Mauricio Soto, Ferdian Thung, Chu-Pan Wong, Claire Le Goues, D. Lo
Many implementations of research techniques that automatically repair software bugs target programs written in C. Work that targets Java often begins from or compares to direct translations of such techniques to a Java context. However, Java and C are very different languages, and Java should be studied to inform the construction of repair approaches to target it. We conduct a large-scale study of bug-fixing commits in Java projects, focusing on assumptions underlying common search-based repair approaches. We make observations that can be leveraged to guide high quality automatic software repair to target Java specifically, including common and uncommon statement modifications in human patches and the applicability of previously-proposed patch construction operators in the Java context.
{"title":"A Deeper Look into Bug Fixes: Patterns, Replacements, Deletions, and Additions","authors":"Mauricio Soto, Ferdian Thung, Chu-Pan Wong, Claire Le Goues, D. Lo","doi":"10.1145/2901739.2903495","DOIUrl":"https://doi.org/10.1145/2901739.2903495","url":null,"abstract":"Many implementations of research techniques that automatically repair software bugs target programs written in C. Work that targets Java often begins from or compares to direct translations of such techniques to a Java context. However, Java and C are very different languages, and Java should be studied to inform the construction of repair approaches to target it. We conduct a large-scale study of bug-fixing commits in Java projects, focusing on assumptions underlying common search-based repair approaches. We make observations that can be leveraged to guide high quality automatic software repair to target Java specifically, including common and uncommon statement modifications in human patches and the applicability of previously-proposed patch construction operators in the Java context.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"73 1","pages":"512-515"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76098354","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}
Sebastian Proksch, Sven Amann, Sarah Nadi, M. Mezini
In this paper, we present a curated collection of 2833 C# solutions taken from Github. We encode the data in a new intermediate representation (IR) that facilitates further analysis by restricting the complexity of the syntax tree and by avoiding implicit information. The dataset is intended as a standardized input for research on recommendation systems for software engineering, but is also useful in many other areas that analyze source code.
{"title":"A Dataset of Simplified Syntax Trees for C#","authors":"Sebastian Proksch, Sven Amann, Sarah Nadi, M. Mezini","doi":"10.1145/2901739.2903507","DOIUrl":"https://doi.org/10.1145/2901739.2903507","url":null,"abstract":"In this paper, we present a curated collection of 2833 C# solutions taken from Github. We encode the data in a new intermediate representation (IR) that facilitates further analysis by restricting the complexity of the syntax tree and by avoiding implicit information. The dataset is intended as a standardized input for research on recommendation systems for software engineering, but is also useful in many other areas that analyze source code.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"30 1","pages":"476-479"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81350029","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}
Infrastructure as Code (IaC) is the practice of specifying computing system configurations through code, and managing them through traditional software engineering methods. The wide adoption of configuration management and increasing size and complexity of the associated code, prompt for assessing, maintaining, and improving the configuration code's quality. In this context, traditional software engineering knowledge and best practices associated with code quality management can be leveraged to assess and manage configuration code quality. We propose a catalog of 13 implementation and 11 design configuration smells, where each smell violates recommended best practices for configuration code. We analyzed 4,621 Puppet repositories containing 8.9 million lines of code and detected the cataloged implementation and design configuration smells. Our analysis reveals that the design configuration smells show 9% higher average co-occurrence among themselves than the implementation configuration smells. We also observed that configuration smells belonging to a smell category tend to co-occur with configuration smells belonging to another smell category when correlation is computed by volume of identified smells. Finally, design configuration smell density shows negative correlation whereas implementation configuration smell density exhibits no correlation with the size of a configuration management system.
