Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00030
Sara Pérez-Soler, E. Guerra, J. Lara
Chatbots are agents that enable the interaction of users and software by means of written or spoken natural language conversation. Their use is growing, and many companies are starting to offer their services via chatbots, e.g., for booking, shopping or customer support. For this reason, many chatbot development tools have emerged, which makes choosing the most appropriate tool difficult. Moreover, there is hardly any support for migrating chatbots between tools. To alleviate these issues, we propose a model-driven engineering solution that includes: (i) a domain-specific language to model chatbots independently of the development tool; (ii) a recommender that suggests the most suitable development tool for the given chatbot requirements and model; (iii) code generators that synthesize the chatbot code for the selected tool; and (iv) parsers to extract chatbot models out of existing chatbot implementations. Our solution is supported by a web IDE called Conga that can be used for both chatbot creation and migration. A demo video is available at https://youtu.be/3sw1FDdZ7XY.
{"title":"Creating and Migrating Chatbots with Conga","authors":"Sara Pérez-Soler, E. Guerra, J. Lara","doi":"10.1109/ICSE-Companion52605.2021.00030","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00030","url":null,"abstract":"Chatbots are agents that enable the interaction of users and software by means of written or spoken natural language conversation. Their use is growing, and many companies are starting to offer their services via chatbots, e.g., for booking, shopping or customer support. For this reason, many chatbot development tools have emerged, which makes choosing the most appropriate tool difficult. Moreover, there is hardly any support for migrating chatbots between tools. To alleviate these issues, we propose a model-driven engineering solution that includes: (i) a domain-specific language to model chatbots independently of the development tool; (ii) a recommender that suggests the most suitable development tool for the given chatbot requirements and model; (iii) code generators that synthesize the chatbot code for the selected tool; and (iv) parsers to extract chatbot models out of existing chatbot implementations. Our solution is supported by a web IDE called Conga that can be used for both chatbot creation and migration. A demo video is available at https://youtu.be/3sw1FDdZ7XY.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132511098","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 the research work, we have highlighted the importance of regularly updating the software documentation. For this purpose, we analyzed the function documentations indirectly dependent on other functions. This artifact provides scripts to extract the data and the final dataset containing observations obtained on manually annotating the extracted data. The details of this work may be found in the paper appearing in the technical track, titled 'On Indirectly Dependent Documentation in the Context of Code Evolution: A Study'.
{"title":"Dataset to Study Indirectly Dependent Documentation in GitHub Repositories","authors":"Devika Sondhi, Avyakt Gupta, Salil Purandare, A. Rana, Deepanshu Kaushal, Rahul Purandare","doi":"10.1109/ICSE-Companion52605.2021.00096","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00096","url":null,"abstract":"In the research work, we have highlighted the importance of regularly updating the software documentation. For this purpose, we analyzed the function documentations indirectly dependent on other functions. This artifact provides scripts to extract the data and the final dataset containing observations obtained on manually annotating the extracted data. The details of this work may be found in the paper appearing in the technical track, titled 'On Indirectly Dependent Documentation in the Context of Code Evolution: A Study'.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127844380","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00102
An Ju, Hitesh Sajnani, Scot Kelly, Kim Herzig
This repository contains the supplementary material for our paper "A Case Study of Onboarding in Software Teams: Tasks and Strategies" at ICSE 2021. It contains interview guides, surveys, anonymized survey responses, and interview analysis and quotes. We have removed open-ended questions from survey responses to protect participants' privacy. All artifacts are publically available at https://doi.org/10.5281/zenodo.4455936.
