CMake is one of the most widely used build automation tools in the industry. Facebook engineers often rely on examining large and complex CMake build files for various program analyses tasks. In this paper, we report on some of the unique challenges when analyzing CMake files at Facebook.
{"title":"Analyzing the CMake Build System","authors":"KimHao Nguyen, T. Nguyen, Quoc-Sang Phan","doi":"10.1145/3510457.3513064","DOIUrl":"https://doi.org/10.1145/3510457.3513064","url":null,"abstract":"CMake is one of the most widely used build automation tools in the industry. Facebook engineers often rely on examining large and complex CMake build files for various program analyses tasks. In this paper, we report on some of the unique challenges when analyzing CMake files at Facebook.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117082611","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}
Zexin Zhong, Jiangchao Liu, Diyu Wu, Peng Di, Yulei Sui, A. Liu
Taint analysis is widely used for tracing sensitive data. However, the state-of-the-art taint analyzers face challenges on recall, scalability, and precision when applied on industrial microservices. To overcome these challenges, we present a field-based static taint analysis approach, which does not distinguish different instances of the same type but distinguishes fields of the same kind for tracing sensitive data on industrial microservices. The experimental results demonstrate that our approach is practical in industrial scenarios.
{"title":"Field-Based Static Taint Analysis for Industrial Microservices","authors":"Zexin Zhong, Jiangchao Liu, Diyu Wu, Peng Di, Yulei Sui, A. Liu","doi":"10.1145/3510457.3513075","DOIUrl":"https://doi.org/10.1145/3510457.3513075","url":null,"abstract":"Taint analysis is widely used for tracing sensitive data. However, the state-of-the-art taint analyzers face challenges on recall, scalability, and precision when applied on industrial microservices. To overcome these challenges, we present a field-based static taint analysis approach, which does not distinguish different instances of the same type but distinguishes fields of the same kind for tracing sensitive data on industrial microservices. The experimental results demonstrate that our approach is practical in industrial scenarios.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132563408","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}
Preethu Rose Anish, Prashant Lawhatre, Ranit Chatterjee, Vivek Joshi, S. Ghaisas
Business Rules (BRs) are a critical artifact in the requirements elicitation phase of the software development life cycle. Several taxonomies have been proposed for classification of BRs. In this paper, we utilize Ross's BR classification schema and present an approach to automatically label and classify BRs along this schema. Our approach uses Data Programming (DP) for generating labeled training data needed for training two deep learning-based models to classify the BRs. We obtained an average labeling accuracy of 0.73 for all the BR classes using DP. Upon evaluating the approach on industryspecific dataset, we obtained highest weighted F-score (0.69) with a Bi-LSTM with attention-based model.
{"title":"Automated Labeling and Classification of Business Rules from Software Requirement Specifications","authors":"Preethu Rose Anish, Prashant Lawhatre, Ranit Chatterjee, Vivek Joshi, S. Ghaisas","doi":"10.1145/3510457.3513071","DOIUrl":"https://doi.org/10.1145/3510457.3513071","url":null,"abstract":"Business Rules (BRs) are a critical artifact in the requirements elicitation phase of the software development life cycle. Several taxonomies have been proposed for classification of BRs. In this paper, we utilize Ross's BR classification schema and present an approach to automatically label and classify BRs along this schema. Our approach uses Data Programming (DP) for generating labeled training data needed for training two deep learning-based models to classify the BRs. We obtained an average labeling accuracy of 0.73 for all the BR classes using DP. Upon evaluating the approach on industryspecific dataset, we obtained highest weighted F-score (0.69) with a Bi-LSTM with attention-based model.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130489891","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}
Emma Söderberg, Luke Church, J. Börstler, D. Niehorster, Christofer Rydenfält
The practice of code review is widely adopted in industry and has been studied to an increasing degree in the research community. However, the developer experience of code review has received limited attention. Here, we report on initial results from a mixed-method exploratory study of the developer experience.