{"title":"Does Your Configuration Code Smell?","authors":"Tushar Sharma, Marios Fragkoulis, D. Spinellis","doi":"10.1145/2901739.2901761","DOIUrl":"https://doi.org/10.1145/2901739.2901761","url":null,"abstract":"Infrastructure as Code (IaC) is the practice of specifying computing system configurations through code, and managing them through traditional software engineering methods. The wide adoption of configuration management and increasing size and complexity of the associated code, prompt for assessing, maintaining, and improving the configuration code's quality. In this context, traditional software engineering knowledge and best practices associated with code quality management can be leveraged to assess and manage configuration code quality. We propose a catalog of 13 implementation and 11 design configuration smells, where each smell violates recommended best practices for configuration code. We analyzed 4,621 Puppet repositories containing 8.9 million lines of code and detected the cataloged implementation and design configuration smells. Our analysis reveals that the design configuration smells show 9% higher average co-occurrence among themselves than the implementation configuration smells. We also observed that configuration smells belonging to a smell category tend to co-occur with configuration smells belonging to another smell category when correlation is computed by volume of identified smells. Finally, design configuration smell density shows negative correlation whereas implementation configuration smell density exhibits no correlation with the size of a configuration management system.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"2006 1","pages":"189-200"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82436582","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}
Problem reports at NASA are similar to bug reports: they capture defects found during test, post-launch operational anomalies, and document the investigation and corrective action of the issue. These artifacts are a rich source of lessons learned for NASA, but are expensive to analyze since problem reports are comprised primarily of natural language text. We apply {topic modeling to a corpus of NASA problem reports to extract trends in testing and operational failures. We collected 16,669 problem reports from six NASA space flight missions and applied Latent Dirichlet Allocation topic modeling to the document corpus. We analyze the most popular topics within and across missions, and how popular topics changed over the lifetime of a mission. We find that hardware material and flight software issues are common during the integration and testing phase, while ground station software and equipment issues are more common during the operations phase. We identify a number of challenges in topic modeling for trend analysis: 1) that the process of selecting the topic modeling parameters lacks definitive guidance, 2) defining semantically-meaningful topic labels requires non-trivial effort and domain expertise, 3) topic models derived from the combined corpus of the six missions were biased toward the larger missions, and 4) topics must be semantically distinct as well as cohesive to be useful. Nonetheless, topic modeling can identify problem themes within missions and across mission lifetimes, providing useful feedback to engineers and project managers.
{"title":"Topic Modeling of NASA Space System Problem Reports: Research in Practice","authors":"L. Layman, A. Nikora, Joshua Meek, T. Menzies","doi":"10.1145/2901739.2901760","DOIUrl":"https://doi.org/10.1145/2901739.2901760","url":null,"abstract":"Problem reports at NASA are similar to bug reports: they capture defects found during test, post-launch operational anomalies, and document the investigation and corrective action of the issue. These artifacts are a rich source of lessons learned for NASA, but are expensive to analyze since problem reports are comprised primarily of natural language text. We apply {topic modeling to a corpus of NASA problem reports to extract trends in testing and operational failures. We collected 16,669 problem reports from six NASA space flight missions and applied Latent Dirichlet Allocation topic modeling to the document corpus. We analyze the most popular topics within and across missions, and how popular topics changed over the lifetime of a mission. We find that hardware material and flight software issues are common during the integration and testing phase, while ground station software and equipment issues are more common during the operations phase. We identify a number of challenges in topic modeling for trend analysis: 1) that the process of selecting the topic modeling parameters lacks definitive guidance, 2) defining semantically-meaningful topic labels requires non-trivial effort and domain expertise, 3) topic models derived from the combined corpus of the six missions were biased toward the larger missions, and 4) topics must be semantically distinct as well as cohesive to be useful. Nonetheless, topic modeling can identify problem themes within missions and across mission lifetimes, providing useful feedback to engineers and project managers.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"32 1","pages":"303-314"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84161841","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}
A. Zagalsky, Carlos Gómez Teshima, D. Germán, M. Storey, Germán Poo-Caamaño
One of the many effects of social media in software development is the flourishing of very large communities of practice where members share a common interest, such as programming languages, frameworks, and tools. These communities of practice use many different communication channels but little is known about how these communities create, share, and curate knowledge using such channels. In this paper, we report a qualitative study of how one community of practice—the R software development community—creates and curates knowledge associated with questions and answers (Q&A) in two of its main communication channels: the R-tag in Stack Overflow and the R-users mailing list. The results reveal that knowledge is created and curated in two main forms: participatory, where multiple members explicitly collaborate to build knowledge, and crowdsourced, where individuals work independently of each other. The contribution of this paper is a characterization of knowledge types that are exchanged by these communities of practice, including a description of the reasons why members choose one channel over the other. Finally, this paper enumerates a set of recommendations to assist practitioners in the use of multiple channels for Q&A.
