Pub Date : 2021-06-01DOI: 10.1109/BotSE52550.2021.00014
Jordi Cabot, L. Burgueño, R. Clarisó, Gwendal Daniel, Jorge Perianez-Pascual, Roberto Rodríguez-Echeverría
The popularity of bots is on the rise, with many bots able to interact with users via a chat or voice interface thanks to the embedding of a Natural Language Processing (NLP) component. Still, companies often express concerns about the quality of such bots, as their malfunctioning could have a severe impact on the company revenue or image. Unfortunately, the field of testing NLP-intensive bots is still in its infancy. This paper aims to characterize the testing properties and techniques (and their adaptation) relevant to this type of bots. We believe this will be helpful as a reference framework to compare and evaluate future bot testing research initiatives.
{"title":"Testing challenges for NLP-intensive bots","authors":"Jordi Cabot, L. Burgueño, R. Clarisó, Gwendal Daniel, Jorge Perianez-Pascual, Roberto Rodríguez-Echeverría","doi":"10.1109/BotSE52550.2021.00014","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00014","url":null,"abstract":"The popularity of bots is on the rise, with many bots able to interact with users via a chat or voice interface thanks to the embedding of a Natural Language Processing (NLP) component. Still, companies often express concerns about the quality of such bots, as their malfunctioning could have a severe impact on the company revenue or image. Unfortunately, the field of testing NLP-intensive bots is still in its infancy. This paper aims to characterize the testing properties and techniques (and their adaptation) relevant to this type of bots. We believe this will be helpful as a reference framework to compare and evaluate future bot testing research initiatives.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124049770","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 : 2021-06-01DOI: 10.1109/BotSE52550.2021.00013
Dragos Serban, Bart Golsteijn, Ralph Holdorp, Alexander Serebrenik
In this experience report we present SAW-BOT, a bot proposing fixes for static analysis warnings. The bot has been evaluated with five professional software developers by means of a Wizard of Oz experiment, semi-structured interviews and the mTAM questionnaire. We have observed that developers prefer GitHub suggestions to two baseline operation modes. Our study indicates that GitHub suggestions are a viable mechanism for implementing bots proposing fixes for static analysis warnings.
{"title":"SAW-BOT: Proposing Fixes for Static Analysis Warnings with GitHub Suggestions","authors":"Dragos Serban, Bart Golsteijn, Ralph Holdorp, Alexander Serebrenik","doi":"10.1109/BotSE52550.2021.00013","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00013","url":null,"abstract":"In this experience report we present SAW-BOT, a bot proposing fixes for static analysis warnings. The bot has been evaluated with five professional software developers by means of a Wizard of Oz experiment, semi-structured interviews and the mTAM questionnaire. We have observed that developers prefer GitHub suggestions to two baseline operation modes. Our study indicates that GitHub suggestions are a viable mechanism for implementing bots proposing fixes for static analysis warnings.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129351473","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 : 2021-06-01DOI: 10.1109/botse52550.2021.00005
{"title":"Message from the BotSE 2021 Organizers","authors":"","doi":"10.1109/botse52550.2021.00005","DOIUrl":"https://doi.org/10.1109/botse52550.2021.00005","url":null,"abstract":"","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465427","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 : 2021-03-17DOI: 10.1109/BotSE52550.2021.00010
Chris Brown, Chris Parnin
Student experiences in large undergraduate Computer Science courses are increasingly impacted by automated systems. Bots, or agents of software automation, are useful for efficiently grading and generating feedback. Current efforts at automation in CS education focus on supporting instructional tasks, but do not address student struggles due to poor behaviors, such as procrastination. In this paper, we explore using bots to improve the software engineering behaviors of students using developer recommendation choice architectures, a framework incorporating behavioral science concepts in recommendations to improve the actions of programmers. We implemented this framework in class-bot, a novel system designed to nudge students to make better choices while working on programming assignments. This work presents a preliminary evaluation integrating this tool in an introductory programming course. Our results show that class-bot is beneficial for improving student development behaviors increasing code quality and productivity.