{"title":"Research Tools, Survey Responses, and Interview Analysis from a Case Study of Onboarding Software Teams at Microsoft","authors":"An Ju, Hitesh Sajnani, Scot Kelly, Kim Herzig","doi":"10.1109/ICSE-Companion52605.2021.00102","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00102","url":null,"abstract":"This repository contains the supplementary material for our paper \"A Case Study of Onboarding in Software Teams: Tasks and Strategies\" at ICSE 2021. It contains interview guides, surveys, anonymized survey responses, and interview analysis and quotes. We have removed open-ended questions from survey responses to protect participants' privacy. All artifacts are publically available at https://doi.org/10.5281/zenodo.4455936.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122341943","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00071
Norman Peitek, S. Apel, Chris Parnin, A. Brechmann, J. Siegmund
In this artifact, we document our publicly shared data set of our functional magnetic resonance imaging (fMRI) study on programmers. We have conducted an fMRI study with 19 participants observing program comprehension of short code snippets at varying complexity levels. We dissected four classes of code complexity metrics and their relationship to neuronal, behavioral, and subjective correlates of program comprehension. Our data corroborate that complexity metrics can—to a limited degree—explain programmers' cognition in program comprehension. In the paper on the fMRI study, we outline several follow-up experiments investigating fine-grained effects of code complexity and describe possible refinements to code complexity metrics. We view our conducted experiment as a starting point to link code complexity metrics to neural and behavioral correlates. To enable future research to continue this line of work, we aim to provide as much support as possible to conduct similar studies with this artifact.
{"title":"Program Comprehension and Code Complexity Metrics: A Replication Package of an fMRI Study","authors":"Norman Peitek, S. Apel, Chris Parnin, A. Brechmann, J. Siegmund","doi":"10.1109/ICSE-Companion52605.2021.00071","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00071","url":null,"abstract":"In this artifact, we document our publicly shared data set of our functional magnetic resonance imaging (fMRI) study on programmers. We have conducted an fMRI study with 19 participants observing program comprehension of short code snippets at varying complexity levels. We dissected four classes of code complexity metrics and their relationship to neuronal, behavioral, and subjective correlates of program comprehension. Our data corroborate that complexity metrics can—to a limited degree—explain programmers' cognition in program comprehension. In the paper on the fMRI study, we outline several follow-up experiments investigating fine-grained effects of code complexity and describe possible refinements to code complexity metrics. We view our conducted experiment as a starting point to link code complexity metrics to neural and behavioral correlates. To enable future research to continue this line of work, we aim to provide as much support as possible to conduct similar studies with this artifact.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121519580","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00070
Xianhao Jin, Francisco Servant
Continuous integration (CI) is a widely used practice in modern software engineering. Unfortunately, it is also an expensive practice — Google and Mozilla estimate their CI systems in millions of dollars. There are a number of techniques and tools designed to or having the potential to save the cost of CI or expand its benefit - reducing time to feedback. However, their benefits in some dimensions may also result in drawbacks in others. They may also be beneficial in other scenarios where they are not designed to help. Therefore, we built CIBench, a dataset and collection of techniques for build and test selection and prioritization in Continuous Integration. CIBench is based on a popular existing dataset for CI — TravisTorrent [2] and extends it in multiple ways including mining additional Travis logs, Github commits, and building dependency graphs for studied projects. This dataset allows us to replicate and evaluate existing techniques to improve CI under the same settings, to better understand the impact of applying different strategies in a more comprehensive way.
{"title":"CIBench: A Dataset and Collection of Techniques for Build and Test Selection and Prioritization in Continuous Integration","authors":"Xianhao Jin, Francisco Servant","doi":"10.1109/ICSE-Companion52605.2021.00070","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00070","url":null,"abstract":"Continuous integration (CI) is a widely used practice in modern software engineering. Unfortunately, it is also an expensive practice — Google and Mozilla estimate their CI systems in millions of dollars. There are a number of techniques and tools designed to or having the potential to save the cost of CI or expand its benefit - reducing time to feedback. However, their benefits in some dimensions may also result in drawbacks in others. They may also be beneficial in other scenarios where they are not designed to help. Therefore, we built CIBench, a dataset and collection of techniques for build and test selection and prioritization in Continuous Integration. CIBench is based on a popular existing dataset for CI — TravisTorrent [2] and extends it in multiple ways including mining additional Travis logs, Github commits, and building dependency graphs for studied projects. This dataset allows us to replicate and evaluate existing techniques to improve CI under the same settings, to better understand the impact of applying different strategies in a more comprehensive way.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131378552","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00136
S. Maoz, Jan Oliver Ringert
Spectra is a formal specification language specifically tailored for use in the context of reactive synthesis, an automated procedure to obtain a correct-by-construction reactive system from its temporal logic specification. Spectra comes with the Spectra Tools, a set of analyses, including a synthesizer to obtain a correct-by-construction implementation, several means for executing the resulting controller, and additional analyses aimed at helping engineers write higher-quality specifications. This hands-on tutorial will introduce participants to the language and the tool set, using examples and exercises, covering an end-to-end process from specification writing to synthesis to execution. The tutorial may be of interest to software engineers and researchers who are interested in the potential applications of formal methods to software engineering.
