Pub Date : 2019-05-25DOI: 10.1109/ICSE-Companion.2019.00057
Kalvin Eng
We present a novel visual-inspection methodology that relies on formal concept analysis to help developers ensure that only needed parts of sensitive information are released to authorized users in an access control model. The first step involves the annotation of the to-be-exposed data using a domain-specific ontology, which includes sensitivity attributes at a meta-level for its elements. During the role-creation step, roles are assigned privileges in the form of queries that access different parts of the data. The resulting set of roles, each associated with its own set of queries, is represented in a roles-permissions matrix and transformed into a graphical concept lattice. The lattice can be analyzed and inspected for deficiencies in the access-control model, based on the data sensitivity attributes. We hypothesize that visualizing concept lattices are useful when creating access-control models to manage data access so that the unauthorized access to sensitive and private information is curtailed.
{"title":"Visually Identifying Potential Sensitive Information Leaks in Access-Controlled Data Services","authors":"Kalvin Eng","doi":"10.1109/ICSE-Companion.2019.00057","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00057","url":null,"abstract":"We present a novel visual-inspection methodology that relies on formal concept analysis to help developers ensure that only needed parts of sensitive information are released to authorized users in an access control model. The first step involves the annotation of the to-be-exposed data using a domain-specific ontology, which includes sensitivity attributes at a meta-level for its elements. During the role-creation step, roles are assigned privileges in the form of queries that access different parts of the data. The resulting set of roles, each associated with its own set of queries, is represented in a roles-permissions matrix and transformed into a graphical concept lattice. The lattice can be analyzed and inspected for deficiencies in the access-control model, based on the data sensitivity attributes. We hypothesize that visualizing concept lattices are useful when creating access-control models to manage data access so that the unauthorized access to sensitive and private information is curtailed.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129498649","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 : 2019-05-25DOI: 10.1109/ICSE-Companion.2019.00084
Jirayus Jiarpakdee
Software Quality Assurance (SQA) activities are exercised to ensure high-quality software systems. Defect models help developers identify the most risky modules to prioritise their limited SQA resources. The interpretation of defect models also helps managers understand what factors impact software quality to chart quality improvement plans. Unfortunately, the commonly-used interpretation techniques (e.g., ANOVA for logistic regression and variable importance for random forests) only explain defect models at the high level (e.g., what factors impact software quality). Researchers and practitioners also raise concerns about a lack of explainability of defect models that hinders the adoption in practice. This thesis hypothesises that: A lack of explainability poses a critical challenge when adopting defect models in practice. To validate the hypothesis, we formulate 3 research questions, i.e., (1) what is the best defect modelling workflow that produces the most accurate and reliable interpretation of defect models?, (2) what is the best technique for explaining the predictions of defect models?, and (3) how do practitioners perceive when adopting explainable defect models? Through case studies of publicly-available open-source and industrial software systems, the results show that correlated variables impact the interpretation of defect models and must be mitigated; our proposed feature selection technique, AutoSpearman, is the only studied feature selection technique that can automatically mitigate correlated variables with a little impact on model performance; and the instance-level interpretation of defect models is needed to derive actionable insights to guide operational and technical decisions in SQA efforts.
