Twitter enables software developers to track users'reactions to newly released systems. Such information, oftenexpressed in the form of raw emotions, can be leveraged to enablea more informed software release process. However, automaticallycapturing and interpreting multi-dimensional structures ofhuman emotions expressed in Twitter messages is not a trivialtask. Challenges stem from the scale of the data available, itsinherently sparse nature, and the high percentage of domainspecificwords. Motivated by these observations, in this paperwe present a preliminary study aimed at detecting, classifying, and interpreting emotions in software users' tweets. A datasetof 1000 tweets sampled from a broad range of software systems'Twitter feeds is used to conduct our analysis. Our results showthat supervised text classifiers (Naive Bayes and Support vectorMachines) are more accurate than general-purpose sentimentanalysis techniques in detecting general and specific emotionsexpressed in software-relevant Tweets.
{"title":"Analyzing, Classifying, and Interpreting Emotions in Software Users' Tweets","authors":"Grant Williams, Anas Mahmoud","doi":"10.1109/SEmotion.2017.1","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.1","url":null,"abstract":"Twitter enables software developers to track users'reactions to newly released systems. Such information, oftenexpressed in the form of raw emotions, can be leveraged to enablea more informed software release process. However, automaticallycapturing and interpreting multi-dimensional structures ofhuman emotions expressed in Twitter messages is not a trivialtask. Challenges stem from the scale of the data available, itsinherently sparse nature, and the high percentage of domainspecificwords. Motivated by these observations, in this paperwe present a preliminary study aimed at detecting, classifying, and interpreting emotions in software users' tweets. A datasetof 1000 tweets sampled from a broad range of software systems'Twitter feeds is used to conduct our analysis. Our results showthat supervised text classifiers (Naive Bayes and Support vectorMachines) are more accurate than general-purpose sentimentanalysis techniques in detecting general and specific emotionsexpressed in software-relevant Tweets.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114352832","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 : 2017-05-20DOI: 10.1109/SEmotion.2017.12
A. Fountaine, Bonita Sharif
Emotions have an effect on developers' progress during software development tasks. The purpose of this position paper is to investigate the effects of emotional awareness, specifically type clarity, on developers' progress. A proposal for this investigation, and a discussion of the current work implicating the effects of emotion in software development, are presented.
{"title":"Emotional Awareness in Software Development: Theory and Measurement","authors":"A. Fountaine, Bonita Sharif","doi":"10.1109/SEmotion.2017.12","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.12","url":null,"abstract":"Emotions have an effect on developers' progress during software development tasks. The purpose of this position paper is to investigate the effects of emotional awareness, specifically type clarity, on developers' progress. A proposal for this investigation, and a discussion of the current work implicating the effects of emotion in software development, are presented.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332348","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}
We propose an affective API designed with a focus on agile development. The OpenAffect API enables different types of applications to produce and consume affective measurements. At the workshop, we will present the details of the REST API and illustrate its use with a demonstration.
{"title":"OpenAffect API: A Proposal for Enabling an Ecosystem of Emotion Awareness Tools","authors":"O. Liechti, R. Reis","doi":"10.1109/SEmotion.2017.9","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.9","url":null,"abstract":"We propose an affective API designed with a focus on agile development. The OpenAffect API enables different types of applications to produce and consume affective measurements. At the workshop, we will present the details of the REST API and illustrate its use with a demonstration.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122720600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional self-adaptive systems research has focused on external contextual aspects such as performance, system reaction to environment. In this paper, we introduce the idea of measuring emotions in order to empower the adaptability of software services at runtime. We present two type of monitoring mechanisms and an adaptive adaptation strategy, which were implemented as part of the HAPPYNESS middleware. A preliminary test using data from the Empatica repository was carried out with the purpose of assessing the goodness of the controller (i.e. inference engine), a component of our middleware. The obtained results were consistent with the expected values. Moreover, we test also the connectivity and synchronization between E4-Wristband and an adaptive mobile application that were used by two volunteer users.
{"title":"Using Emotions to Empower the Self-Adaptation Capability of Software Services","authors":"Nelly Condori-Fernández, Franci Suni Lopez","doi":"10.1109/SEmotion.2017.8","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.8","url":null,"abstract":"Traditional self-adaptive systems research has focused on external contextual aspects such as performance, system reaction to environment. In this paper, we introduce the idea of measuring emotions in order to empower the adaptability of software services at runtime. We present two type of monitoring mechanisms and an adaptive adaptation strategy, which were implemented as part of the HAPPYNESS middleware. A preliminary test using data from the Empatica repository was carried out with the purpose of assessing the goodness of the controller (i.e. inference engine), a component of our middleware. The obtained results were consistent with the expected values. Moreover, we test also the connectivity and synchronization between E4-Wristband and an adaptive mobile application that were used by two volunteer users.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130530532","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 : 2017-05-20DOI: 10.1109/SEMOTION.2017...2
Saurabh Sarkar, Chris Parnin
Mental fatigue reduces one's cognitive and physical abilities. In tasks requiring continuous attention, such as driving, fatigue is a well-known risk. However, when fatigued during daily tasks, such as programming, the nature of risk is more diffuse and accumulative, yet the consequences can be just as severe (e.g. defects in autopilot software). Identifying risks of fatigue in the context of programming can lead to interventions that prevent introduction of defects and introduce coping mechanisms. To character and predict these risks, we conducted two studies: a survey study in which we asked 311 software developers to rate the severity and frequency of their fatigue and to recall a recent experience of being fatigued while programming, and an observational study with 9 professional software developers to investigate the feasibility of predicting fatigue from interaction history. From the survey, we found that a majority of developers report severe (66%) and frequent (59%) issues with fatigue. Further, we categorized their experiences into seven effects on programming tasks, which include reduced motivation and reduced ability to handle tasks involving large mental workloads. From our observational study, our results found how several measures, such as focus duration, key press time, error rates, and increases in software quality warnings, may be applied for detecting fatigue levels. Together, these results aims to support developers and the industry for improving software quality and work conditions for software developers.
