. Our insight behind the algorithm is that when user-perceived failure occurs on an app page, the users will backtrack and revisit the certain page to retry. Preliminary evaluation results show that our approach can achieve good detection performance on a dataset collected from real world users.
{"title":"Detecting User-Perceived Failure in Mobile Applications via Mining User Traces","authors":"Deyu Tian","doi":"10.1109/ICSE-Companion52605.2021.00054","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00054","url":null,"abstract":"Mobile applications (apps) often suffer from failure nowadays. Developers usually pay more attention to the failure that is perceived by users and compromises the user experience. Existing approaches focus on mining large volume logs to detect failure, however, to our best knowledge, there is no approach focusing on detecting whether users have actually perceived failure, which directly influence the user experience. In this paper, we propose a novel approach to detecting user-perceived failure in mobile apps. By leveraging the frontend user traces, our approach first builds an app page model, and applies an unsupervised detection algorithm to detect whether a user has perceived failure. Our insight behind the algorithm is that when user-perceived failure occurs on an app page, the users will backtrack and revisit the certain page to retry. Preliminary evaluation results show that our approach can achieve good detection performance on a dataset collected from real world users.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133144899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-23DOI: 10.1109/ICSE-Companion52605.2021.00117
Binger Chen, Ziawasch Abedjan
Program translation is a growing demand in software engineering. Manual program translation requires programming expertise in source and target language. One way to automate this process is to make use of the big data of programs, i.e., Big Code. However, existing code retrieval techniques lack the design to cover cross-language code retrieval. Other data-driven approaches require human efforts in constructing cross-language parallel datasets to train translation models. In this paper, we present RPT, a novel code translation retrieval system. We propose a lightweight but informative program representation, which can be generalized to all imperative PLs. Furthermore, we present our index structure and hierarchical filtering mechanism for efficient code retrieval from a Big Code database.
{"title":"RPT: Effective and Efficient Retrieval of Program Translations from Big Code","authors":"Binger Chen, Ziawasch Abedjan","doi":"10.1109/ICSE-Companion52605.2021.00117","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00117","url":null,"abstract":"Program translation is a growing demand in software engineering. Manual program translation requires programming expertise in source and target language. One way to automate this process is to make use of the big data of programs, i.e., Big Code. However, existing code retrieval techniques lack the design to cover cross-language code retrieval. Other data-driven approaches require human efforts in constructing cross-language parallel datasets to train translation models. In this paper, we present RPT, a novel code translation retrieval system. We propose a lightweight but informative program representation, which can be generalized to all imperative PLs. Furthermore, we present our index structure and hierarchical filtering mechanism for efficient code retrieval from a Big Code database.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124844616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-18DOI: 10.1109/ICSE-Companion52605.2021.00059
Bianca Trinkenreich
Women represent less than 24% of the software development industry and suffer from various types of prejudice and biases. In Open Source Software projects, despite a variety of efforts to increase diversity and multi-gendered participation, women are even more underrepresented (less than 10%). My research focuses on answering the question: How can OSS communities increase women's participation in OSS projects? I will identify the different OSS career pathways, and develop a holistic view of women's motivations to join or leave OSS, along with their definitions of success. Based on this empirical investigation, I will work together with the Linux Foundation to design attraction and retention strategies focused on women. Before and after implementing the strategies, I will conduct empirical studies to evaluate the state of the practice and understand the implications of the strategies.
