Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops最新文献_第9页

Does Neuron Coverage Matter for Deep Reinforcement Learning?: A Preliminary Study 神经元覆盖对深度强化学习有影响吗?:初步研究

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391462

Miller Trujillo, M. Linares-Vásquez, Camilo Escobar-Velásquez, Ivana Dusparic, Nicolás Cardozo

引用次数: 15

Metamorphic Robustness Testing of Google Translate Google翻译的变形鲁棒性测试

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391484

Dickson T. S. Lee, Z. Zhou, T. H. Tse

引用次数: 6

Sorry to Bother You Again: Developer Recommendation Choice Architectures for Designing Effective Bots 很抱歉再次打扰你:开发者推荐设计有效机器人的选择架构

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391506

Chris Brown, Chris Parnin

引用次数: 9

Automatic Improvement of Machine Translation Using Mutamorphic Relation: Invited Talk Paper 利用变形关系自动改进机器翻译:特邀报告

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391541

Jie M. Zhang

引用次数: 0

Dialogue Act Classification for Virtual Agents for Software Engineers during Debugging 软件工程师调试过程中的虚拟代理对话行为分类

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391487

Andrew Wood, Zachary Eberhart, Collin McMillan

{"title":"Dialogue Act Classification for Virtual Agents for Software Engineers during Debugging","authors":"Andrew Wood, Zachary Eberhart, Collin McMillan","doi":"10.1145/3387940.3391487","DOIUrl":"https://doi.org/10.1145/3387940.3391487","url":null,"abstract":"A \"dialogue act\" is a written or spoken action during a conversation. Dialogue acts are usually only a few words long, and are often categorized by researchers into a relatively small set of dialogue act types, such as eliciting information, expressing an opinion, or making a greeting. Research interest into automatic classification of dialogue acts has grown recently due to the proliferation of Virtual Agents (VA) e.g. Siri, Cortana, Alexa. But unfortunately, the gains made into VA development in one domain are generally not applicable to other domains, since the composition of dialogue acts differs in different conversations. In this paper, we target the problem of dialogue act classification for a VA for software engineers repairing bugs. A problem in the SE domain is that very little sample data exists - the only public dataset is a recently-released Wizard of Oz study with 30 conversations. Therefore, we present a transfer-learning technique to learn on a much larger dataset for general business conversations, and apply the knowledge to the SE dataset. In an experiment, we observe between 8% and 20% improvement over two key baselines.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117080965","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}

引用次数: 6

More than Code: Contributions in Scrum Software Engineering Teams 不仅仅是代码:Scrum软件工程团队中的贡献

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392241

Frederike Ramin, Christoph Matthies, Ralf Teusner

引用次数: 12

Digital Twin for Cybersecurity Incident Prediction: A Multivocal Literature Review 网络安全事件预测的数字孪生:多声音文献综述

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392199

Abhishek Pokhrel, Vikash Katta, Ricardo Colomo Palacios

引用次数: 16

Do You Just Discuss or Do You Solve?: Meeting Analysis in a Software Project at Early Stages 你只是讨论还是解决?:软件项目早期阶段的会议分析

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391468

J. Klünder, Nils Prenner, Ann-Kathrin Windmann, Marek Stess, Michael Nolting, Fabian Kortum, Lisa Handke, K. Schneider, S. Kauffeld

{"title":"Do You Just Discuss or Do You Solve?: Meeting Analysis in a Software Project at Early Stages","authors":"J. Klünder, Nils Prenner, Ann-Kathrin Windmann, Marek Stess, Michael Nolting, Fabian Kortum, Lisa Handke, K. Schneider, S. Kauffeld","doi":"10.1145/3387940.3391468","DOIUrl":"https://doi.org/10.1145/3387940.3391468","url":null,"abstract":"Software development is a very cooperative and communicative task. In most software projects, meetings are a very important medium to share information. However, these meetings are often not as effective as expected. One big issue hindering productive and satisfying meetings is inappropriate behavior such as complaining. In particular, talking about problems without at least trying to solve them decreases motivation and mood of the team. Interaction analyses in meetings allow the assessment of appropriate and inappropriate behavior influencing the quality of a meeting. Derived from an established interaction analysis coding scheme in psychology, we present act4teams-short which allows real-time coding of meetings in software projects. We apply act4teams-short in an industrial case study at Volkswagen Commercial Vehicles, a large German company in the automotive domain. We analyze ten team-internal meetings at early project stages. Our results reveal difficulties due to missing project structure and the overall project goal. Furthermore, the team has an intrinsic interest in identifying problems and solving them, without any extrinsic input being required.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124602116","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}

引用次数: 8

Improving Code Recommendations by Combining Neural and Classical Machine Learning Approaches 结合神经和经典机器学习方法改进代码推荐

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391489

M. Schumacher, K. T. Le, A. Andrzejak

{"title":"Improving Code Recommendations by Combining Neural and Classical Machine Learning Approaches","authors":"M. Schumacher, K. T. Le, A. Andrzejak","doi":"10.1145/3387940.3391489","DOIUrl":"https://doi.org/10.1145/3387940.3391489","url":null,"abstract":"Code recommendation systems for software engineering are designed to accelerate the development of large software projects. A classical example is code completion or next token prediction offered by modern integrated development environments. A particular challenging case for such systems are dynamic languages like Python due to limited type information at editing time. Recently, researchers proposed machine learning approaches to address this challenge. In particular, the Probabilistic Higher Order Grammar technique (Bielik et al., ICML 2016) uses a grammar-based approach with a classical machine learning schema to exploit local context. A method by Li et al., (IJCAI 2018) uses deep learning methods, in detail a Recurrent Neural Network coupled with a Pointer Network. We compare these two approaches quantitatively on a large corpus of Python files from GitHub. We also propose a combination of both approaches, where a neural network decides which schema to use for each prediction. The proposed method achieves a slightly better accuracy than either of the systems alone. This demonstrates the potential of ensemble-like methods for code completion and recommendation tasks in dynamically typed languages.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131324260","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}

引用次数: 6

Understanding Implicit User Feedback from Multisensorial and Physiological Data: A case study 从多感官和生理数据中理解隐含用户反馈:一个案例研究

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391466

Franci Suni Lopez, Nelly Condori-Fernández, Alejandro Catalá

{"title":"Understanding Implicit User Feedback from Multisensorial and Physiological Data: A case study","authors":"Franci Suni Lopez, Nelly Condori-Fernández, Alejandro Catalá","doi":"10.1145/3387940.3391466","DOIUrl":"https://doi.org/10.1145/3387940.3391466","url":null,"abstract":"Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that continuously change over time (e.g., emotional state of end-user). Due to these changes in the context, software continually needs to adapt for delivering software services that can satisfy user needs. However, to achieve this adaptation, it is important to gather and understand the user feedback. In this paper, we mainly investigate whether physiological data can be considered and used as a form of implicit user feedback. To this end, we conducted a case study involving a tourist traveling abroad, who used a wearable device for monitoring his physiological data, and a smartphone with a mobile app for reminding him to take his medication on time during four days. Through the case study, we were able to identify some factors and activities as emotional triggers, which were used for understanding the user context. Our results highlight the importance of having a context analyzer, which can help the system to determine whether the detected stress could be considered as actionable and consequently as implicit user feedback.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128658475","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}

引用次数: 2