All mainstream languages ship with libraries implementing lists, maps, sets, trees, and other common data structures. These libraries are sufficient for some use cases, but other applications need specialized data structures with different operations. For such applications, the standard libraries are not enough. I propose to develop techniques to automatically synthesize data structure implementations from high-level specifications. My initial results on a large class of collection data structures demonstrate that this is possible and lend hope to the prospect of general data structure synthesis. Synthesized implementations can save programmer time and improve correctness while matching the performance of handwritten code.
{"title":"Data structure synthesis","authors":"Calvin Loncaric","doi":"10.1145/2950290.2983946","DOIUrl":"https://doi.org/10.1145/2950290.2983946","url":null,"abstract":"All mainstream languages ship with libraries implementing lists, maps, sets, trees, and other common data structures. These libraries are sufficient for some use cases, but other applications need specialized data structures with different operations. For such applications, the standard libraries are not enough. I propose to develop techniques to automatically synthesize data structure implementations from high-level specifications. My initial results on a large class of collection data structures demonstrate that this is possible and lend hope to the prospect of general data structure synthesis. Synthesized implementations can save programmer time and improve correctness while matching the performance of handwritten code.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84456427","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}
While adding features, fixing bugs, or refactoring the code, developers may perform repetitive code edits. Although Integrated Development Environments (IDEs) automate some transformations such as renaming, many repetitive edits are performed manually, which is error-prone and time-consuming. To help developers to apply these edits, we propose a technique to perform repetitive edits using examples. The technique receives as input the source code before and after the developer edits some target locations of the change and produces as output the top-ranked program transformation that can be applied to edit the remaining target locations in the codebase. The technique uses a state-of-the-art program synthesis methodology and has three main components: a) a DSL for describing program transformations; b) synthesis algorithms to learn program transformations in this DSL; c) ranking algorithms to select the program transformation with the higher probability of performing the desired repetitive edit. In our preliminary evaluation, in a dataset of 59 repetitive edit cases taken from real C# source code repositories, the technique performed, in 83% of the cases, the intended transformation using only 2.8 examples.
{"title":"Automating repetitive code changes using examples","authors":"Reudismam Rolim","doi":"10.1145/2950290.2983944","DOIUrl":"https://doi.org/10.1145/2950290.2983944","url":null,"abstract":"While adding features, fixing bugs, or refactoring the code, developers may perform repetitive code edits. Although Integrated Development Environments (IDEs) automate some transformations such as renaming, many repetitive edits are performed manually, which is error-prone and time-consuming. To help developers to apply these edits, we propose a technique to perform repetitive edits using examples. The technique receives as input the source code before and after the developer edits some target locations of the change and produces as output the top-ranked program transformation that can be applied to edit the remaining target locations in the codebase. The technique uses a state-of-the-art program synthesis methodology and has three main components: a) a DSL for describing program transformations; b) synthesis algorithms to learn program transformations in this DSL; c) ranking algorithms to select the program transformation with the higher probability of performing the desired repetitive edit. In our preliminary evaluation, in a dataset of 59 repetitive edit cases taken from real C# source code repositories, the technique performed, in 83% of the cases, the intended transformation using only 2.8 examples.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82942519","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}
To customize the behavior of a smart home, an end user writes rules. When an external event satisfies a rule's trigger, the rule's action executes; for example, when the temperature is above a certain threshold, then window awnings might be extended. End users often write incorrect rules. This paper's technique prevents a certain category of errors in the rules: errors due to too few triggers. It statically analyzes a rule's actions to automatically determine a set of necessary and sufficient triggers. We implemented the technique in a tool called TrigGen and tested it on 96 end-user written rules for openHAB, an open-source home automation platform. It identified that 80% of the rules had fewer triggers than required for correct behavior. The missing triggers could lead to unexpected behavior and security vulnerabilities in a smart home.