{"title":"Reactive Synthesis with Spectra: A Tutorial","authors":"S. Maoz, Jan Oliver Ringert","doi":"10.1109/ICSE-Companion52605.2021.00136","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00136","url":null,"abstract":"Spectra is a formal specification language specifically tailored for use in the context of reactive synthesis, an automated procedure to obtain a correct-by-construction reactive system from its temporal logic specification. Spectra comes with the Spectra Tools, a set of analyses, including a synthesizer to obtain a correct-by-construction implementation, several means for executing the resulting controller, and additional analyses aimed at helping engineers write higher-quality specifications. This hands-on tutorial will introduce participants to the language and the tool set, using examples and exercises, covering an end-to-end process from specification writing to synthesis to execution. The tutorial may be of interest to software engineers and researchers who are interested in the potential applications of formal methods to software engineering.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115578685","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00094
Ze-Yi Zhao, Yanyan Jiang, Chang Xu, Tianxiao Gu, Xiaoxing Ma
Object transformation (upgrading heap objects to their new-version counterparts) is a crucial step in dynamic software update (DSU). However, providing non-trivial object transformers for complex software updates can be difficult for software developers and upgrade maintainers. This paper presents the design and implementation of PASTA, a tool for automatic object transformer synthesis.
{"title":"PASTA: Synthesizing Object State Transformers for Dynamic Software Updates","authors":"Ze-Yi Zhao, Yanyan Jiang, Chang Xu, Tianxiao Gu, Xiaoxing Ma","doi":"10.1109/ICSE-Companion52605.2021.00094","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00094","url":null,"abstract":"Object transformation (upgrading heap objects to their new-version counterparts) is a crucial step in dynamic software update (DSU). However, providing non-trivial object transformers for complex software updates can be difficult for software developers and upgrade maintainers. This paper presents the design and implementation of PASTA, a tool for automatic object transformer synthesis.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122167240","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00052
Jinyan Shao
Object detection is to identify objects from images. In autonomous driving systems, object detection serves as an intermediate module, which is used as the input of autonomous decisions for vehicles. That is, the accuracy of autonomous decisions relies on the object detection. The state-of-the-art object detection modules are designed based on Deep Neural Networks (DNNs). It is difficult to employ white-box testing on DNNs since the output of a single neuron is inexplicable. Existing work conducted metamorphic testing for object detection via image synthesis: the detected object in the original image should be detected in the new synthetic image. However, a synthetic image may not look real from humans' perspective. Even the object detection module fails in detecting such synthetic image, the failure may not reflect the ability of object detection. In this paper, we propose an automatic approach to testing object detection via 3D reconstruction of vehicles in real photos. The 3D reconstruction is developed via vanishing point estimation in photos and heuristic based image insertion. Our approach adds new objects to blank spaces in photos to synthesize images. For example, a new vehicle can be added to a photo of a road and vehicles. In this approach, the output synthetic images are expected to be more natural-looking than randomly synthesizing images. The experiment is conducting on 500 driving photos from the Apollo autonomous driving dataset.