{"title":"Nudging Students Toward Better Software Engineering Behaviors","authors":"Chris Brown, Chris Parnin","doi":"10.1109/BotSE52550.2021.00010","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00010","url":null,"abstract":"Student experiences in large undergraduate Computer Science courses are increasingly impacted by automated systems. Bots, or agents of software automation, are useful for efficiently grading and generating feedback. Current efforts at automation in CS education focus on supporting instructional tasks, but do not address student struggles due to poor behaviors, such as procrastination. In this paper, we explore using bots to improve the software engineering behaviors of students using developer recommendation choice architectures, a framework incorporating behavioral science concepts in recommendations to improve the actions of programmers. We implemented this framework in class-bot, a novel system designed to nudge students to make better choices while working on programming assignments. This work presents a preliminary evaluation integrating this tool in an introductory programming course. Our results show that class-bot is beneficial for improving student development behaviors increasing code quality and productivity.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127562864","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 : 2021-03-17DOI: 10.1109/BotSE52550.2021.00016
Liliane do Nascimento Vale, M. Maia
Question answering platforms, such as Stack Overflow, have impacted substantially how developers search for solutions for their programming problems. The crowd knowledge content available from such platforms has also been used to leverage software development tools. The recent advances on Natural Language Processing, specifically on more powerful language models, have demonstrated ability to enhance text understanding and generation. In this context, we aim at investigating the factors that can influence on the application of such models for understanding source code related data and produce more interactive and intelligent assistants for software development. In this preliminary study, we particularly investigate if a how-to question filter and the level of context in the question may impact the results of a question answering transformer-based model. We suggest that fine-tuning models with corpus based on how-to questions can impact positively in the model and more contextualized questions also induce more objective answers.
{"title":"Towards a question answering assistant for software development using a transformer-based language model","authors":"Liliane do Nascimento Vale, M. Maia","doi":"10.1109/BotSE52550.2021.00016","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00016","url":null,"abstract":"Question answering platforms, such as Stack Overflow, have impacted substantially how developers search for solutions for their programming problems. The crowd knowledge content available from such platforms has also been used to leverage software development tools. The recent advances on Natural Language Processing, specifically on more powerful language models, have demonstrated ability to enhance text understanding and generation. In this context, we aim at investigating the factors that can influence on the application of such models for understanding source code related data and produce more interactive and intelligent assistants for software development. In this preliminary study, we particularly investigate if a how-to question filter and the level of context in the question may impact the results of a question answering transformer-based model. We suggest that fine-tuning models with corpus based on how-to questions can impact positively in the model and more contextualized questions also induce more objective answers.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"470 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115850650","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 : 2021-03-16DOI: 10.1109/BotSE52550.2021.00008
Samaneh Saadat, Natalia Colmenares, G. Sukthankar
The ever-increasing complexity of modern software engineering projects makes the usage of automated assistants imperative. Bots can be used to complete repetitive tasks during development and testing, as well as promoting communication between team members through issue reporting and documentation. Although the ultimate aim of these automated assistants is to speed taskwork completion, their inclusion into GitHub repositories may affect teamwork as well. This paper studies the question of how bots modify the team workflow. We examined the event sequences of repositories with bots and without bots using a contrast motif discovery method to detect subsequences that are more prevalent in one set of event sequences vs. the other. Our study reveals that teams with bots are more likely to intersperse comments throughout their coding activities, while not actually being more prolific commenters.
{"title":"Do Bots Modify the Workflow of GitHub Teams?","authors":"Samaneh Saadat, Natalia Colmenares, G. Sukthankar","doi":"10.1109/BotSE52550.2021.00008","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00008","url":null,"abstract":"The ever-increasing complexity of modern software engineering projects makes the usage of automated assistants imperative. Bots can be used to complete repetitive tasks during development and testing, as well as promoting communication between team members through issue reporting and documentation. Although the ultimate aim of these automated assistants is to speed taskwork completion, their inclusion into GitHub repositories may affect teamwork as well. This paper studies the question of how bots modify the team workflow. We examined the event sequences of repositories with bots and without bots using a contrast motif discovery method to detect subsequences that are more prevalent in one set of event sequences vs. the other. Our study reveals that teams with bots are more likely to intersperse comments throughout their coding activities, while not actually being more prolific commenters.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114571428","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 : 2021-03-16DOI: 10.1109/BotSE52550.2021.00015
Ilham A. Qasse, Shailesh Mishra, Mohammad Hamdaqa
Recently, Blockchain technology adoption has expanded to many application areas due to the evolution of smart contracts. However, developing smart contracts is non-trivial and challenging due to the lack of tools and expertise in this field. A promising solution to overcome this issue is to use Model-Driven Engineering (MDE), however, using models still involves a learning curve and might not be suitable for non-technical users. To tackle this challenge, chatbot or conversational interfaces can be used to assess the non-technical users to specify a smart contract in gradual and interactive manner. In this paper, we propose iContractBot, a chatbot for modeling and developing smart contracts. Moreover, we investigate how to integrate iContractBot with iContractML, a domainspecific modeling language for developing smart contracts, and instantiate intention models from the chatbot. The iContractBot framework provides a domain-specific language (DSL) based on the user intention and performs model-to-text transformation to generate the smart contract code. A smart contract use case is presented to demonstrate how iContractBot can be utilized for creating models and generating the deployment artifacts for smart contracts based on a simple conversation.