{"title":"Towards a More Reliable Interpretation of Defect Models","authors":"Jirayus Jiarpakdee","doi":"10.1109/ICSE-Companion.2019.00084","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00084","url":null,"abstract":"Software Quality Assurance (SQA) activities are exercised to ensure high-quality software systems. Defect models help developers identify the most risky modules to prioritise their limited SQA resources. The interpretation of defect models also helps managers understand what factors impact software quality to chart quality improvement plans. Unfortunately, the commonly-used interpretation techniques (e.g., ANOVA for logistic regression and variable importance for random forests) only explain defect models at the high level (e.g., what factors impact software quality). Researchers and practitioners also raise concerns about a lack of explainability of defect models that hinders the adoption in practice. This thesis hypothesises that: A lack of explainability poses a critical challenge when adopting defect models in practice. To validate the hypothesis, we formulate 3 research questions, i.e., (1) what is the best defect modelling workflow that produces the most accurate and reliable interpretation of defect models?, (2) what is the best technique for explaining the predictions of defect models?, and (3) how do practitioners perceive when adopting explainable defect models? Through case studies of publicly-available open-source and industrial software systems, the results show that correlated variables impact the interpretation of defect models and must be mitigated; our proposed feature selection technique, AutoSpearman, is the only studied feature selection technique that can automatically mitigate correlated variables with a little impact on model performance; and the instance-level interpretation of defect models is needed to derive actionable insights to guide operational and technical decisions in SQA efforts.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129581032","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 : 2019-05-01DOI: 10.1109/icse-companion.2019.00014
{"title":"Message from the Technical Briefings Chairs of ICSE 2019","authors":"","doi":"10.1109/icse-companion.2019.00014","DOIUrl":"https://doi.org/10.1109/icse-companion.2019.00014","url":null,"abstract":"","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715555","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 : 2019-05-01DOI: 10.1109/ICSE-Companion.2019.00105
Luiz Fernando Capretz, P. Waychal, Jingdong Jia, Daniel Varona, Yadira Lizama
This paper attempts to understand motivators and de-motivators that influence the decisions of software professionals to take up and sustain software testing careers across four different countries, i.e. Canada, China, Cuba, and India. The research question can be framed as "How many software professionals across different geographies are keen to take up testing careers, and what are the reasons for their choices?" Towards that, we developed a cross-sectional but simple survey-based instrument. In this study we investigated how software testers perceived and valued what they do and their environmental settings. The study pointed out the importance of visualizing software testing activities as a set of human-dependent tasks and emphasized the need for research that examines critically individual assessments of software testers about software testing activities. This investigation can help global industry leaders to understand the impact of work-related factors on the motivation of testing professionals, as well as inform and support management and leadership in this context.
{"title":"Studies on the Software Testing Profession","authors":"Luiz Fernando Capretz, P. Waychal, Jingdong Jia, Daniel Varona, Yadira Lizama","doi":"10.1109/ICSE-Companion.2019.00105","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00105","url":null,"abstract":"This paper attempts to understand motivators and de-motivators that influence the decisions of software professionals to take up and sustain software testing careers across four different countries, i.e. Canada, China, Cuba, and India. The research question can be framed as \"How many software professionals across different geographies are keen to take up testing careers, and what are the reasons for their choices?\" Towards that, we developed a cross-sectional but simple survey-based instrument. In this study we investigated how software testers perceived and valued what they do and their environmental settings. The study pointed out the importance of visualizing software testing activities as a set of human-dependent tasks and emphasized the need for research that examines critically individual assessments of software testers about software testing activities. This investigation can help global industry leaders to understand the impact of work-related factors on the motivation of testing professionals, as well as inform and support management and leadership in this context.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197531","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 : 2019-05-01DOI: 10.1109/ICSE-Companion.2019.00065
S. Nguyen
In a buggy configurable system, configuration-dependent bugs cause the failures in only certain configurations due to unexpected interactions among features. Manually localizing configuration-dependent faults in configurable systems could be highly time-consuming due to their complexity. However, the cause of configuration-dependent bugs is not considered by existing automated fault localization techniques, which are designed to localize bugs in non-configurable code. Thus, their capacity for efficient configuration-dependent localization is limited. In this work, we propose COFL, a novel approach to localize configuration-dependent bugs by identifying and analyzing suspicious feature interactions that potentially cause the failures in buggy configurable systems. We evaluated the efficiency of COFL in fault localization of artificial configuration-dependent faults in a highly-configurable system. We found that COFL significantly improves the baseline spectrum-based approaches. With COFL, on average, the correctness in ranking the buggy statements increases more than 7 times, and the search space is significantly narrowed down, about 15 times.