{"title":"Testing Object Detection for Autonomous Driving Systems via 3D Reconstruction","authors":"Jinyan Shao","doi":"10.1109/ICSE-Companion52605.2021.00052","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00052","url":null,"abstract":"Object detection is to identify objects from images. In autonomous driving systems, object detection serves as an intermediate module, which is used as the input of autonomous decisions for vehicles. That is, the accuracy of autonomous decisions relies on the object detection. The state-of-the-art object detection modules are designed based on Deep Neural Networks (DNNs). It is difficult to employ white-box testing on DNNs since the output of a single neuron is inexplicable. Existing work conducted metamorphic testing for object detection via image synthesis: the detected object in the original image should be detected in the new synthetic image. However, a synthetic image may not look real from humans' perspective. Even the object detection module fails in detecting such synthetic image, the failure may not reflect the ability of object detection. In this paper, we propose an automatic approach to testing object detection via 3D reconstruction of vehicles in real photos. The 3D reconstruction is developed via vanishing point estimation in photos and heuristic based image insertion. Our approach adds new objects to blank spaces in photos to synthesize images. For example, a new vehicle can be added to a photo of a road and vehicles. In this approach, the output synthetic images are expected to be more natural-looking than randomly synthesizing images. The experiment is conducting on 500 driving photos from the Apollo autonomous driving dataset.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338866","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00069
Wei Song, Mengqi Han, Jeff Huang
We summarize five anti-patterns of image loading defects in Android apps, including image passing by intent, image decoding without resizing, local image loading without permission, repeated decoding without caching, and image decoding in UI thread. Based on the anti-patterns, we propose a static analyzer, IMGDroid, to automatically and effectively detect such defects. Readers can access our artifacts from GitHub and Zenodo, and can run IMGDroid to detect image loading defects in Android apps; so we are applying for Reusable and Available Badges. We implement IMGDroid in Java, and perform the experiments on a computer with Windows 10, JDK 1.8, and Android 7.1.1. Therefore, reviewers are required to be familiar with Java and proficient in using Eclipse.
{"title":"IMGDroid: A Static Analyzer for Detecting Image Loading Defects in Android Applications","authors":"Wei Song, Mengqi Han, Jeff Huang","doi":"10.1109/ICSE-Companion52605.2021.00069","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00069","url":null,"abstract":"We summarize five anti-patterns of image loading defects in Android apps, including image passing by intent, image decoding without resizing, local image loading without permission, repeated decoding without caching, and image decoding in UI thread. Based on the anti-patterns, we propose a static analyzer, IMGDroid, to automatically and effectively detect such defects. Readers can access our artifacts from GitHub and Zenodo, and can run IMGDroid to detect image loading defects in Android apps; so we are applying for Reusable and Available Badges. We implement IMGDroid in Java, and perform the experiments on a computer with Windows 10, JDK 1.8, and Android 7.1.1. Therefore, reviewers are required to be familiar with Java and proficient in using Eclipse.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128743506","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 : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00098
Rahul Gopinath, Hamed Nemati, A. Zeller
Grammar-based fuzzers are effective and efficient. They can produce an infinite number of syntactically valid test inputs, which can be used to explore the input space without bias. However, it is notoriously difficult to generate focused inputs to induce a specific behavior such as failure without affecting their effectiveness. This is the fuzzer taming problem. In our paper Input Algebras, we show how one can specialize the grammar towards inclusion or exclusion of specific patterns, and their arbitrary boolean combinations. The resulting specialized grammars can be used both for focused fuzzing and also as validators that can indicate the presence or absence of specific behavior-inducing input patterns. In our evaluation of real-world bugs, we show that specialized grammars are accurate both in producing and validating targeted inputs. We also provide a completely worked out Jupyter notebook that explains our algorithms in detail along with a sufficient number of examples. Further, we describe in detail how to replicate our evaluation.
{"title":"Replication Package for Input Algebras","authors":"Rahul Gopinath, Hamed Nemati, A. Zeller","doi":"10.1109/ICSE-Companion52605.2021.00098","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00098","url":null,"abstract":"Grammar-based fuzzers are effective and efficient. They can produce an infinite number of syntactically valid test inputs, which can be used to explore the input space without bias. However, it is notoriously difficult to generate focused inputs to induce a specific behavior such as failure without affecting their effectiveness. This is the fuzzer taming problem. In our paper Input Algebras, we show how one can specialize the grammar towards inclusion or exclusion of specific patterns, and their arbitrary boolean combinations. The resulting specialized grammars can be used both for focused fuzzing and also as validators that can indicate the presence or absence of specific behavior-inducing input patterns. In our evaluation of real-world bugs, we show that specialized grammars are accurate both in producing and validating targeted inputs. We also provide a completely worked out Jupyter notebook that explains our algorithms in detail along with a sufficient number of examples. Further, we describe in detail how to replicate our evaluation.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116922342","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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