{"title":"Automatic trigger generation for end user written rules for home automation","authors":"Chandrakana Nandi","doi":"10.1145/2950290.2983965","DOIUrl":"https://doi.org/10.1145/2950290.2983965","url":null,"abstract":"To customize the behavior of a smart home, an end user writes rules. When an external event satisfies a rule's trigger, the rule's action executes; for example, when the temperature is above a certain threshold, then window awnings might be extended. End users often write incorrect rules. This paper's technique prevents a certain category of errors in the rules: errors due to too few triggers. It statically analyzes a rule's actions to automatically determine a set of necessary and sufficient triggers. We implemented the technique in a tool called TrigGen and tested it on 96 end-user written rules for openHAB, an open-source home automation platform. It identified that 80% of the rules had fewer triggers than required for correct behavior. The missing triggers could lead to unexpected behavior and security vulnerabilities in a smart home.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"71 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90718884","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}
Research aimed at understanding and addressing coordination breakdowns experienced in global software development (GSD) projects at Lucent Technologies took a path from open-ended qualitative exploratory studies to quantitative studies with a tight focus on a key problem – delay – and its causes. Rather than being directly associated with delay, multi-site work items involved more people than comparable same-site work items, and the number of people was a powerful predictor of delay. To counteract this, we developed and deployed tools and practices to support more effective communication and expertise location. After conducting two case studies of open source development, an extreme form of GSD, we realized that many tools and practices could be effective for multi-site work, but none seemed to work under all conditions. To achieve deeper insight, we developed and tested our Socio-Technical Theory of Coordination (STTC) in which the dependencies among engineering decisions are seen as defining a constraint satisfaction problem that the organization can solve in a variety of ways. I conclude by explaining how we applied these ideas to transparent development environments, then sketch important open research questions.
{"title":"Building a socio-technical theory of coordination: why and how (outstanding research award)","authors":"J. Herbsleb","doi":"10.1145/2950290.2994160","DOIUrl":"https://doi.org/10.1145/2950290.2994160","url":null,"abstract":"Research aimed at understanding and addressing coordination breakdowns experienced in global software development (GSD) projects at Lucent Technologies took a path from open-ended qualitative exploratory studies to quantitative studies with a tight focus on a key problem – delay – and its causes. Rather than being directly associated with delay, multi-site work items involved more people than comparable same-site work items, and the number of people was a powerful predictor of delay. To counteract this, we developed and deployed tools and practices to support more effective communication and expertise location. After conducting two case studies of open source development, an extreme form of GSD, we realized that many tools and practices could be effective for multi-site work, but none seemed to work under all conditions. To achieve deeper insight, we developed and tested our Socio-Technical Theory of Coordination (STTC) in which the dependencies among engineering decisions are seen as defining a constraint satisfaction problem that the organization can solve in a variety of ways. I conclude by explaining how we applied these ideas to transparent development environments, then sketch important open research questions.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"311 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77390588","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}
Xia Zeng, Dengfeng Li, Wujie Zheng, Fan Xia, Yuetang Deng, Wing Lam, Wei Yang, Tao Xie
Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial practices) outperformed all of the research tools that they studied. In this paper, we present two significant extensions of that study. First, we conduct the first industrial case study of applying Monkey against WeChat, a popular messenger app with over 762 million monthly active users, and report the empirical findings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey, along with empirical insights for future enhancements to both Monkey and our approach.
{"title":"Automated test input generation for Android: are we really there yet in an industrial case?","authors":"Xia Zeng, Dengfeng Li, Wujie Zheng, Fan Xia, Yuetang Deng, Wing Lam, Wei Yang, Tao Xie","doi":"10.1145/2950290.2983958","DOIUrl":"https://doi.org/10.1145/2950290.2983958","url":null,"abstract":"Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question \"Are we there yet?\" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial practices) outperformed all of the research tools that they studied. In this paper, we present two significant extensions of that study. First, we conduct the first industrial case study of applying Monkey against WeChat, a popular messenger app with over 762 million monthly active users, and report the empirical findings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey, along with empirical insights for future enhancements to both Monkey and our approach.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"168 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77937731","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}
Modern Web applications exploit Cloud infrastructures to scale their resources and cope with sudden changes in the workload. While the state of practice is to focus on dynamically adding and removing virtual machines, we advocate that there are strong benefits in containerizing the applications and in scaling the containers. In this paper we present an autoscaling technique that allows containerized applications to scale their resources both at the VM level and at the container level. Furthermore, applications can combine this infrastructural adaptation with platform-level adaptation. The autoscaling is made possible by our planner, which consists of a grey-box discrete-time feedback controller. The work has been validated using two application benchmarks deployed to Amazon EC2. Our experiments show that our planner outperforms Amazon's AutoScaling by 78% on average without containers; and that the introduction of containers allows us to improve by yet another 46% on average.