{"title":"Please Don't Go — Increasing Women's Participation in Open Source Software","authors":"Bianca Trinkenreich","doi":"10.1109/ICSE-Companion52605.2021.00059","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00059","url":null,"abstract":"Women represent less than 24% of the software development industry and suffer from various types of prejudice and biases. In Open Source Software projects, despite a variety of efforts to increase diversity and multi-gendered participation, women are even more underrepresented (less than 10%). My research focuses on answering the question: How can OSS communities increase women's participation in OSS projects? I will identify the different OSS career pathways, and develop a holistic view of women's motivations to join or leave OSS, along with their definitions of success. Based on this empirical investigation, I will work together with the Linux Foundation to design attraction and retention strategies focused on women. Before and after implementing the strategies, I will conduct empirical studies to evaluate the state of the practice and understand the implications of the strategies.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127559050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-15DOI: 10.1109/ICSE-Companion52605.2021.00131
Bianca Trinkenreich
Women represent less than 24% of employees in the software development industry and experience various types of prejudice and bias. Despite various efforts to increase diversity and multi-gendered participation, women are even more underrepresented in Open Source Software (OSS) projects. In my Ph.D., I investigate the following question: How can OSS communities increase women's participation in their projects? I will identify different OSS career pathways and develop a holistic view of women's motivations to join or leave OSS, as well as their definitions of success. Based on this empirical investigation, I will work together with the Linux Foundation to design attraction and retention strategies focused on women. Before and after implementing the strategies, I will conduct empirical studies to evaluate the state of the practice and understand the implications of the strategies.
{"title":"Please Don't Go — A Comprehensive Approach to Increase Women's Participation in Open Source Software","authors":"Bianca Trinkenreich","doi":"10.1109/ICSE-Companion52605.2021.00131","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00131","url":null,"abstract":"Women represent less than 24% of employees in the software development industry and experience various types of prejudice and bias. Despite various efforts to increase diversity and multi-gendered participation, women are even more underrepresented in Open Source Software (OSS) projects. In my Ph.D., I investigate the following question: How can OSS communities increase women's participation in their projects? I will identify different OSS career pathways and develop a holistic view of women's motivations to join or leave OSS, as well as their definitions of success. Based on this empirical investigation, I will work together with the Linux Foundation to design attraction and retention strategies focused on women. Before and after implementing the strategies, I will conduct empirical studies to evaluate the state of the practice and understand the implications of the strategies.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117098867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-12DOI: 10.1109/ICSE-Companion52605.2021.00047
Mingyue Zhang
Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. However, modeling the system as a single player, as done in prior works in security domain, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game and model the defender (i.e., the system) at the granularity of components in system architecture. The system architecture model is translated into a Bayesian multi-player game, where each component is modeled as an independent player while security attacks are encoded as variant types for the components. The defensive strategy for the system is dynamically computed by solving the pure equilibrium to achieve the best possible system utility, improving the resiliency of the system against security attacks.
{"title":"System Component-Level Self-Adaptations for Security via Bayesian Games","authors":"Mingyue Zhang","doi":"10.1109/ICSE-Companion52605.2021.00047","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00047","url":null,"abstract":"Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. However, modeling the system as a single player, as done in prior works in security domain, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game and model the defender (i.e., the system) at the granularity of components in system architecture. The system architecture model is translated into a Bayesian multi-player game, where each component is modeled as an independent player while security attacks are encoded as variant types for the components. The defensive strategy for the system is dynamically computed by solving the pure equilibrium to achieve the best possible system utility, improving the resiliency of the system against security attacks.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115020459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-12DOI: 10.1109/ICSE-Companion52605.2021.00064
Omar Elazhary
Continuous practices are a staple of the modern software development workflow. Automation, in particular, is widely adopted due to its benefits related to quality and productivity. However, automation, similarly to all other aspects of the software development workflow, interacts with humans (in this case developers). While some work has investigated the impact of automation on developers, it is not clear to what extent context and process influence that impact. We present our ADEPT theory of developers and automation, in an attempt to bridge this gap and identify the possible ways context, process, and other factors may influence how developers perceive, interpret, and interact with automation.
{"title":"Investigating the Interplay between Developers and Automation","authors":"Omar Elazhary","doi":"10.1109/ICSE-Companion52605.2021.00064","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00064","url":null,"abstract":"Continuous practices are a staple of the modern software development workflow. Automation, in particular, is widely adopted due to its benefits related to quality and productivity. However, automation, similarly to all other aspects of the software development workflow, interacts with humans (in this case developers). While some work has investigated the impact of automation on developers, it is not clear to what extent context and process influence that impact. We present our ADEPT theory of developers and automation, in an attempt to bridge this gap and identify the possible ways context, process, and other factors may influence how developers perceive, interpret, and interact with automation.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132244766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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