{"title":"What's bothering developers in code review?","authors":"Emma Söderberg, Luke Church, J. Börstler, D. Niehorster, Christofer Rydenfält","doi":"10.1145/3510457.3513083","DOIUrl":"https://doi.org/10.1145/3510457.3513083","url":null,"abstract":"The practice of code review is widely adopted in industry and has been studied to an increasing degree in the research community. However, the developer experience of code review has received limited attention. Here, we report on initial results from a mixed-method exploratory study of the developer experience.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921154","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 : 2022-05-01DOI: 10.1109/ICSE-SEIP55303.2022.9793899
Hyungjin Kim, Yonghwi Kwon, H. Kwon, Yeonhee Ryou, Sangwoo Joh, Taeksu Kim, Chul-Joo Kim
Code Review is an essential activity to ensure the quality of the software in the development process. Code Review Automation with various analyses can reduce human efforts of code review activities. However, it is a challenge to automate the code review process for large-scale companies such as Samsung Electronics due to their complex development environments: many kinds of products, various sizes of software, different version control systems, and diverse code review systems. In this work, we show how we automated the code review process for those intricate environments, and share some lessons learned during two years of operation. Our unified code review automation system, Code Review Bot, is designed to process review requests holistically regardless of such environments. Our findings provide practical evidence that our system motivates developers in Samsung Electronics to improve code quality.
{"title":"A Unified Code Review Automation for Large-scale Industry with Diverse Development Environments","authors":"Hyungjin Kim, Yonghwi Kwon, H. Kwon, Yeonhee Ryou, Sangwoo Joh, Taeksu Kim, Chul-Joo Kim","doi":"10.1109/ICSE-SEIP55303.2022.9793899","DOIUrl":"https://doi.org/10.1109/ICSE-SEIP55303.2022.9793899","url":null,"abstract":"Code Review is an essential activity to ensure the quality of the software in the development process. Code Review Automation with various analyses can reduce human efforts of code review activities. However, it is a challenge to automate the code review process for large-scale companies such as Samsung Electronics due to their complex development environments: many kinds of products, various sizes of software, different version control systems, and diverse code review systems. In this work, we show how we automated the code review process for those intricate environments, and share some lessons learned during two years of operation. Our unified code review automation system, Code Review Bot, is designed to process review requests holistically regardless of such environments. Our findings provide practical evidence that our system motivates developers in Samsung Electronics to improve code quality.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130188751","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}
Daniel Elsner, Roland Wuersching, Markus Schnappinger, A. Pretschner, Maria Graber, René Dammer, Silke Reimer
At IVU Traffic Technologies, continuous integration (CI) pipelines build, analyze, and test the code for inadvertent effects before pull requests are merged. However, compiling the entire code base and executing all regression tests for each pull request is infeasible due to prohibitively long feedback times. Regression test selection (RTS) aims to reduce the testing effort. Yet, existing safe RTS techniques are not suitable, as they largely rely on language-specific program analysis. The IVU code base consists of more than 13 million lines of code in Java or C/C++ and contains thousands of non-code artifacts. Regression tests commonly operate across languages, using cross-language links, or read from non-code artifacts. In this paper, we describe our build system aware multi-language RTS approach, which selectively compiles and executes affected code modules and regression tests, respectively, for a pull request. We evaluate our RTS technique on 397 pull requests, covering roughly 2,700 commits. The results show that we are able to safely exclude up to 75% of tests on average (no undetected real failures slip into the target branches) and thereby save 72% of testing time, whereas end-to-end CI pipeline time is reduced by up to 63% on average.
{"title":"Build System Aware Multi-language Regression Test Selection in Continuous Integration","authors":"Daniel Elsner, Roland Wuersching, Markus Schnappinger, A. Pretschner, Maria Graber, René Dammer, Silke Reimer","doi":"10.1145/3510457.3513078","DOIUrl":"https://doi.org/10.1145/3510457.3513078","url":null,"abstract":"At IVU Traffic Technologies, continuous integration (CI) pipelines build, analyze, and test the code for inadvertent effects before pull requests are merged. However, compiling the entire code base and executing all regression tests for each pull request is infeasible due to prohibitively long feedback times. Regression test selection (RTS) aims to reduce the testing effort. Yet, existing safe RTS techniques are not suitable, as they largely rely on language-specific program analysis. The IVU code base consists of more than 13 million lines of code in Java or C/C++ and contains thousands of non-code artifacts. Regression tests commonly operate across languages, using cross-language links, or read from non-code artifacts. In this paper, we describe our build system aware multi-language RTS approach, which selectively compiles and executes affected code modules and regression tests, respectively, for a pull request. We evaluate our RTS technique on 397 pull requests, covering roughly 2,700 commits. The results show that we are able to safely exclude up to 75% of tests on average (no undetected real failures slip into the target branches) and thereby save 72% of testing time, whereas end-to-end CI pipeline time is reduced by up to 63% on average.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127311435","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}
Georges Aaron Randrianaina, Xhevahire Tërnava, D. Khelladi, Mathieu Acher
Code review is essential for assuring system quality in software engineering. Over decades in practice, code review has evolved to be a lightweight tool-based process focusing on code change: the smallest unit of the development cycle, and we refer to it as Modern Code Review (MCR). MCR involves code contributors committing code changes and code reviewers reviewing the assigned code changes. Such a reviewer assigning process is challenged by efficiently finding appropriate reviewers. Recent studies propose automated code reviewer recommendation (CRR) approaches to resolve such challenges. These approaches are often evaluated on open-source projects and obtain promising performance. However, the code reviewer recommendation systems are not widely used on proprietary projects, and most current reviewer selecting practice is still manual or, at best, semi-manual. No previous work systematically evaluated these approaches’ effectiveness and compared each other on proprietary projects in practice. In this paper, we performed a quantitative analysis of typical recommendation approaches on proprietary projects in Tencent. The results show an imperfect performance of these approaches on proprietary projects and reveal practical challenges like the “cold start problem”. To better understand practical challenges, we interviewed practitioners about the expectations of applying reviewer recommendations to a production environment. The interview involves the current systems’ limitations, expected application scenario, and information requirements. Finally, we discuss the implications and the direction of practical code reviewer recommendation tools.