{"title":"A discrete-time feedback controller for containerized cloud applications","authors":"L. Baresi, Sam Guinea, A. Leva, G. Quattrocchi","doi":"10.1145/2950290.2950328","DOIUrl":"https://doi.org/10.1145/2950290.2950328","url":null,"abstract":"Modern Web applications exploit Cloud infrastructures to scale their resources and cope with sudden changes in the workload. While the state of practice is to focus on dynamically adding and removing virtual machines, we advocate that there are strong benefits in containerizing the applications and in scaling the containers. In this paper we present an autoscaling technique that allows containerized applications to scale their resources both at the VM level and at the container level. Furthermore, applications can combine this infrastructural adaptation with platform-level adaptation. The autoscaling is made possible by our planner, which consists of a grey-box discrete-time feedback controller. The work has been validated using two application benchmarks deployed to Amazon EC2. Our experiments show that our planner outperforms Amazon's AutoScaling by 78% on average without containers; and that the introduction of containers allows us to improve by yet another 46% on average.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88507996","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}
It is well known that verification of safety properties of sequential programs is reducible to satisfiability modulo theory of a first-order logic formula, called a verification condition (VC). The reduction is used both in deductive and automated verification, the difference is only in whether the user or the solver provides candidates for inductive invariants. In this paper, we extend the reduction to parameterized systems consisting of arbitrary many copies of a user-specified process, and whose transition relation is definable in first-order logic modulo theory of linear arithmetic and arrays. We show that deciding whether a parameterized system has a universally quantified inductive invariant is reducible to satisfiability of (non-linear) Constraint Horn Clauses (CHC). As a consequence of our reduction, we obtain a new automated procedure for verifying parameterized systems using existing PDR and CHC engines. While the new procedure is applicable to a wide variety of systems, we show that it is a decision procedure for several decidable fragments.
{"title":"SMT-based verification of parameterized systems","authors":"A. Gurfinkel, Sharon Shoham, Yuri Meshman","doi":"10.1145/2950290.2950330","DOIUrl":"https://doi.org/10.1145/2950290.2950330","url":null,"abstract":"It is well known that verification of safety properties of sequential programs is reducible to satisfiability modulo theory of a first-order logic formula, called a verification condition (VC). The reduction is used both in deductive and automated verification, the difference is only in whether the user or the solver provides candidates for inductive invariants. In this paper, we extend the reduction to parameterized systems consisting of arbitrary many copies of a user-specified process, and whose transition relation is definable in first-order logic modulo theory of linear arithmetic and arrays. We show that deciding whether a parameterized system has a universally quantified inductive invariant is reducible to satisfiability of (non-linear) Constraint Horn Clauses (CHC). As a consequence of our reduction, we obtain a new automated procedure for verifying parameterized systems using existing PDR and CHC engines. While the new procedure is applicable to a wide variety of systems, we show that it is a decision procedure for several decidable fragments.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88608340","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}
Code contributions in Free/Libre and Open Source Software projects are controlled to maintain high-quality of software. Alternatives to patch-based code contribution tools such as mailing lists and issue trackers have been developed with the pull request systems being the most visible and widely available on GitHub. Is the code contribution process more effective with pull request systems? To answer that, we quantify the effectiveness via the rates contributions are accepted and ignored, via the time until the first response and final resolution and via the numbers of contributions. To control for the latent variables, our study includes a project that migrated from an issue tracker to the GitHub pull request system and a comparison between projects using mailing lists and pull request systems. Our results show pull request systems to be associated with reduced review times and larger numbers of contributions. However, not all the comparisons indicate substantially better accept or ignore rates in pull request systems. These variations may be most simply explained by the differences in contribution practices the projects employ and may be less affected by the type of tool. Our results clarify the importance of understanding the role of tools in effective management of the broad network of potential contributors and may lead to strategies and practices making the code contribution more satisfying and efficient from both contributors' and maintainers' perspectives.