{"title":"iContractBot: A Chatbot for Smart Contracts’ Specification and Code Generation","authors":"Ilham A. Qasse, Shailesh Mishra, Mohammad Hamdaqa","doi":"10.1109/BotSE52550.2021.00015","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00015","url":null,"abstract":"Recently, Blockchain technology adoption has expanded to many application areas due to the evolution of smart contracts. However, developing smart contracts is non-trivial and challenging due to the lack of tools and expertise in this field. A promising solution to overcome this issue is to use Model-Driven Engineering (MDE), however, using models still involves a learning curve and might not be suitable for non-technical users. To tackle this challenge, chatbot or conversational interfaces can be used to assess the non-technical users to specify a smart contract in gradual and interactive manner. In this paper, we propose iContractBot, a chatbot for modeling and developing smart contracts. Moreover, we investigate how to integrate iContractBot with iContractML, a domainspecific modeling language for developing smart contracts, and instantiate intention models from the chatbot. The iContractBot framework provides a domain-specific language (DSL) based on the user intention and performs model-to-text transformation to generate the smart contract code. A smart contract use case is presented to demonstrate how iContractBot can be utilized for creating models and generating the deployment artifacts for smart contracts based on a simple conversation.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134461815","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 : 2021-03-10DOI: 10.1109/BotSE52550.2021.00012
M. Golzadeh, Alexandre Decan, Eleni Constantinou, T. Mens
Development bots are used on Github to automate repetitive activities. Such bots communicate with human actors via issue comments and pull request comments. Identifying such bot comments allows to prevent bias in socio-technical studies related to software development. To automate their identification, we propose a classification model based on natural language processing. Starting from a balanced ground-truth dataset of 19,282 PR and issue comments, we encode the comments as vectors using a combination of the bag of words and TF-IDF techniques. We train a range of binary classifiers to predict the type of comment (human or bot) based on this vector representation. A multinomial Naive Bayes classifier provides the best results. Its performance on a test set containing 50% of the data achieves an average precision, recall, and F1 score of 0.88. Although the model shows a promising result on the pull request and issue comments, further work is required to generalize the model on other types of activities, like commit messages and code reviews.
{"title":"Identifying bot activity in GitHub pull request and issue comments","authors":"M. Golzadeh, Alexandre Decan, Eleni Constantinou, T. Mens","doi":"10.1109/BotSE52550.2021.00012","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00012","url":null,"abstract":"Development bots are used on Github to automate repetitive activities. Such bots communicate with human actors via issue comments and pull request comments. Identifying such bot comments allows to prevent bias in socio-technical studies related to software development. To automate their identification, we propose a classification model based on natural language processing. Starting from a balanced ground-truth dataset of 19,282 PR and issue comments, we encode the comments as vectors using a combination of the bag of words and TF-IDF techniques. We train a range of binary classifiers to predict the type of comment (human or bot) based on this vector representation. A multinomial Naive Bayes classifier provides the best results. Its performance on a test set containing 50% of the data achieves an average precision, recall, and F1 score of 0.88. Although the model shows a promising result on the pull request and issue comments, further work is required to generalize the model on other types of activities, like commit messages and code reviews.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126652694","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 : 2021-03-10DOI: 10.1109/BotSE52550.2021.00011
A. Basu, Kunal Banerjee
The tracking and timely resolution of service requests is one of the major challenges in agile project management. Having an efficient solution to this problem is a key requirement for Walmart to facilitate seamless collaboration across its different business units. The Jira software is one of the popular choices in industries for monitoring such service requests. A service request once logged into the system by a reporter is referred to as a (Jira) ticket which is assigned to an engineer for servicing. In this work, we explore how the tickets which may arise in any of the Walmart stores and offices distributed over several countries can be assigned to engineers efficiently. Specifically, we will discuss how the introduction of a bot for automated ticket assignment has helped in reducing the disparity in ticket assignment to engineers by human managers and also decreased the average ticket resolution time– thereby improving the experience for both the reporters and the engineers. Additionally, the bot sends reminders and status updates over different business communication platforms for timely tracking of tickets; it can be suitably modified to provision for human intervention in case of special needs by some teams. The current study conducted over data collected from various teams within Walmart shows the efficacy of our bot.