{"title":"Configuration-Dependent Fault Localization","authors":"S. Nguyen","doi":"10.1109/ICSE-Companion.2019.00065","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00065","url":null,"abstract":"In a buggy configurable system, configuration-dependent bugs cause the failures in only certain configurations due to unexpected interactions among features. Manually localizing configuration-dependent faults in configurable systems could be highly time-consuming due to their complexity. However, the cause of configuration-dependent bugs is not considered by existing automated fault localization techniques, which are designed to localize bugs in non-configurable code. Thus, their capacity for efficient configuration-dependent localization is limited. In this work, we propose COFL, a novel approach to localize configuration-dependent bugs by identifying and analyzing suspicious feature interactions that potentially cause the failures in buggy configurable systems. We evaluated the efficiency of COFL in fault localization of artificial configuration-dependent faults in a highly-configurable system. We found that COFL significantly improves the baseline spectrum-based approaches. With COFL, on average, the correctness in ranking the buggy statements increases more than 7 times, and the search space is significantly narrowed down, about 15 times.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116479502","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}
Software is incrementally evolved as various new feature requests are implemented to meet users' requirements. To accelerate the incoming feature implementation, developers often utilize existing third-party APIs that encapsulate featurerelated functionality into simple APIs. However, it is non-trivial for developers to choose which APIs to use and where to use them in a target program since the search space of APIs and their usage locations are usually large. In this paper, we introduce a tool, MULAPI, to facilitate the decision of suitable APIs at potential usage locations for implementing the incoming feature requests. MULAPI combines feature localization and information retrieval techniques to accomplish API recommendation and usage location. Empirical studies demonstrate that MULAPI can effectively recommend correct APIs and their usage locations with higher precision than state-of-the-art approaches. The video of our demo is available at https://youtu.be/s3Cs5ltqdvs.
{"title":"MULAPI: A Tool for API Method and Usage Location Recommendation","authors":"Congying Xu, Bosen Min, Xiaobing Sun, Jiajun Hu, Bin Li, Yucong Duan","doi":"10.1109/ICSE-Companion.2019.00053","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00053","url":null,"abstract":"Software is incrementally evolved as various new feature requests are implemented to meet users' requirements. To accelerate the incoming feature implementation, developers often utilize existing third-party APIs that encapsulate featurerelated functionality into simple APIs. However, it is non-trivial for developers to choose which APIs to use and where to use them in a target program since the search space of APIs and their usage locations are usually large. In this paper, we introduce a tool, MULAPI, to facilitate the decision of suitable APIs at potential usage locations for implementing the incoming feature requests. MULAPI combines feature localization and information retrieval techniques to accomplish API recommendation and usage location. Empirical studies demonstrate that MULAPI can effectively recommend correct APIs and their usage locations with higher precision than state-of-the-art approaches. The video of our demo is available at https://youtu.be/s3Cs5ltqdvs.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127589331","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 : 2019-05-01DOI: 10.1109/ICSE-Companion.2019.00055
A. Chen
In modern software development, maintenance is one of the most expensive processes. When end-users encounter software defects, they report the bug to developers by specifying the expected behavior and error messages (e.g., log message). Then, they wait for a bug fix from the developers. However, on the developers' side, it can be very challenging and expensive to debug the problem. To fix the bugs, developers often have to play the role of detectives: seeking clues in the user-reported logs files or stack trace in a snapshot of specific system execution. This debugging process may take several hours or even days. In this paper, we first look at the usefulness of the user-reported logs. Then, we propose an automated approach to assist the debugging process by reconstructing the execution path. Through the analysis, our investigation shows that 31% of the time, developer further requests logs from the reporter. Moreover, our preliminary results show that the reconducted path illustrates the user's execution. We believe that our approach proposes a novel solution in debugging production failures.
{"title":"An Empirical Study on Leveraging Logs for Debugging Production Failures","authors":"A. Chen","doi":"10.1109/ICSE-Companion.2019.00055","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00055","url":null,"abstract":"In modern software development, maintenance is one of the most expensive processes. When end-users encounter software defects, they report the bug to developers by specifying the expected behavior and error messages (e.g., log message). Then, they wait for a bug fix from the developers. However, on the developers' side, it can be very challenging and expensive to debug the problem. To fix the bugs, developers often have to play the role of detectives: seeking clues in the user-reported logs files or stack trace in a snapshot of specific system execution. This debugging process may take several hours or even days. In this paper, we first look at the usefulness of the user-reported logs. Then, we propose an automated approach to assist the debugging process by reconstructing the execution path. Through the analysis, our investigation shows that 31% of the time, developer further requests logs from the reporter. Moreover, our preliminary results show that the reconducted path illustrates the user's execution. We believe that our approach proposes a novel solution in debugging production failures.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133722067","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 : 2019-05-01DOI: 10.1109/ICSE-Companion.2019.00047
Naoyasu Ubayashi, Takuya Watanabe, Yasutaka Kamei, Ryosuke Sato
Nowadays, many software systems are required to be updated and delivered in a short period of time. It is important for developers to make software embrace uncertainty, because user requirements or design decisions are not always completely determined. This paper introduces iArch-U, an Eclipse-based uncertainty-aware software development tool chain, for developers to properly describe, trace, and manage uncertainty crosscutting over UML modeling, Java programming, and testing phases. Integrating with Git, iArch-U can manage why/when/where uncertain concerns arise or are fixed to be certain in a project. In this tool demonstration, we show the world of uncertainty-aware software development using iArch-U. Our tool is open source software released from http://posl.github.io/iArch/.