{"title":"Effectiveness of code contribution: from patch-based to pull-request-based tools","authors":"Jiaxin Zhu, Minghui Zhou, A. Mockus","doi":"10.1145/2950290.2950364","DOIUrl":"https://doi.org/10.1145/2950290.2950364","url":null,"abstract":"Code contributions in Free/Libre and Open Source Software projects are controlled to maintain high-quality of software. Alternatives to patch-based code contribution tools such as mailing lists and issue trackers have been developed with the pull request systems being the most visible and widely available on GitHub. Is the code contribution process more effective with pull request systems? To answer that, we quantify the effectiveness via the rates contributions are accepted and ignored, via the time until the first response and final resolution and via the numbers of contributions. To control for the latent variables, our study includes a project that migrated from an issue tracker to the GitHub pull request system and a comparison between projects using mailing lists and pull request systems. Our results show pull request systems to be associated with reduced review times and larger numbers of contributions. However, not all the comparisons indicate substantially better accept or ignore rates in pull request systems. These variations may be most simply explained by the differences in contribution practices the projects employ and may be less affected by the type of tool. Our results clarify the importance of understanding the role of tools in effective management of the broad network of potential contributors and may lead to strategies and practices making the code contribution more satisfying and efficient from both contributors' and maintainers' perspectives.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90371378","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}
THANH VAN NGUYEN, Peter C. Rigby, A. Nguyen, Mark Karanfil, T. Nguyen
In this work, we develop T2API, a statistical machine translation-based tool that takes a given English description of a programming task as a query, and synthesizes the API usage template for the task by learning from training data. T2API works in two steps. First, it derives the API elements relevant to the task described in the input by statistically learning from a StackOverflow corpus of text descriptions and corresponding code. To infer those API elements, it also considers the context of the words in the textual input and the context of API elements that often go together in the corpus. The inferred API elements with their relevance scores are ensembled into an API usage by our novel API usage synthesis algorithm that learns the API usages from a large code corpus via a graph-based language model. Importantly, T2API is capable of generating new API usages from smaller, previously-seen usages.
{"title":"T2API: synthesizing API code usage templates from English texts with statistical translation","authors":"THANH VAN NGUYEN, Peter C. Rigby, A. Nguyen, Mark Karanfil, T. Nguyen","doi":"10.1145/2950290.2983931","DOIUrl":"https://doi.org/10.1145/2950290.2983931","url":null,"abstract":"In this work, we develop T2API, a statistical machine translation-based tool that takes a given English description of a programming task as a query, and synthesizes the API usage template for the task by learning from training data. T2API works in two steps. First, it derives the API elements relevant to the task described in the input by statistically learning from a StackOverflow corpus of text descriptions and corresponding code. To infer those API elements, it also considers the context of the words in the textual input and the context of API elements that often go together in the corpus. The inferred API elements with their relevance scores are ensembled into an API usage by our novel API usage synthesis algorithm that learns the API usages from a large code corpus via a graph-based language model. Importantly, T2API is capable of generating new API usages from smaller, previously-seen usages.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89211386","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}
Traditional sensitive data disclosure analysis faces two challenges: to identify sensitive data that is not generated by specific API calls, and to report the potential disclosures when the disclosed data is recognized as sensitive only after the sink operations. We address these issues by developing BidText, a novel static technique to detect sensitive data disclosures. BidText formulates the problem as a type system, in which variables are typed with the text labels that they encounter (e.g., during key-value pair operations). The type system features a novel bi-directional propagation technique that propagates the variable label sets through forward and backward data-flow. A data disclosure is reported if a parameter at a sink point is typed with a sensitive text label. BidText is evaluated on 10,000 Android apps. It reports 4,406 apps that have sensitive data disclosures, with 4,263 apps having log based disclosures and 1,688 having disclosures due to other sinks such as HTTP requests. Existing techniques can only report 64.0% of what BidText reports. And manual inspection shows that the false positive rate for BidText is 10%.
{"title":"Detecting sensitive data disclosure via bi-directional text correlation analysis","authors":"Jianjun Huang, X. Zhang, Lin Tan","doi":"10.1145/2950290.2950348","DOIUrl":"https://doi.org/10.1145/2950290.2950348","url":null,"abstract":"Traditional sensitive data disclosure analysis faces two challenges: to identify sensitive data that is not generated by specific API calls, and to report the potential disclosures when the disclosed data is recognized as sensitive only after the sink operations. We address these issues by developing BidText, a novel static technique to detect sensitive data disclosures. BidText formulates the problem as a type system, in which variables are typed with the text labels that they encounter (e.g., during key-value pair operations). The type system features a novel bi-directional propagation technique that propagates the variable label sets through forward and backward data-flow. A data disclosure is reported if a parameter at a sink point is typed with a sensitive text label. BidText is evaluated on 10,000 Android apps. It reports 4,406 apps that have sensitive data disclosures, with 4,263 apps having log based disclosures and 1,688 having disclosures due to other sinks such as HTTP requests. Existing techniques can only report 64.0% of what BidText reports. And manual inspection shows that the false positive rate for BidText is 10%.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89309288","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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