{"title":"Designing a Bot for Efficient Distribution of Service Requests","authors":"A. Basu, Kunal Banerjee","doi":"10.1109/BotSE52550.2021.00011","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00011","url":null,"abstract":"The tracking and timely resolution of service requests is one of the major challenges in agile project management. Having an efficient solution to this problem is a key requirement for Walmart to facilitate seamless collaboration across its different business units. The Jira software is one of the popular choices in industries for monitoring such service requests. A service request once logged into the system by a reporter is referred to as a (Jira) ticket which is assigned to an engineer for servicing. In this work, we explore how the tickets which may arise in any of the Walmart stores and offices distributed over several countries can be assigned to engineers efficiently. Specifically, we will discuss how the introduction of a bot for automated ticket assignment has helped in reducing the disparity in ticket assignment to engineers by human managers and also decreased the average ticket resolution time– thereby improving the experience for both the reporters and the engineers. Additionally, the bot sends reminders and status updates over different business communication platforms for timely tracking of tickets; it can be suitably modified to provision for human intervention in case of special needs by some teams. The current study conducted over data collected from various teams within Walmart shows the efficacy of our bot.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126632713","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 : 2021-03-05DOI: 10.1109/BotSE52550.2021.00009
Marvin Wyrich, Raoul Ghit, T. Haller, Christiana Müller
As a maintainer of an open source software project, you are usually happy about contributions in the form of pull requests that bring the project a step forward. Past studies have shown that when reviewing a pull request, not only its content is taken into account, but also, for example, the social characteristics of the contributor. Whether a contribution is accepted and how long this takes therefore depends not only on the content of the contribution. What we only have indications for so far, however, is that pull requests from bots may be prioritized lower, even if the bots are explicitly deployed by the development team and are considered useful. One goal of the bot research and development community is to design helpful bots to effectively support software development in a variety of ways. To get closer to this goal, in this GitHub mining study, we examine the measurable differences in how maintainers interact with manually created pull requests from humans compared to those created automatically by bots. About one third of all pull requests on GitHub currently come from bots. While pull requests from humans are accepted and merged in 72.53% of all cases, this applies to only 37.38% of bot pull requests. Furthermore, it takes significantly longer for a bot pull request to be interacted with and for it to be merged, even though they contain fewer changes on average than human pull requests. These results suggest that bots have yet to realize their full potential.
{"title":"Bots Don’t Mind Waiting, Do They? Comparing the Interaction With Automatically and Manually Created Pull Requests","authors":"Marvin Wyrich, Raoul Ghit, T. Haller, Christiana Müller","doi":"10.1109/BotSE52550.2021.00009","DOIUrl":"https://doi.org/10.1109/BotSE52550.2021.00009","url":null,"abstract":"As a maintainer of an open source software project, you are usually happy about contributions in the form of pull requests that bring the project a step forward. Past studies have shown that when reviewing a pull request, not only its content is taken into account, but also, for example, the social characteristics of the contributor. Whether a contribution is accepted and how long this takes therefore depends not only on the content of the contribution. What we only have indications for so far, however, is that pull requests from bots may be prioritized lower, even if the bots are explicitly deployed by the development team and are considered useful. One goal of the bot research and development community is to design helpful bots to effectively support software development in a variety of ways. To get closer to this goal, in this GitHub mining study, we examine the measurable differences in how maintainers interact with manually created pull requests from humans compared to those created automatically by bots. About one third of all pull requests on GitHub currently come from bots. While pull requests from humans are accepted and merged in 72.53% of all cases, this applies to only 37.38% of bot pull requests. Furthermore, it takes significantly longer for a bot pull request to be interacted with and for it to be merged, even though they contain fewer changes on average than human pull requests. These results suggest that bots have yet to realize their full potential.","PeriodicalId":339364,"journal":{"name":"2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE)","volume":"67 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116129381","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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