{"title":"How the R Community Creates and Curates Knowledge: A Comparative Study of Stack Overflow and Mailing Lists","authors":"A. Zagalsky, Carlos Gómez Teshima, D. Germán, M. Storey, Germán Poo-Caamaño","doi":"10.1145/2901739.2901772","DOIUrl":"https://doi.org/10.1145/2901739.2901772","url":null,"abstract":"One of the many effects of social media in software development is the flourishing of very large communities of practice where members share a common interest, such as programming languages, frameworks, and tools. These communities of practice use many different communication channels but little is known about how these communities create, share, and curate knowledge using such channels. In this paper, we report a qualitative study of how one community of practice—the R software development community—creates and curates knowledge associated with questions and answers (Q&A) in two of its main communication channels: the R-tag in Stack Overflow and the R-users mailing list. The results reveal that knowledge is created and curated in two main forms: participatory, where multiple members explicitly collaborate to build knowledge, and crowdsourced, where individuals work independently of each other. The contribution of this paper is a characterization of knowledge types that are exchanged by these communities of practice, including a description of the reasons why members choose one channel over the other. Finally, this paper enumerates a set of recommendations to assist practitioners in the use of multiple channels for Q&A.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"8 1","pages":"441-451"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83706004","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}
Developers summarize their changes to code in commit messages.When a message seems “unusual’', however, this puts doubt into the quality of the code contained in the commit. We trained n-gram language models and used cross-entropy as an indicator of commit message “unusualness” of over 120,000 commits from open source projects.Build statuses collected from Travis-CI were used as a proxy for code quality. We then compared the distributions of failed and successful commits with regards to the “unusualness'’ of their commit message. Our analysis yielded significant results when correlating cross-entropy with build status.
{"title":"Judging a Commit by Its Cover: Correlating Commit Message Entropy with Build Status on Travis-CI","authors":"E. Santos, Abram Hindle","doi":"10.1145/2901739.2903493","DOIUrl":"https://doi.org/10.1145/2901739.2903493","url":null,"abstract":"Developers summarize their changes to code in commit messages.When a message seems “unusual’', however, this puts doubt into the quality of the code contained in the commit. We trained n-gram language models and used cross-entropy as an indicator of commit message “unusualness” of over 120,000 commits from open source projects.Build statuses collected from Travis-CI were used as a proxy for code quality. We then compared the distributions of failed and successful commits with regards to the “unusualness'’ of their commit message. Our analysis yielded significant results when correlating cross-entropy with build status.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"14 1","pages":"504-507"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89798849","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}
Exception handling is a powerful tool provided by many pro- gramming languages to help developers deal with unforeseen conditions. Java is one of the few programming languages to enforce an additional compilation check on certain sub- classes of the Exception class through checked exceptions. As part of this study, empirical data was extracted from soft- ware projects developed in Java. The intent is to explore how developers respond to checked exceptions and identify common patterns used by them to deal with exceptions, checked or otherwise. Bloch’s book - “Effective Java” [1] was used as reference for best practices in exception handling - these recommendations were compared against results from the empirical data. Results of this study indicate that most programmers ignore checked exceptions and leave them un- noticed. Additionally, it is observed that classes higher in the exception class hierarchy are more frequently used as compared to specific exception subclasses.
{"title":"Analysis of Exception Handling Patterns in Java Projects: An Empirical Study","authors":"Suman Nakshatri, Maithri Hegde, Sahithi Thandra","doi":"10.1145/2901739.2903499","DOIUrl":"https://doi.org/10.1145/2901739.2903499","url":null,"abstract":"Exception handling is a powerful tool provided by many pro- gramming languages to help developers deal with unforeseen conditions. Java is one of the few programming languages to enforce an additional compilation check on certain sub- classes of the Exception class through checked exceptions. As part of this study, empirical data was extracted from soft- ware projects developed in Java. The intent is to explore how developers respond to checked exceptions and identify common patterns used by them to deal with exceptions, checked or otherwise. Bloch’s book - “Effective Java” [1] was used as reference for best practices in exception handling - these recommendations were compared against results from the empirical data. Results of this study indicate that most programmers ignore checked exceptions and leave them un- noticed. Additionally, it is observed that classes higher in the exception class hierarchy are more frequently used as compared to specific exception subclasses.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"73 1","pages":"500-503"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73345564","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}
Sven Amann, Sarah Nadi, H. Nguyen, T. Nguyen, M. Mezini
Over the last few years, researchers proposed a multitude of automated bug-detection approaches that mine a class of bugs that we call API misuses. Evaluations on a variety of software products show both the omnipresence of such misuses and the ability of the approaches to detect them. This work presents MuBench, a dataset of 89 API misuses that we collected from 33 real-world projects and a survey. With the dataset we empirically analyze the prevalence of API misuses compared to other types of bugs, finding that they are rare, but almost always cause crashes. Furthermore, we discuss how to use it to benchmark and compare API-misuse detectors.
