2021 IEEE 29th International Requirements Engineering Conference (RE)最新文献_第5页

NFRNet: A Deep Neural Network for Automatic Classification of Non-Functional Requirements 非功能性需求自动分类的深度神经网络

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00057

Bingyu Li, Zhi Li, Yilong Yang

引用次数: 2

What can Open Domain Model Tell Us about the Missing Software Requirements: A Preliminary Study 关于缺失的软件需求，开放领域模型能告诉我们什么:初步研究

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00010

Ziyan Zhao, Li Zhang, Xiaoli Lian

引用次数: 2

A Survey of Instructional Approaches in the Requirements Engineering Education Literature 需求工程教育文献中教学方法综述

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00030

Marian Daun, A. Grubb, B. Tenbergen

引用次数: 8

On the Role of User Feedback in Software Evolution: a Practitioners’ Perspective 用户反馈在软件演进中的作用:一个实践者的视角

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00027

Simon van Oordt, Emitzá Guzmán

{"title":"On the Role of User Feedback in Software Evolution: a Practitioners’ Perspective","authors":"Simon van Oordt, Emitzá Guzmán","doi":"10.1109/RE51729.2021.00027","DOIUrl":"https://doi.org/10.1109/RE51729.2021.00027","url":null,"abstract":"User feedback is indispensable in software evolution. Previous work has proposed ways for automatically extracting requirements, bug reports and other valuable information from feedback. However, little is actually known about how user feedback— especially the one available through newer channels, such as social media—is incorporated in development processes. To date, only a few case studies discuss the matter and the results are not always consistent. We carried out a mixed methods study to understand the current state of practice of harnessing user feedback in software development. Qualitatively, we performed interviews with 18 software practitioners to get a deeper understanding of the role of user feedback in software evolution. Quantitatively, we surveyed 101 software practitioners to cross-validate the interview findings and improve the generalizability of the results. We found that feedback is captured to (1) identify bugs, features and usability issues, (2) get a better understanding of the user, and (3) prioritize requirements. Our results indicate that analyzing feedback is time-consuming and has a number of challenges. Among them, feedback is typically analyzed manually and is spread over a wide range of channels and company departments. Our findings stress the current importance for cross-department cooperation and call for the exploration of tools that can centralize user feedback.","PeriodicalId":440285,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference (RE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114429424","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: 10

SusAF Welcomes SusApp: Tool Support for the Sustainability Awareness Framework SusApp:可持续发展意识框架的工具支持

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00049

Maike Basmer, Timo Kehrer, B. Penzenstadler

引用次数: 3

MRDQA: A Deep Multimodal Requirement Document Quality Analyzer MRDQA:深度多模态需求文档质量分析器

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00063

M. Ye, Jicheng Cao, Shengyu Cheng, Dong Liu, Shenghai Xu, Jinning He

{"title":"MRDQA: A Deep Multimodal Requirement Document Quality Analyzer","authors":"M. Ye, Jicheng Cao, Shengyu Cheng, Dong Liu, Shenghai Xu, Jinning He","doi":"10.1109/RE51729.2021.00063","DOIUrl":"https://doi.org/10.1109/RE51729.2021.00063","url":null,"abstract":"In the field of requirement document quality assessment, existing methods mainly focused on textual patterns of requirements. Actually, the cognitive process that experts read and qualitatively measure a requirement document is from outward appearance to inner essence. Inspired by this intuition, this paper proposed a Multimodal Requirement Document Quality Analyzer (MRDQA), a neural model which combines the textual content with the visual rendering of requirement documents for quality assessing. MRDQA can capture implicit quality indicators which do not exist in requirement text, such as tables, diagrams, and visual layout. We evaluated MRDQA on the requirement documents collected from ZTE and achieved 81.3% accuracy in classifying their quality into three levels (high, medium, and low). We have successfully applied MRDQA as a pre-filter in ZTE’s requirement review system. It identifies low and medium quality requirements, thereby allows review experts to focus only on high-quality requirements. With this mechanism, the workload can be greatly reduced and the requirement review process can be accelerated.","PeriodicalId":440285,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference (RE)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132410077","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: 1

Design Decisions in the Construction of Traceability Information Models for Safe Automotive Systems 安全汽车系统可追溯信息模型构建中的设计决策

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00024

Jan-Philipp Steghöfer, Björn Koopmann, J. Becker, Mikaela Törnlund, Yulla Ibrahim, Mazen Mohamad

引用次数: 3

MARE: an Active Learning Approach for Requirements Classification MARE:需求分类的主动学习方法

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.9714537

Cláudia Magalhães, João Araújo, Alberto Sardinha

引用次数: 3

Zoom4PF: A Tool for Refining Static and Dynamic Domain Descriptions in Problem Frames Zoom4PF:在问题框架中精炼静态和动态领域描述的工具

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00047

Shangfeng Wei, Zhi Li, Yilong Yang, Hongbin Xiao

引用次数: 3

A Pipeline for Automating Labeling to Prediction in Classification of NFRs NFRs分类中自动标注到预测的流水线

2021 IEEE 29th International Requirements Engineering Conference (RE) Pub Date : 2021-09-01 DOI: 10.1109/RE51729.2021.00036

Ranit Chatterjee, Abdul Ahmed, Preethu Rose Anish, B. Suman, Prashant Lawhatre, S. Ghaisas

{"title":"A Pipeline for Automating Labeling to Prediction in Classification of NFRs","authors":"Ranit Chatterjee, Abdul Ahmed, Preethu Rose Anish, B. Suman, Prashant Lawhatre, S. Ghaisas","doi":"10.1109/RE51729.2021.00036","DOIUrl":"https://doi.org/10.1109/RE51729.2021.00036","url":null,"abstract":"Non-Functional Requirements (NFRs) focus on the operational constraints of the software system. Early detection of NFRs enables their incorporation into the architectural design at an initial stage, a practice obviously preferable to expensive refactoring at a later stage. Automated identification and classification of NFRs has therefore seen numerous efforts using rule-based, machine learning and deep learning-based approaches. One of the major challenges for such an automation is the manual effort that needs to be invested into labeling of training data. This is a concern for large software vendors who typically work on a variety of applications in diverse domains. We address this challenge by designing a pipeline that facilitates classification of NFRs using only a limited amount (~ 20% of an available new dataset) of labeled data for training. We (1) employed Snorkel to automatically label a dataset comprising NFRs from various Software Requirement Specification documents, (2) trained several classifiers using it, and (3) reused these pre-trained classifiers using a Transfer Learning approach to classify NFRs in industry-specific datasets. From among the various language model classifiers, the best results have been obtained for a BERT based classifier fine-tuned to learn the linguistic intricacies of three different domain-specific datasets from real-life projects.","PeriodicalId":440285,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference (RE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133822043","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