{"title":"Characterizing and Predicting Mental Fatigue during Programming Tasks","authors":"Saurabh Sarkar, Chris Parnin","doi":"10.1109/SEMOTION.2017...2","DOIUrl":"https://doi.org/10.1109/SEMOTION.2017...2","url":null,"abstract":"Mental fatigue reduces one's cognitive and physical abilities. In tasks requiring continuous attention, such as driving, fatigue is a well-known risk. However, when fatigued during daily tasks, such as programming, the nature of risk is more diffuse and accumulative, yet the consequences can be just as severe (e.g. defects in autopilot software). Identifying risks of fatigue in the context of programming can lead to interventions that prevent introduction of defects and introduce coping mechanisms. To character and predict these risks, we conducted two studies: a survey study in which we asked 311 software developers to rate the severity and frequency of their fatigue and to recall a recent experience of being fatigued while programming, and an observational study with 9 professional software developers to investigate the feasibility of predicting fatigue from interaction history. From the survey, we found that a majority of developers report severe (66%) and frequent (59%) issues with fatigue. Further, we categorized their experiences into seven effects on programming tasks, which include reduced motivation and reduced ability to handle tasks involving large mental workloads. From our observational study, our results found how several measures, such as focus duration, key press time, error rates, and increases in software quality warnings, may be applied for detecting fatigue levels. Together, these results aims to support developers and the industry for improving software quality and work conditions for software developers.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122327735","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}
Socio-emotional content is vital for building trusting, productive relationships that go beyond task-oriented communication in teams. But for distributed collaborators, it is challenging to communicate emotional status because of working over a distance. We propose to use non-work-related, non-competitive, and playful drawing online to encourage nonverbal expressions of emotions and interactions. We describe our research and preliminary results from a recent study where we demonstrated that it was effective to use freeform drawing to help distributed teams to enhance individual positive emotions. We also outline future research that could shed light on design opportunities for supporting affective expressions and sharing in distributed software engineering teams.
{"title":"Using Playful Drawing to Support Affective Expressions and Sharing in Distributed Teams","authors":"Mengyao Zhao, Yi Wang, D. Redmiles","doi":"10.1109/SEmotion.2017.3","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.3","url":null,"abstract":"Socio-emotional content is vital for building trusting, productive relationships that go beyond task-oriented communication in teams. But for distributed collaborators, it is challenging to communicate emotional status because of working over a distance. We propose to use non-work-related, non-competitive, and playful drawing online to encourage nonverbal expressions of emotions and interactions. We describe our research and preliminary results from a recent study where we demonstrated that it was effective to use freeform drawing to help distributed teams to enhance individual positive emotions. We also outline future research that could shed light on design opportunities for supporting affective expressions and sharing in distributed software engineering teams.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133757200","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}
This position paper describes an approach to create a framework for modeling affect in decision making for agile processes, and a procedure to test its use by applying it to the introduction of Multi Criteria Decision Methods, and in particular the Best Worst Method, into agile development. We believe that affect changes development in a significant way especially for agile development, which requires close collaboration. We postulate that the structuring of a decision will engage a larger audience eliciting full participation by actors like the customer who are not necessarily accustomed to a development environment. Further, we believe that the structuring of the decision process and the involvement of a larger pool of actors will reduce negative affect, thus enabling a faster, better empowering set of decisions that ultimately will result in higher quality software products and a lower development time.