{"title":"Git-Based Integrated Uncertainty Manager","authors":"Naoyasu Ubayashi, Takuya Watanabe, Yasutaka Kamei, Ryosuke Sato","doi":"10.1109/ICSE-Companion.2019.00047","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00047","url":null,"abstract":"Nowadays, many software systems are required to be updated and delivered in a short period of time. It is important for developers to make software embrace uncertainty, because user requirements or design decisions are not always completely determined. This paper introduces iArch-U, an Eclipse-based uncertainty-aware software development tool chain, for developers to properly describe, trace, and manage uncertainty crosscutting over UML modeling, Java programming, and testing phases. Integrating with Git, iArch-U can manage why/when/where uncertain concerns arise or are fixed to be certain in a project. In this tool demonstration, we show the world of uncertainty-aware software development using iArch-U. Our tool is open source software released from http://posl.github.io/iArch/.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116787047","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 : 2019-05-01DOI: 10.1109/icse-companion.2019.00008
{"title":"Message from the Posters Chairs of ICSE 2019","authors":"","doi":"10.1109/icse-companion.2019.00008","DOIUrl":"https://doi.org/10.1109/icse-companion.2019.00008","url":null,"abstract":"","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127216963","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 : 2019-05-01DOI: 10.1109/ICSE-Companion.2019.00056
Xiaoning Du
Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but inefficient to achieve a full spectrum examination. In this work, we propose MARVEL, a generic, scalable and effective vulnerability detection platform. Firstly, a lightweight static tool, LEOPARD, is designed and implemented to identify potential vulnerable functions through program metrics. LEOPARD uses complexity metrics to group functions into a set of bins and then ranks functions in each bin with vulnerability metrics. Top functions in each bin are identified as potentially vulnerable. Secondly, a directed grey-box fuzzer is designed to take the results from LEOPARD for further confirmation. Our design stands out with the ability to automatically group adjacent functions and orchestrate both the macro level function directed fuzzing and the micro level path-condition directed fuzzing. LEOPARD is evaluated to cover 74.0% of vulnerable function when identifying 20% of functions as vulnerable and outperforms the baseline approaches. Further, three applications are proposed to demonstrate the usefulness of LEOPARD. As a result, we discovered 22 new bugs and eight of them are new vulnerabilities.
{"title":"MARVEL: A Generic, Scalable and Effective Vulnerability Detection Platform","authors":"Xiaoning Du","doi":"10.1109/ICSE-Companion.2019.00056","DOIUrl":"https://doi.org/10.1109/ICSE-Companion.2019.00056","url":null,"abstract":"Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but inefficient to achieve a full spectrum examination. In this work, we propose MARVEL, a generic, scalable and effective vulnerability detection platform. Firstly, a lightweight static tool, LEOPARD, is designed and implemented to identify potential vulnerable functions through program metrics. LEOPARD uses complexity metrics to group functions into a set of bins and then ranks functions in each bin with vulnerability metrics. Top functions in each bin are identified as potentially vulnerable. Secondly, a directed grey-box fuzzer is designed to take the results from LEOPARD for further confirmation. Our design stands out with the ability to automatically group adjacent functions and orchestrate both the macro level function directed fuzzing and the micro level path-condition directed fuzzing. LEOPARD is evaluated to cover 74.0% of vulnerable function when identifying 20% of functions as vulnerable and outperforms the baseline approaches. Further, three applications are proposed to demonstrate the usefulness of LEOPARD. As a result, we discovered 22 new bugs and eight of them are new vulnerabilities.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116751914","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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