Program Committee Members Hung-Chang Hsiao, National Cheng Kung University Gabor Kecskemeti, MTA SZTAKI Chandra Sekaran, NITK Xiaofei Wang, Seoul National University Craig Lee, The Aerospace Corporation Francisco Brasileiro, UFCG Saeid Abolfazli, University of Malaya Syed Ali Haider, SEECS-NUST Sheng Di, INRIA Vineet Chadha, Nvelo R& D, Samsung Josef Spillner, TU Dresden Eduardo Jacob, University of the Basque Country Gilles Fedak, INRIA, University of Lyon Ramin Yahyapour, GWDG University of Göttingen Erwin Laure, KTH/PDC Jiannong Cao, Hong Kong Polytechnic University Hong Xu, City University of Hong Kong Hemant Mehta, Senior Member, IEEE Xingwei Wang, Northeastern University Chunming Hu, Beihang University Alexandru Iosup, Delft University of Technology Annette Bieniusa, University of Kaiserslautern Thomas Bauschert, TU Chemnitz Pietro Michiardi, Eurecom Annappa B, NITK Tyng-Yeu Liang, National Kaohsiung University of Applied Sciences Oliver Hohlfeld, RWTH Aachen University Bo Yang, University of Electronic Science and Technology of China Jakub Szefer, Yale University David Hausheer, TU Darmstadt Rajesh Ingle, IEEE Pune Section Wolfgang Kellerer, Technische Universität München Tamas Lukovszki, Eötvös Loránd University, Budapest Burkhard Stiller, University of Zurich
项目委员会成员肖洪昌、国立成功大学Gabor keskemeti、MTA SZTAKI Chandra Sekaran、NITK Xiaofei Wang、首尔国立大学Craig Lee、航空航天公司Francisco Brasileiro、UFCG Saeid Abolfazli、马来西亚大学Syed Ali Haider、SEECS-NUST Sheng Di、INRIA Vineet Chadha、Nvelo r&d、Samsung Josef Spillner、TU Dresden Eduardo Jacob、巴斯克地区大学Gilles Fedak、INRIA、里昂大学Ramin Yahyapour、GWDG大学Göttingen Erwin Laure, KTH/PDC曹建农，香港理工大学徐宏，香港城市大学Hemant Mehta，资深会员，IEEE王兴伟，东北大学胡春明，北京航空航天大学Alexandru Iosup, Delft工业大学Annette Bieniusa，凯泽斯劳滕大学Thomas Bauschert, TU Chemnitz Pietro Michiardi, Eurecom Annappa B, NITK ting - yeu Liang，国立高雄应用科技大学Oliver Hohlfeld，亚琛工业大学杨博，中国电子科技大学Jakub Szefer，耶鲁大学David Hausheer，德国达姆施塔特工业大学Rajesh Ingle, IEEE浦那组Wolfgang Kellerer, Technische Universität m nchen Tamas Lukovszki, Eötvös Loránd布达佩斯大学Burkhard Stiller，苏黎世大学
{"title":"Program Committee Members","authors":"S. Gopalakrishnan","doi":"10.1109/GCCW.2006.74","DOIUrl":"https://doi.org/10.1109/GCCW.2006.74","url":null,"abstract":"Program Committee Members Hung-Chang Hsiao, National Cheng Kung University Gabor Kecskemeti, MTA SZTAKI Chandra Sekaran, NITK Xiaofei Wang, Seoul National University Craig Lee, The Aerospace Corporation Francisco Brasileiro, UFCG Saeid Abolfazli, University of Malaya Syed Ali Haider, SEECS-NUST Sheng Di, INRIA Vineet Chadha, Nvelo R& D, Samsung Josef Spillner, TU Dresden Eduardo Jacob, University of the Basque Country Gilles Fedak, INRIA, University of Lyon Ramin Yahyapour, GWDG University of Göttingen Erwin Laure, KTH/PDC Jiannong Cao, Hong Kong Polytechnic University Hong Xu, City University of Hong Kong Hemant Mehta, Senior Member, IEEE Xingwei Wang, Northeastern University Chunming Hu, Beihang University Alexandru Iosup, Delft University of Technology Annette Bieniusa, University of Kaiserslautern Thomas Bauschert, TU Chemnitz Pietro Michiardi, Eurecom Annappa B, NITK Tyng-Yeu Liang, National Kaohsiung University of Applied Sciences Oliver Hohlfeld, RWTH Aachen University Bo Yang, University of Electronic Science and Technology of China Jakub Szefer, Yale University David Hausheer, TU Darmstadt Rajesh Ingle, IEEE Pune Section Wolfgang Kellerer, Technische Universität München Tamas Lukovszki, Eötvös Loránd University, Budapest Burkhard Stiller, University of Zurich","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116473874","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/SEmotion52567.2021.00010
Angela Mayhua-Quispe, Franci Suni Lopez, Nelly Condori-Fernández, Maria Fernanda Granda
Emotion research in the area of software engineering has gained significant attention. Mostly this research has been focused on understanding the role of emotions in software programming carried out within collaborative software development environments. With the purpose of providing more evidence on emotion research in the early stages of the software life cycle, in this paper, we report the results of a live study conducted in competitive conditions. The main objective of the study is to analyze the emotions expressed by competitors when performing verification tasks with the support of CoSTest, a model-driven testing tool. Our results show that participants tend to experience more positive emotions (e.g., attentive, alert, active) than negative emotions (upset, hostile, afraid) when verification tasks are performed in an online contest.
