Application frameworks are a powerful technique for large-scale reuse but often very hard to learn from scratch. Although good documentation helps on reducing the learning curve, it is often found lacking, and costly, as it needs to attend different audiences with disparate learning needs. When code and documentation prove insufficient, developers turn to their network of experts. The lack of awareness about the experts, interrupting the wrong people, and experts unavailability are well known hindrances to effective collaboration. This paper presents the DRIVER platform, a collaborative learning environment for framework users to share their knowledge. It provides the documentation on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended, thus tapping into the collective knowledge of the community of framework users. The tool can be obtained at http://bit.ly/driverTool.
{"title":"DRIVER -- A Platform for Collaborative Framework Understanding","authors":"Nuno H. Flores, Ademar Aguiar","doi":"10.1109/ASE.2015.105","DOIUrl":"https://doi.org/10.1109/ASE.2015.105","url":null,"abstract":"Application frameworks are a powerful technique for large-scale reuse but often very hard to learn from scratch. Although good documentation helps on reducing the learning curve, it is often found lacking, and costly, as it needs to attend different audiences with disparate learning needs. When code and documentation prove insufficient, developers turn to their network of experts. The lack of awareness about the experts, interrupting the wrong people, and experts unavailability are well known hindrances to effective collaboration. This paper presents the DRIVER platform, a collaborative learning environment for framework users to share their knowledge. It provides the documentation on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended, thus tapping into the collective knowledge of the community of framework users. The tool can be obtained at http://bit.ly/driverTool.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"24 1","pages":"783-788"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74039343","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}
Ziyi Lin, D. Marinov, Hao Zhong, Yuting Chen, Jianjun Zhao
Researchers have proposed various approaches to detect concurrency bugs and improve multi-threaded programs, but performing evaluations of the effectiveness of these approaches still remains a substantial challenge. We survey the existing evaluations and find out that they often use code or bugs not representative of real world. To improve representativeness, we have prepared JaConTeBe, a benchmark suite of 47 confirmed concurrency bugs from 8 popular open-source projects, supplemented with test cases for reproducing buggy behaviors. Running three approaches on JaConTeBe shows that our benchmark suite confirms some limitations of the three approaches. We submitted JaConTeBe to the SIR repository (a software-artifact repository for rigorous controlled experiments), and it was included as a part of SIR.
{"title":"JaConTeBe: A Benchmark Suite of Real-World Java Concurrency Bugs (T)","authors":"Ziyi Lin, D. Marinov, Hao Zhong, Yuting Chen, Jianjun Zhao","doi":"10.1109/ASE.2015.87","DOIUrl":"https://doi.org/10.1109/ASE.2015.87","url":null,"abstract":"Researchers have proposed various approaches to detect concurrency bugs and improve multi-threaded programs, but performing evaluations of the effectiveness of these approaches still remains a substantial challenge. We survey the existing evaluations and find out that they often use code or bugs not representative of real world. To improve representativeness, we have prepared JaConTeBe, a benchmark suite of 47 confirmed concurrency bugs from 8 popular open-source projects, supplemented with test cases for reproducing buggy behaviors. Running three approaches on JaConTeBe shows that our benchmark suite confirms some limitations of the three approaches. We submitted JaConTeBe to the SIR repository (a software-artifact repository for rigorous controlled experiments), and it was included as a part of SIR.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"15 1","pages":"178-189"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75285261","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}
Atrisha Sarkar, Jianmei Guo, Norbert Siegmund, S. Apel, K. Czarnecki
A key challenge of the development and maintenanceof configurable systems is to predict the performance ofindividual system variants based on the features selected. It isusually infeasible to measure the performance of all possible variants, due to feature combinatorics. Previous approaches predictperformance based on small samples of measured variants, butit is still open how to dynamically determine an ideal samplethat balances prediction accuracy and measurement effort. Inthis paper, we adapt two widely-used sampling strategies forperformance prediction to the domain of configurable systemsand evaluate them in terms of sampling cost, which considersprediction accuracy and measurement effort simultaneously. Togenerate an initial sample, we introduce a new heuristic based onfeature frequencies and compare it to a traditional method basedon t-way feature coverage. We conduct experiments on six realworldsystems and provide guidelines for stakeholders to predictperformance by sampling.
