2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)最新文献_第8页

Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work 用深度学习检测需求气味:经验、挑战和未来工作

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2021-08-06 DOI: 10.1109/REW53955.2021.00027

Mohammad Kasra Habib, S. Wagner, D. Graziotin

{"title":"Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work","authors":"Mohammad Kasra Habib, S. Wagner, D. Graziotin","doi":"10.1109/REW53955.2021.00027","DOIUrl":"https://doi.org/10.1109/REW53955.2021.00027","url":null,"abstract":"Requirements Engineering (RE) is one of the initial phases when building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem with natural language is that it can easily lead to different understandings if it is not expressed precisely by the stakeholders involved. This results in building a product which is different from the expected one. Previous work proposed to enhance the quality of the software requirements by detecting language errors based on ISO 29148 requirements language criteria. The existing solutions apply classical Natural Language Processing (NLP) to detect them. NLP has some limitations, such as domain dependability which results in poor generalization capability. Therefore, this work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem that is tied with classical NLP and improve precision and recall metrics using a manually labeled dataset. The current findings show that the dataset is unbalanced and which class examples should be added more. It is tempting to train algorithms even if the dataset is not considerably representative. Whence, the results show that models are overfitting; in Machine Learning this issue is adressed by adding more instances to the dataset, improving label quality, removing noise, and reducing the learning algorithms complexity, which is planned for this research.","PeriodicalId":393646,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116144138","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: 4

Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives 智能系统的可解释性审计:多学科视角的基本原理

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2021-08-05 DOI: 10.1109/REW53955.2021.00030

Markus Langer, Kevin Baum, K. Hartmann, Stefan Hessel, Timo Speith, Jonas Wahl

引用次数: 12

From Textual to Verbal Communication: Towards Applying Sentiment Analysis to a Software Project Meeting 从文本到口头交流:情感分析在软件项目会议中的应用

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2021-08-04 DOI: 10.1109/REW53955.2021.00065

M. Herrmann, J. Klünder

{"title":"From Textual to Verbal Communication: Towards Applying Sentiment Analysis to a Software Project Meeting","authors":"M. Herrmann, J. Klünder","doi":"10.1109/REW53955.2021.00065","DOIUrl":"https://doi.org/10.1109/REW53955.2021.00065","url":null,"abstract":"Sentiment analysis gets increasing attention in software engineering with new tools emerging from new insights provided by researchers. Existing use cases and tools are meant to be used for textual communication such as comments on collaborative version control systems. While this can already provide useful feedback for development teams, a lot of communication takes place in meetings and is not suited for present tool designs and concepts. In this paper, we present a concept that is capable of processing live meeting audio and classifying transcribed statements into sentiment polarity classes. We combine the latest advances in open source speech recognition with previous research in sentiment analysis. We tested our approach on a student software project meeting to gain proof of concept, showing moderate agreement between the classifications of our tool and a human observer on the meeting audio. Despite the preliminary character of our study, we see promising results motivating future research in sentiment analysis on meetings. For example, the polarity classification can be extended to detect destructive behaviour that can endanger project success.","PeriodicalId":393646,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127768763","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

The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback 在CrowdRE中使用视觉视频的潜力:视频评论作为反馈来源

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2021-08-04 DOI: 10.1109/REW53955.2021.00053

Oliver Karras, Eklekta Kristo, J. Klünder

{"title":"The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback","authors":"Oliver Karras, Eklekta Kristo, J. Klünder","doi":"10.1109/REW53955.2021.00053","DOIUrl":"https://doi.org/10.1109/REW53955.2021.00053","url":null,"abstract":"Vision videos are established for soliciting feedback and stimulating discussions in requirements engineering (RE) practices such as focus groups. Different researchers motivated the transfer of these benefits into crowd-based RE (CrowdRE) by using vision videos on social media platforms. So far, however, little research explored the potential of using vision videos for CrowdRE in detail. In this paper, we analyze and assess this potential, in particular, focusing on video comments as a source of feedback. In a case study, we analyzed 4505 comments on a vision video from YouTube. We found that the video solicited 2770 comments from 2660 viewers in four days. This is more than 50% of all comments the video received in four years. Even though only a certain fraction of these comments are relevant to RE, the relevant comments address typical intentions and topics of user feedback, such as feature request or problem report. Besides the typical user feedback categories, we found more than 300 comments that address the topic safety which has not appeared in previous analyses of user feedback. In an automated analysis, we compared the performance of three machine learning algorithms on classifying the video comments. Despite certain differences, the algorithms classified the video comments well. Based on these findings, we conclude that the use of vision videos for CrowdRE has a large potential. Despite the preliminary nature of the case study, we are optimistic that vision videos can motivate stakeholders to actively participate in a crowd and solicit numerous of video comments as a valuable source of feedback.","PeriodicalId":393646,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131530583","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: 7

Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews 从tweet和App Store评论中挖掘功能请求和Bug报告的迁移学习

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2021-08-02 DOI: 10.1109/REW53955.2021.00019

Pablo Restrepo Henao, Jannik Fischbach, Dominik Spies, Julian Frattini, Andreas Vogelsang

{"title":"Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews","authors":"Pablo Restrepo Henao, Jannik Fischbach, Dominik Spies, Julian Frattini, Andreas Vogelsang","doi":"10.1109/REW53955.2021.00019","DOIUrl":"https://doi.org/10.1109/REW53955.2021.00019","url":null,"abstract":"Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and app stores is challenging since (1) about 70% of user comments contain noisy, irrelevant information, (2) the amount of user comments grows daily making manual analysis unfeasible, and (3) user comments are written in different languages. Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for this task. In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments. Specifically, we train both monolingual and multilingual BERT models and compare the performance with state-of-the-art methods. We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets. However, we also observed that the application of heavyweight TL models does not necessarily lead to better performance. In fact, our multilingual BERT models perform worse than traditional ML methods.","PeriodicalId":393646,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126705417","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: 19

CATE: CAusality Tree Extractor from Natural Language Requirements CATE:自然语言需求的因果树提取器

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2021-07-21 DOI: 10.1109/REW53955.2021.00018

Noah Jadallah, Jannik Fischbach, Julian Frattini, Andreas Vogelsang

引用次数: 1

Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks 基于递归神经张量网络的自然语言需求细粒度因果关系提取

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2021-07-21 DOI: 10.1109/REW53955.2021.00016

Jannik Fischbach, Tobias Springer, Julian Frattini, Henning Femmer, Andreas Vogelsang, D. Méndez

{"title":"Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks","authors":"Jannik Fischbach, Tobias Springer, Julian Frattini, Henning Femmer, Andreas Vogelsang, D. Méndez","doi":"10.1109/REW53955.2021.00016","DOIUrl":"https://doi.org/10.1109/REW53955.2021.00016","url":null,"abstract":"[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective & Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on Recursive Neural Tensor Networks. Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74% in the evaluation on the Causality Treebank. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community.","PeriodicalId":393646,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121106816","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: 3

Welcome from the Organizers 主办方的欢迎

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) Pub Date : 2020-08-01 DOI: 10.1109/modre51215.2020.00005

Ana Moreira, G. Mussbacher, J. Araújo, Pablo Sánchez

引用次数: 0