{"title":"Analyzing Emotions in Conceptual Models Verification Tasks performed in Online Contests","authors":"Angela Mayhua-Quispe, Franci Suni Lopez, Nelly Condori-Fernández, Maria Fernanda Granda","doi":"10.1109/SEmotion52567.2021.00010","DOIUrl":"https://doi.org/10.1109/SEmotion52567.2021.00010","url":null,"abstract":"Emotion research in the area of software engineering has gained significant attention. Mostly this research has been focused on understanding the role of emotions in software programming carried out within collaborative software development environments. With the purpose of providing more evidence on emotion research in the early stages of the software life cycle, in this paper, we report the results of a live study conducted in competitive conditions. The main objective of the study is to analyze the emotions expressed by competitors when performing verification tasks with the support of CoSTest, a model-driven testing tool. Our results show that participants tend to experience more positive emotions (e.g., attentive, alert, active) than negative emotions (upset, hostile, afraid) when verification tasks are performed in an online contest.","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"41 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":"115178968","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/SEmotion52567.2021.00011
Alex Cummaudo, Ulrike M. Graetsch, M. Curumsing, Rajesh Vasa, Scott Barnett, J. Grundy
Software developers are increasingly using cloud-based services that provide machine learning capabilities to implement ‘intelligent’ features. Studies show that incorporating machine learning into an application increases technical debt, creates data dependencies, and introduces uncertainty due to their non-deterministic behaviour. We know very little about the emotional state of software developers who have to deal with such issues; and the impacts on productivity. This paper presents a preliminary effort to better understand the emotions of developers when experiencing issues with these services with the wider goal of discovering potential service improvements. We conducted a landscape analysis of emotions found in 1,425 Stack Overflow questions about a specific and mature subset of these cloud-based services, namely those that provide computer vision techniques. To speed up the emotion identification process, we trialled an automatic approach using a pre-trained emotion classifier that was specifically trained on Stack Overflow content, EmoTxt, and manually verified its classification results. We found that the identified emotions vary for different types of questions, and a discrepancy exists between automatic and manual emotion analysis due to subjectivity.
{"title":"Emotions in Computer Vision Service Q&A","authors":"Alex Cummaudo, Ulrike M. Graetsch, M. Curumsing, Rajesh Vasa, Scott Barnett, J. Grundy","doi":"10.1109/SEmotion52567.2021.00011","DOIUrl":"https://doi.org/10.1109/SEmotion52567.2021.00011","url":null,"abstract":"Software developers are increasingly using cloud-based services that provide machine learning capabilities to implement ‘intelligent’ features. Studies show that incorporating machine learning into an application increases technical debt, creates data dependencies, and introduces uncertainty due to their non-deterministic behaviour. We know very little about the emotional state of software developers who have to deal with such issues; and the impacts on productivity. This paper presents a preliminary effort to better understand the emotions of developers when experiencing issues with these services with the wider goal of discovering potential service improvements. We conducted a landscape analysis of emotions found in 1,425 Stack Overflow questions about a specific and mature subset of these cloud-based services, namely those that provide computer vision techniques. To speed up the emotion identification process, we trialled an automatic approach using a pre-trained emotion classifier that was specifically trained on Stack Overflow content, EmoTxt, and manually verified its classification results. We found that the identified emotions vary for different types of questions, and a discrepancy exists between automatic and manual emotion analysis due to subjectivity.","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"43 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":"121748597","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/SEmotion52567.2021.00012
Niloofar Mansoor, Cole S. Peterson, Bonita Sharif
The paper presents results from a pilot questionnaire-based study on ten Stack Overflow (SO) questions. Eleven developers were tasked with determining if the SO question sentiment was positive, negative or neutral. The results from the questionnaire indicate that developers mostly rated the sentiment of SO questions as neutral, stating that they received little or no emotional feedback from the questions. Tools that were designed to analyze Software Engineering related texts (SentiStrength-SE, SentiCR, and Senti4SD) were on average more closely aligned with developer ratings for a majority of the questions than general purpose tools for detecting SO question sentiment. We discuss cases where tools and developer sentiment differ along with implications of the results. Overall, the sentiment tool output on the question title and body is more aligned with the developer rating than just the title alone. Since SO is a very common medium of technical exchange, we also report that adding code snippets, short titles, and multiple tags were top three features developers prefer in SO questions in order for it to be answered quickly.