{"title":"MUBench: A Benchmark for API-Misuse Detectors","authors":"Sven Amann, Sarah Nadi, H. Nguyen, T. Nguyen, M. Mezini","doi":"10.1145/2901739.2903506","DOIUrl":"https://doi.org/10.1145/2901739.2903506","url":null,"abstract":"Over the last few years, researchers proposed a multitude of automated bug-detection approaches that mine a class of bugs that we call API misuses. Evaluations on a variety of software products show both the omnipresence of such misuses and the ability of the approaches to detect them. This work presents MuBench, a dataset of 89 API misuses that we collected from 33 real-world projects and a survey. With the dataset we empirically analyze the prevalence of API misuses compared to other types of bugs, finding that they are rare, but almost always cause crashes. Furthermore, we discuss how to use it to benchmark and compare API-misuse detectors.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"8 1","pages":"464-467"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73935076","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}
Fabian Trautsch, S. Herbold, Philip Makedonski, J. Grabowski
Research in software repository mining has grown considerably the last decade. Due to the data-driven nature of this venue of investigation, we identified several problems within the current state-of-the-art that pose a threat to the external validity of results. The heavy re-use of data sets in many studies may invalidate the results in case problems with the data itself are identified. Moreover, for many studies data and/or the implementations are not available, which hinders a replication of the results and, thereby, decreases the comparability between studies. Even if all information about the studies is available, the diversity of the used tooling can make their replication even then very hard. Within this paper, we discuss a potential solution to these problems through a cloud-based platform that integrates data collection and analytics. We created the prototype SmartSHARK that implements our approach. Using SmartSHARK, we collected data from several projects and created different analytic examples. Within this article, we present SmartSHARK and discuss our experiences regarding the use of SmartSHARK and the mentioned problems.
{"title":"Adressing Problems with External Validity of Repository Mining Studies Through a Smart Data Platform","authors":"Fabian Trautsch, S. Herbold, Philip Makedonski, J. Grabowski","doi":"10.1145/2901739.2901753","DOIUrl":"https://doi.org/10.1145/2901739.2901753","url":null,"abstract":"Research in software repository mining has grown considerably the last decade. Due to the data-driven nature of this venue of investigation, we identified several problems within the current state-of-the-art that pose a threat to the external validity of results. The heavy re-use of data sets in many studies may invalidate the results in case problems with the data itself are identified. Moreover, for many studies data and/or the implementations are not available, which hinders a replication of the results and, thereby, decreases the comparability between studies. Even if all information about the studies is available, the diversity of the used tooling can make their replication even then very hard. Within this paper, we discuss a potential solution to these problems through a cloud-based platform that integrates data collection and analytics. We created the prototype SmartSHARK that implements our approach. Using SmartSHARK, we collected data from several projects and created different analytic examples. Within this article, we present SmartSHARK and discuss our experiences regarding the use of SmartSHARK and the mentioned problems.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"36 1","pages":"97-108"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75291273","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}
Many studies analyze issue tracking repositories to understand and support software development. To facilitate the analyses, we share a Mozilla issue tracking dataset covering a 15-year history. The dataset includes three extracts and multiple levels for each extract. The three extracts were retrieved through two channels, a front-end (web user interface (UI)), and a back-end (official database dump) of Mozilla Bugzilla at three different times. The variations (dynamics) among extracts provide space for researchers to reproduce and validate their studies, while revealing potential opportunities for studies that otherwise could not be conducted. We provide different data levels for each extract ranging from raw data to standardized data as well as to the calculated data level for targeting specific research questions. Data retrieving and processing scripts related to each data level are offered too. By employing the multi-level structure, analysts can more efficiently start an inquiry from the standardized level and easily trace the data chain when necessary (e.g., to verify if a phenomenon reflected by the data is an actual event). We applied this dataset to several published studies and intend to expand the multi-level and multi-extract feature to other software engineering datasets.
{"title":"Multi-extract and Multi-level Dataset of Mozilla Issue Tracking History","authors":"Jiaxin Zhu, Minghui Zhou, Hong Mei","doi":"10.1145/2901739.2903502","DOIUrl":"https://doi.org/10.1145/2901739.2903502","url":null,"abstract":"Many studies analyze issue tracking repositories to understand and support software development. To facilitate the analyses, we share a Mozilla issue tracking dataset covering a 15-year history. The dataset includes three extracts and multiple levels for each extract. The three extracts were retrieved through two channels, a front-end (web user interface (UI)), and a back-end (official database dump) of Mozilla Bugzilla at three different times. The variations (dynamics) among extracts provide space for researchers to reproduce and validate their studies, while revealing potential opportunities for studies that otherwise could not be conducted. We provide different data levels for each extract ranging from raw data to standardized data as well as to the calculated data level for targeting specific research questions. Data retrieving and processing scripts related to each data level are offered too. By employing the multi-level structure, analysts can more efficiently start an inquiry from the standardized level and easily trace the data chain when necessary (e.g., to verify if a phenomenon reflected by the data is an actual event). We applied this dataset to several published studies and intend to expand the multi-level and multi-extract feature to other software engineering datasets.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"34 1","pages":"472-475"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72759891","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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