{"title":"Code Reviewer Recommendation in Tencent: Practice, Challenge, and Direction*","authors":"Georges Aaron Randrianaina, Xhevahire Tërnava, D. Khelladi, Mathieu Acher","doi":"10.1145/3510457.3513035","DOIUrl":"https://doi.org/10.1145/3510457.3513035","url":null,"abstract":"Code review is essential for assuring system quality in software engineering. Over decades in practice, code review has evolved to be a lightweight tool-based process focusing on code change: the smallest unit of the development cycle, and we refer to it as Modern Code Review (MCR). MCR involves code contributors committing code changes and code reviewers reviewing the assigned code changes. Such a reviewer assigning process is challenged by efficiently finding appropriate reviewers. Recent studies propose automated code reviewer recommendation (CRR) approaches to resolve such challenges. These approaches are often evaluated on open-source projects and obtain promising performance. However, the code reviewer recommendation systems are not widely used on proprietary projects, and most current reviewer selecting practice is still manual or, at best, semi-manual. No previous work systematically evaluated these approaches’ effectiveness and compared each other on proprietary projects in practice. In this paper, we performed a quantitative analysis of typical recommendation approaches on proprietary projects in Tencent. The results show an imperfect performance of these approaches on proprietary projects and reveal practical challenges like the “cold start problem”. To better understand practical challenges, we interviewed practitioners about the expectations of applying reviewer recommendations to a production environment. The interview involves the current systems’ limitations, expected application scenario, and information requirements. Finally, we discuss the implications and the direction of practical code reviewer recommendation tools.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115501418","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}
Machine learning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine-learning experiments and assets when building intelligent software systems. Among others, they allow tracing back system behavior to experiment runs, for instance, when model performance drifts. Unfortunately, despite a surge of these tools, they are not well integrated with traditional software engineering tooling, and no hard empirical data exists on their effectiveness and value for users. We present a short research agenda and early results towards unified and effective software engineering and experiment management software.
{"title":"On the Effectiveness of Machine Learning Experiment Management Tools","authors":"S. Idowu, O. Osman, D. Strüber, T. Berger","doi":"10.1145/3510457.3513084","DOIUrl":"https://doi.org/10.1145/3510457.3513084","url":null,"abstract":"Machine learning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine-learning experiments and assets when building intelligent software systems. Among others, they allow tracing back system behavior to experiment runs, for instance, when model performance drifts. Unfortunately, despite a surge of these tools, they are not well integrated with traditional software engineering tooling, and no hard empirical data exists on their effectiveness and value for users. We present a short research agenda and early results towards unified and effective software engineering and experiment management software.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129739439","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}
Context: DevOps and microservices are acknowledged to be important new paradigms to tackle contemporary software demands and provide capabilities for rapid and reliable software development. Industrial reports show that they are quickly adopted together in massive software companies. However, because of the technical and organizational requirements, many difficulties against efficient implementation of the both emerge in real software teams. Objectives: This study aims to discover the organization, benefits and issues of software teams using DevOps & microservices from an immersive perspective. Method: An ethnographic study was carried out in three companies with different business, size, products, customers, and degree of globalization. All the three companies claimed their adoption of DevOps and microservices. Seven months (cumulative) of participant observations and nine interviews with practitioners were conducted to collect the data of software teams related to DevOps and microservices. A cross-company empirical investigation using grounded theory was done by analyzing the archive data. Results: The virtual software teams were organized for adopting DevOps and microservices under the stubborn organizational structure. The adoption of DevOps and microservices brings benefits to rapid delivery, ability improvements and burden reduction, whilst the high cost and lack of practical guidance were emerged. Two major issues of adopting DevOps and microservices in software teams (i.e. fragmentary DevOps and abuse of microservices) were found common in the companies. Moreover, our observations and interviews reflect that in software teams, the relationship between DevOps and microservices is not significant, which differs from the relationship described in the previous studies. Four lessons for practitioners and four implications for researchers were discussed based on our findings. Conclusion: Our findings contribute to the understanding of the organization, benefits and issues of adopting DevOps and microservices from an immersive perspective of software teams.