{"title":"How Developers and Tools Categorize Sentiment in Stack Overflow Questions - A Pilot Study","authors":"Niloofar Mansoor, Cole S. Peterson, Bonita Sharif","doi":"10.1109/SEmotion52567.2021.00012","DOIUrl":"https://doi.org/10.1109/SEmotion52567.2021.00012","url":null,"abstract":"The paper presents results from a pilot questionnaire-based study on ten Stack Overflow (SO) questions. Eleven developers were tasked with determining if the SO question sentiment was positive, negative or neutral. The results from the questionnaire indicate that developers mostly rated the sentiment of SO questions as neutral, stating that they received little or no emotional feedback from the questions. Tools that were designed to analyze Software Engineering related texts (SentiStrength-SE, SentiCR, and Senti4SD) were on average more closely aligned with developer ratings for a majority of the questions than general purpose tools for detecting SO question sentiment. We discuss cases where tools and developer sentiment differ along with implications of the results. Overall, the sentiment tool output on the question title and body is more aligned with the developer rating than just the title alone. Since SO is a very common medium of technical exchange, we also report that adding code snippets, short titles, and multiple tags were top three features developers prefer in SO questions in order for it to be answered quickly.","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","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":"120986871","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/SEmotion52567.2021.00009
Kang-il Park, Bonita Sharif
The paper presents an eye tracking pilot study on understanding how developers read and assess sentiment in twenty-four GitHub pull requests containing emoji randomly selected from five different open source applications. Gaze data was collected on various elements of the pull request page in Google Chrome while the developers were tasked with determining perceived sentiment. The developer perceived sentiment was compared with sentiment output from five state-of-the-art sentiment analysis tools. SentiStrength-SE had the highest performance, with 55.56% of its predictions being agreed upon by study participants. On the other hand, Stanford CoreNLP fared the worst, with only 5.56% of its predictions matching that of the participants’. Gaze data shows the top three areas that developers looked at the most were the comment body, added lines of code, and username (the person writing the comment). The results also show high attention given to emoji in the pull request comment body compared to the rest of the comment text. These results can help provide additional guidelines on the pull request review process.
{"title":"Assessing Perceived Sentiment in Pull Requests with Emoji: Evidence from Tools and Developer Eye Movements","authors":"Kang-il Park, Bonita Sharif","doi":"10.1109/SEmotion52567.2021.00009","DOIUrl":"https://doi.org/10.1109/SEmotion52567.2021.00009","url":null,"abstract":"The paper presents an eye tracking pilot study on understanding how developers read and assess sentiment in twenty-four GitHub pull requests containing emoji randomly selected from five different open source applications. Gaze data was collected on various elements of the pull request page in Google Chrome while the developers were tasked with determining perceived sentiment. The developer perceived sentiment was compared with sentiment output from five state-of-the-art sentiment analysis tools. SentiStrength-SE had the highest performance, with 55.56% of its predictions being agreed upon by study participants. On the other hand, Stanford CoreNLP fared the worst, with only 5.56% of its predictions matching that of the participants’. Gaze data shows the top three areas that developers looked at the most were the comment body, added lines of code, and username (the person writing the comment). The results also show high attention given to emoji in the pull request comment body compared to the rest of the comment text. These results can help provide additional guidelines on the pull request review process.","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"72 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":"122287603","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 : 1900-01-01DOI: 10.1109/semotion52567.2021.00005
{"title":"Welcome from the Workshop Organizers","authors":"","doi":"10.1109/semotion52567.2021.00005","DOIUrl":"https://doi.org/10.1109/semotion52567.2021.00005","url":null,"abstract":"","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132407586","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 : 1900-01-01DOI: 10.1109/semotion52567.2021.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/semotion52567.2021.00003","DOIUrl":"https://doi.org/10.1109/semotion52567.2021.00003","url":null,"abstract":"","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125535444","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 : 1900-01-01DOI: 10.1109/semotion52567.2021.00001
{"title":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering","authors":"","doi":"10.1109/semotion52567.2021.00001","DOIUrl":"https://doi.org/10.1109/semotion52567.2021.00001","url":null,"abstract":"","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126848268","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 : 1900-01-01DOI: 10.1109/semotion52567.2021.00002
{"title":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering SEmotion 2021","authors":"","doi":"10.1109/semotion52567.2021.00002","DOIUrl":"https://doi.org/10.1109/semotion52567.2021.00002","url":null,"abstract":"","PeriodicalId":432937,"journal":{"name":"2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126768830","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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