{"title":"Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T)","authors":"Atrisha Sarkar, Jianmei Guo, Norbert Siegmund, S. Apel, K. Czarnecki","doi":"10.1109/ASE.2015.45","DOIUrl":"https://doi.org/10.1109/ASE.2015.45","url":null,"abstract":"A key challenge of the development and maintenanceof configurable systems is to predict the performance ofindividual system variants based on the features selected. It isusually infeasible to measure the performance of all possible variants, due to feature combinatorics. Previous approaches predictperformance based on small samples of measured variants, butit is still open how to dynamically determine an ideal samplethat balances prediction accuracy and measurement effort. Inthis paper, we adapt two widely-used sampling strategies forperformance prediction to the domain of configurable systemsand evaluate them in terms of sampling cost, which considersprediction accuracy and measurement effort simultaneously. Togenerate an initial sample, we introduce a new heuristic based onfeature frequencies and compare it to a traditional method basedon t-way feature coverage. We conduct experiments on six realworldsystems and provide guidelines for stakeholders to predictperformance by sampling.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"15 1","pages":"342-352"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72712270","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 systems are often developed and released without formal specifications. For those systems that are formally specified, developers have to continuously maintain and update the specifications or have them fall out of date. To deal with the absence of formal specifications, researchers have proposed techniques to infer the missing specifications of an implementation in a variety of forms, such as finite state automaton (FSA). Despite the progress in this area, the efficacy of the proposed specification miners needs to improve if these miners are to be adopted. We propose SpecForge, a new specification mining approach that synergizes many existing specification miners. SpecForge decomposes FSAs that are inferred by existing miners into simple constraints, through a process we refer to as model fission. It then filters the outlier constraints and fuses the constraints back together into a single FSA (i.e., model fusion). We have evaluated SpecForge on execution traces of 10 programs, which includes 5 programs from DaCapo benchmark, to infer behavioral models of 13 library classes. Our results show that SpecForge achieves an average precision, recall and F-measure of 90.57%, 54.58%, and 64.21% respectively. SpecForge outperforms the best performing baseline by 13.75% in terms of F-measure.
{"title":"Synergizing Specification Miners through Model Fissions and Fusions (T)","authors":"Tien-Duy B. Le, X. Le, D. Lo, Ivan Beschastnikh","doi":"10.1109/ASE.2015.83","DOIUrl":"https://doi.org/10.1109/ASE.2015.83","url":null,"abstract":"Software systems are often developed and released without formal specifications. For those systems that are formally specified, developers have to continuously maintain and update the specifications or have them fall out of date. To deal with the absence of formal specifications, researchers have proposed techniques to infer the missing specifications of an implementation in a variety of forms, such as finite state automaton (FSA). Despite the progress in this area, the efficacy of the proposed specification miners needs to improve if these miners are to be adopted. We propose SpecForge, a new specification mining approach that synergizes many existing specification miners. SpecForge decomposes FSAs that are inferred by existing miners into simple constraints, through a process we refer to as model fission. It then filters the outlier constraints and fuses the constraints back together into a single FSA (i.e., model fusion). We have evaluated SpecForge on execution traces of 10 programs, which includes 5 programs from DaCapo benchmark, to infer behavioral models of 13 library classes. Our results show that SpecForge achieves an average precision, recall and F-measure of 90.57%, 54.58%, and 64.21% respectively. SpecForge outperforms the best performing baseline by 13.75% in terms of F-measure.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"92 1","pages":"115-125"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76983017","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}
Running compute-intensive or blocking I/O operationsin the UI event thread of smartphone apps can severelydegrade responsiveness. Despite the fact that Android provides several async constructs that developers can use, developers can still miss opportunities to encapsulate long-running operations in async constructs. On the other hand, they can use the inappropriate async constructs, which result in memory leaks, lost results, and wasted energy. Fortunately, refactoring tools can eliminate these problems by retrofitting asynchrony to sequential code and transforming async code to use the appropriate constructs. This demo presents two refactoring tools for Android apps: (i) ASYNCHRONIZER, a refactoring tool that enables developers to extract long-running operations into Android AsyncTask. (ii) ASYNCDROID, a refactoring tool which enables developers to transform existing improperly-used AsyncTask into Android IntentService.