{"title":"Toward a Model of Emotion Influences on Agile Decision Making","authors":"Abdulaziz Alhubaishy, L. Benedicenti","doi":"10.1109/SEmotion.2017.7","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.7","url":null,"abstract":"This position paper describes an approach to create a framework for modeling affect in decision making for agile processes, and a procedure to test its use by applying it to the introduction of Multi Criteria Decision Methods, and in particular the Best Worst Method, into agile development. We believe that affect changes development in a significant way especially for agile development, which requires close collaboration. We postulate that the structuring of a decision will engage a larger audience eliciting full participation by actors like the customer who are not necessarily accustomed to a development environment. Further, we believe that the structuring of the decision process and the involvement of a larger pool of actors will reduce negative affect, thus enabling a faster, better empowering set of decisions that ultimately will result in higher quality software products and a lower development time.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123153276","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 : 2017-05-20DOI: 10.1109/SEmotion.2017.10
Marco Ortu, Giuseppe Destefanis, S. Counsell, M. Marchesi, R. Tonelli
The summary presented in this paper highlights the results obtained in a four-years project aiming at analyzing the development process of software artifacts from two points of view: Effectiveness and Affectiveness. The first attribute is meant to analyze the productivity of the Open Source Communities by measuring the time required to resolve an issue, while the latter provides a novel approach for studying the development process by analyzing the affectiveness expressed by developers in their comments posted during the issue resolution phase. Affectivenes is obtained by measuring Sentiment, Politeness and Emotions.
{"title":"Connecting the Dots: Measuring Effectiveness and Affectiveness in Software Systems","authors":"Marco Ortu, Giuseppe Destefanis, S. Counsell, M. Marchesi, R. Tonelli","doi":"10.1109/SEmotion.2017.10","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.10","url":null,"abstract":"The summary presented in this paper highlights the results obtained in a four-years project aiming at analyzing the development process of software artifacts from two points of view: Effectiveness and Affectiveness. The first attribute is meant to analyze the productivity of the Open Source Communities by measuring the time required to resolve an issue, while the latter provides a novel approach for studying the development process by analyzing the affectiveness expressed by developers in their comments posted during the issue resolution phase. Affectivenes is obtained by measuring Sentiment, Politeness and Emotions.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115636265","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 : 2017-03-13DOI: 10.1109/SEmotion.2017.11
Miikka Kuutila, M. Mäntylä, Maëlick Claes, M. Elovainio
During the past few years, psychological diseases related to unhealthy work environments, such as burnouts, have drawn more and more public attention. One of the known causes of these affective problems is time pressure. In order to form a theoretical background for time pressure detection in software repositories, this paper combines interdisciplinary knowledge by analyzing 1270 papers found on Scopus database and containing terms related to time pressure. By clustering those papers based on their abstract, we show that time pressure has been widely studied across different fields, but relatively little in software engineering. From a literature review of the most relevant papers, we infer a list of testable hypotheses that we want to verify in future studies in order to assess the impact of time pressures on software developers' mental health.
{"title":"Reviewing Literature on Time Pressure in Software Engineering and Related Professions: Computer Assisted Interdisciplinary Literature Review","authors":"Miikka Kuutila, M. Mäntylä, Maëlick Claes, M. Elovainio","doi":"10.1109/SEmotion.2017.11","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.11","url":null,"abstract":"During the past few years, psychological diseases related to unhealthy work environments, such as burnouts, have drawn more and more public attention. One of the known causes of these affective problems is time pressure. In order to form a theoretical background for time pressure detection in software repositories, this paper combines interdisciplinary knowledge by analyzing 1270 papers found on Scopus database and containing terms related to time pressure. By clustering those papers based on their abstract, we show that time pressure has been widely studied across different fields, but relatively little in software engineering. From a literature review of the most relevant papers, we infer a list of testable hypotheses that we want to verify in future studies in order to assess the impact of time pressures on software developers' mental health.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131741713","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}
App store analysis has become an important discipline in recent software engineering research. It empirically studies apps using information mined from their distribution platforms. Information provided by users, such as app reviews, are of high interest to developers. Commercial providers such as App Annie analyzing this information became an important source for companies developing and marketing mobile apps. In this paper, we perform an exploratory study, which analyzes over seven million reviews from the Apple AppStore regarding their emotional sentiment. Since recent research in this field used sentiments to detail and refine their results, we aim to gain deeper insights into the nature of sentiments in user reviews. In this study we try to evaluate whether or not the emotional sentiment can be an informative feature for software engineers, as well as pitfalls of its usage. We present our initial results and discuss how they can be interpreted from the software engineering perspective.
{"title":"On the Emotion of Users in App Reviews","authors":"Daniel Martens, Timo Johann","doi":"10.1109/SEmotion.2017.6","DOIUrl":"https://doi.org/10.1109/SEmotion.2017.6","url":null,"abstract":"App store analysis has become an important discipline in recent software engineering research. It empirically studies apps using information mined from their distribution platforms. Information provided by users, such as app reviews, are of high interest to developers. Commercial providers such as App Annie analyzing this information became an important source for companies developing and marketing mobile apps. In this paper, we perform an exploratory study, which analyzes over seven million reviews from the Apple AppStore regarding their emotional sentiment. Since recent research in this field used sentiments to detail and refine their results, we aim to gain deeper insights into the nature of sentiments in user reviews. In this study we try to evaluate whether or not the emotional sentiment can be an informative feature for software engineers, as well as pitfalls of its usage. We present our initial results and discuss how they can be interpreted from the software engineering perspective.","PeriodicalId":202796,"journal":{"name":"2017 IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering (SEmotion)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166414","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：