{"title":"A Cross-Company Ethnographic Study on Software Teams for DevOps and Microservices: Organization, Benefits, and Issues","authors":"Xin Zhou, Huang Huang, He Zhang, Xin Huang, Dong Shao, Chenxing Zhong","doi":"10.1145/3510457.3513054","DOIUrl":"https://doi.org/10.1145/3510457.3513054","url":null,"abstract":"Context: DevOps and microservices are acknowledged to be important new paradigms to tackle contemporary software demands and provide capabilities for rapid and reliable software development. Industrial reports show that they are quickly adopted together in massive software companies. However, because of the technical and organizational requirements, many difficulties against efficient implementation of the both emerge in real software teams. Objectives: This study aims to discover the organization, benefits and issues of software teams using DevOps & microservices from an immersive perspective. Method: An ethnographic study was carried out in three companies with different business, size, products, customers, and degree of globalization. All the three companies claimed their adoption of DevOps and microservices. Seven months (cumulative) of participant observations and nine interviews with practitioners were conducted to collect the data of software teams related to DevOps and microservices. A cross-company empirical investigation using grounded theory was done by analyzing the archive data. Results: The virtual software teams were organized for adopting DevOps and microservices under the stubborn organizational structure. The adoption of DevOps and microservices brings benefits to rapid delivery, ability improvements and burden reduction, whilst the high cost and lack of practical guidance were emerged. Two major issues of adopting DevOps and microservices in software teams (i.e. fragmentary DevOps and abuse of microservices) were found common in the companies. Moreover, our observations and interviews reflect that in software teams, the relationship between DevOps and microservices is not significant, which differs from the relationship described in the previous studies. Four lessons for practitioners and four implications for researchers were discussed based on our findings. Conclusion: Our findings contribute to the understanding of the organization, benefits and issues of adopting DevOps and microservices from an immersive perspective of software teams.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128750549","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}
In a Machine Learning (ML) system, characteristics of the ML components create new challenges for software system design and development activities. Data-dependent behavior causes risks in ML systems. Dealing with such risks in the development phase requires non-trivial costs due to un-controllable data generation processes in the test phase. In addition, ML systems often need continuous monitoring and validation in run-time. In this paper, we propose an integrated dependency tracking system that balances the cost and risks in the development stage and operation stage. Our solution uses blockchain (an immutable data store) to track the co-evolution of the models and the corresponding datasets. The provenance of data and models provides a trustworthy trace for dependencies between datasets and models at the development phase, and predictions at the operation phase. A graph database is used to provide visualization and query of the provenance information, and enables explainability for the model-data co-evolution.
{"title":"Dependency Tracking for Risk Mitigation in Machine Learning (ML) Systems","authors":"Xiwei Xu, Chen Wang, Zhen Wang, Q. Lu, Liming Zhu","doi":"10.1145/3510457.3513058","DOIUrl":"https://doi.org/10.1145/3510457.3513058","url":null,"abstract":"In a Machine Learning (ML) system, characteristics of the ML components create new challenges for software system design and development activities. Data-dependent behavior causes risks in ML systems. Dealing with such risks in the development phase requires non-trivial costs due to un-controllable data generation processes in the test phase. In addition, ML systems often need continuous monitoring and validation in run-time. In this paper, we propose an integrated dependency tracking system that balances the cost and risks in the development stage and operation stage. Our solution uses blockchain (an immutable data store) to track the co-evolution of the models and the corresponding datasets. The provenance of data and models provides a trustworthy trace for dependencies between datasets and models at the development phase, and predictions at the operation phase. A graph database is used to provide visualization and query of the provenance information, and enables explainability for the model-data co-evolution.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133041845","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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