{"title":"Refactorings for Android Asynchronous Programming","authors":"Yu Lin, Danny Dig","doi":"10.1109/ASE.2015.100","DOIUrl":"https://doi.org/10.1109/ASE.2015.100","url":null,"abstract":"Running compute-intensive or blocking I/O operationsin the UI event thread of smartphone apps can severelydegrade responsiveness. Despite the fact that Android provides several async constructs that developers can use, developers can still miss opportunities to encapsulate long-running operations in async constructs. On the other hand, they can use the inappropriate async constructs, which result in memory leaks, lost results, and wasted energy. Fortunately, refactoring tools can eliminate these problems by retrofitting asynchrony to sequential code and transforming async code to use the appropriate constructs. This demo presents two refactoring tools for Android apps: (i) ASYNCHRONIZER, a refactoring tool that enables developers to extract long-running operations into Android AsyncTask. (ii) ASYNCDROID, a refactoring tool which enables developers to transform existing improperly-used AsyncTask into Android IntentService.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"234 1","pages":"836-841"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72846332","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 this paper we introduce a dynamic GUI test generator that incorporates ant colony optimization. We created two ant systems for generating tests. Our first ant system implements the normal ant colony optimization algorithm in order to traverse the GUI and find good event sequences. Our second ant system, called AntQ, implements the antq algorithm that incorporates Q-Learning, which is a behavioral reinforcement learning technique. Both systems use the same fitness function in order to determine good paths through the GUI. Our fitness function looks at the amount of change in the GUI state that each event causes. Events that have a larger impact on the GUI state will be favored in future tests. We compared our two ant systems to random selection. We ran experiments on six subject applications and report on the code coverage and fault finding abilities of all three algorithms.
{"title":"Dynamically Testing GUIs Using Ant Colony Optimization (T)","authors":"Santo Carino, J. Andrews","doi":"10.1109/ASE.2015.70","DOIUrl":"https://doi.org/10.1109/ASE.2015.70","url":null,"abstract":"In this paper we introduce a dynamic GUI test generator that incorporates ant colony optimization. We created two ant systems for generating tests. Our first ant system implements the normal ant colony optimization algorithm in order to traverse the GUI and find good event sequences. Our second ant system, called AntQ, implements the antq algorithm that incorporates Q-Learning, which is a behavioral reinforcement learning technique. Both systems use the same fitness function in order to determine good paths through the GUI. Our fitness function looks at the amount of change in the GUI state that each event causes. Events that have a larger impact on the GUI state will be favored in future tests. We compared our two ant systems to random selection. We ran experiments on six subject applications and report on the code coverage and fault finding abilities of all three algorithms.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"333 1","pages":"138-148"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76592998","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}
Cyrille Artho, M. Seidl, Quentin Gros, Eun-Hye Choi, Takashi Kitamura, A. Mori, R. Ramler, Yoriyuki Yamagata
Modbat makes testing easier by providing a user-friendly modeling language to describe the behavior of systems, from such a model, test cases are generated and executed. Modbat's domain-specific language is based on Scala, its features include probabilistic and non-deterministic transitions, component models with inheritance, and exceptions. We demonstrate the versatility of Modbat by finding a confirmed defect in the currently latest version of Java, and by testing SAT solvers.
{"title":"Model-Based Testing of Stateful APIs with Modbat","authors":"Cyrille Artho, M. Seidl, Quentin Gros, Eun-Hye Choi, Takashi Kitamura, A. Mori, R. Ramler, Yoriyuki Yamagata","doi":"10.1109/ASE.2015.95","DOIUrl":"https://doi.org/10.1109/ASE.2015.95","url":null,"abstract":"Modbat makes testing easier by providing a user-friendly modeling language to describe the behavior of systems, from such a model, test cases are generated and executed. Modbat's domain-specific language is based on Scala, its features include probabilistic and non-deterministic transitions, component models with inheritance, and exceptions. We demonstrate the versatility of Modbat by finding a confirmed defect in the currently latest version of Java, and by testing SAT solvers.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"858-863"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81768358","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}
Programming today regularly involves searching for source code online, whether through a general search engine such as Google or a specialized code search engine such as SearchCode, Ohloh, or GitHub. Searching typically is an iterative process, with develop-ers adjusting the keywords they use based on the results of the previous query. However, searching in this manner is not ideal, because just using keywords places limits on what developers can express as well as the overall interaction that is required. Based on the observation that the results from one query create a con-text in which a next is formulated, we present CodeExchange, a new code search engine that we developed to explicitly leverage this context to support fluid, expressive reformulation of queries. We motivate the need for CodeExchange, highlight its key design decisions and overall architecture, and evaluate its use in both a field deployment and a laboratory study.
{"title":"CodeExchange: Supporting Reformulation of Internet-Scale Code Queries in Context (T)","authors":"Lee Martie, Thomas D. Latoza, A. Hoek","doi":"10.1109/ASE.2015.51","DOIUrl":"https://doi.org/10.1109/ASE.2015.51","url":null,"abstract":"Programming today regularly involves searching for source code online, whether through a general search engine such as Google or a specialized code search engine such as SearchCode, Ohloh, or GitHub. Searching typically is an iterative process, with develop-ers adjusting the keywords they use based on the results of the previous query. However, searching in this manner is not ideal, because just using keywords places limits on what developers can express as well as the overall interaction that is required. Based on the observation that the results from one query create a con-text in which a next is formulated, we present CodeExchange, a new code search engine that we developed to explicitly leverage this context to support fluid, expressive reformulation of queries. We motivate the need for CodeExchange, highlight its key design decisions and overall architecture, and evaluate its use in both a field deployment and a laboratory study.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"15 1","pages":"24-35"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78793063","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}
James R. Wilcox, P. Finch, C. Flanagan, Stephen N. Freund
Precise dynamic race detectors incur significant time and space overheads, particularly for array-intensive programs, due to the need to store and manipulate analysis (or shadow) state for every element of every array. This paper presents SlimState, a precise dynamic race detector that uses an adaptive, online algorithm to optimize array shadow state representations. SlimState is based on the insight that common array access patterns lead to analogous patterns in array shadow state, enabling optimized, space efficient representations of array shadow state with no loss in precision. We have implemented SlimState for Java. Experiments on a variety of benchmarks show that array shadow compression reduces the space and time overhead of race detection by 27% and 9%, respectively. It is particularly effective for array-intensive programs, reducing space and time overheads by 35% and 17%, respectively, on these programs.
{"title":"Array Shadow State Compression for Precise Dynamic Race Detection (T)","authors":"James R. Wilcox, P. Finch, C. Flanagan, Stephen N. Freund","doi":"10.1109/ASE.2015.19","DOIUrl":"https://doi.org/10.1109/ASE.2015.19","url":null,"abstract":"Precise dynamic race detectors incur significant time and space overheads, particularly for array-intensive programs, due to the need to store and manipulate analysis (or shadow) state for every element of every array. This paper presents SlimState, a precise dynamic race detector that uses an adaptive, online algorithm to optimize array shadow state representations. SlimState is based on the insight that common array access patterns lead to analogous patterns in array shadow state, enabling optimized, space efficient representations of array shadow state with no loss in precision. We have implemented SlimState for Java. Experiments on a variety of benchmarks show that array shadow compression reduces the space and time overhead of race detection by 27% and 9%, respectively. It is particularly effective for array-intensive programs, reducing space and time overheads by 35% and 17%, respectively, on these programs.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"51 1","pages":"155-165"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89205434","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}
Dongjiang You, Sanjai Rayadurgam, M. Heimdahl, John Komp, Baekgyu Kim, O. Sokolsky
Model-based testing of embedded real-time systems is challenging because platform-specific details are often abstracted away to make the models amenable to various analyses. Testing an implementation to expose non-conformance to such a model requires reconciling differences arising from these abstractions. Due to stateful behavior, naive comparisons of model and system behaviors often fail causing numerous false positives. Previously proposed approaches address this by being reactively permissive: passing criteria are relaxed to reduce false positives, but may increase false negatives, which is particularly bothersome for safety-critical systems. To address this concern, we propose an automated approach that is proactively adaptive: test stimuli and system responses are suitably modified taking into account platform-specific aspects so that the modified test when executed on the platform-specific implementation exercises the intended scenario captured in the original model-based test. We show that the new framework eliminates false negatives while keeping the number of false positives low for a variety of platform-specific configurations.
{"title":"Executing Model-Based Tests on Platform-Specific Implementations (T)","authors":"Dongjiang You, Sanjai Rayadurgam, M. Heimdahl, John Komp, Baekgyu Kim, O. Sokolsky","doi":"10.1109/ASE.2015.64","DOIUrl":"https://doi.org/10.1109/ASE.2015.64","url":null,"abstract":"Model-based testing of embedded real-time systems is challenging because platform-specific details are often abstracted away to make the models amenable to various analyses. Testing an implementation to expose non-conformance to such a model requires reconciling differences arising from these abstractions. Due to stateful behavior, naive comparisons of model and system behaviors often fail causing numerous false positives. Previously proposed approaches address this by being reactively permissive: passing criteria are relaxed to reduce false positives, but may increase false negatives, which is particularly bothersome for safety-critical systems. To address this concern, we propose an automated approach that is proactively adaptive: test stimuli and system responses are suitably modified taking into account platform-specific aspects so that the modified test when executed on the platform-specific implementation exercises the intended scenario captured in the original model-based test. We show that the new framework eliminates false negatives while keeping the number of false positives low for a variety of platform-specific configurations.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"34 2 1","pages":"418-428"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83441576","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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