从质量属性需求中自动识别和生成质量属性场景

Amsalu Tessema, E. Alemneh
{"title":"从质量属性需求中自动识别和生成质量属性场景","authors":"Amsalu Tessema, E. Alemneh","doi":"10.1109/ict4da53266.2021.9672247","DOIUrl":null,"url":null,"abstract":"Identification and generation of Quality Attribute Scenarios (QASs) from Quality Attribute Requirements (QARs) is a critical software engineering technique for defining system specifications and is helpful in facilitating development of Software Architecture (SA) that meets the expected quality. However, identifying QAS types and extracting their components traditionally is a complex task that consumes time and energy. It also requires high budget and is an error-prone task, especially for inexperienced users. This study aims to develop an automatic QASs identification and generation model that extracts QASs from QARs. We used Natural Language Processing (NLP) to preprocess texts and Machine Learning (ML) approaches to identify QAS types, and we built a Custom Named Entity Recognition (CNER) model to generate QAS components. To evaluate the proposed identification model, we used five algorithms. Both SVM and Scholastic Gradient Descent (SGD) classifier algorithms scored 97.7 % accuracy, while LR, KNN, and NB scored 96%, 91.6 %, and 88.8%, respectively. The CNER model achieved 92.3% recall, 93.3% precision, and 92.8% F1-measure score. The results show that automatic identification of QASs from QARs has a potential to replace time taking and error-prone manual work.","PeriodicalId":371663,"journal":{"name":"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automatic Quality Attribute Scenarios Identification and Generation from Quality Attribute Requirements\",\"authors\":\"Amsalu Tessema, E. Alemneh\",\"doi\":\"10.1109/ict4da53266.2021.9672247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Identification and generation of Quality Attribute Scenarios (QASs) from Quality Attribute Requirements (QARs) is a critical software engineering technique for defining system specifications and is helpful in facilitating development of Software Architecture (SA) that meets the expected quality. However, identifying QAS types and extracting their components traditionally is a complex task that consumes time and energy. It also requires high budget and is an error-prone task, especially for inexperienced users. This study aims to develop an automatic QASs identification and generation model that extracts QASs from QARs. We used Natural Language Processing (NLP) to preprocess texts and Machine Learning (ML) approaches to identify QAS types, and we built a Custom Named Entity Recognition (CNER) model to generate QAS components. To evaluate the proposed identification model, we used five algorithms. Both SVM and Scholastic Gradient Descent (SGD) classifier algorithms scored 97.7 % accuracy, while LR, KNN, and NB scored 96%, 91.6 %, and 88.8%, respectively. The CNER model achieved 92.3% recall, 93.3% precision, and 92.8% F1-measure score. The results show that automatic identification of QASs from QARs has a potential to replace time taking and error-prone manual work.\",\"PeriodicalId\":371663,\"journal\":{\"name\":\"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ict4da53266.2021.9672247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ict4da53266.2021.9672247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

从质量属性需求(qar)中识别和生成质量属性场景(QASs)是定义系统规范的关键软件工程技术,有助于促进满足预期质量的软件体系结构(SA)的开发。然而,传统上,识别QAS类型并提取其成分是一项费时费力的复杂任务。它还需要很高的预算,并且是一个容易出错的任务,特别是对于没有经验的用户。本研究旨在建立一个从qar中提取QASs的自动识别和生成模型。我们使用自然语言处理(NLP)对文本进行预处理,使用机器学习(ML)方法识别QAS类型,并构建了自定义命名实体识别(CNER)模型来生成QAS组件。为了评估所提出的识别模型,我们使用了五种算法。SVM和Scholastic Gradient Descent (SGD)分类器算法的准确率均为97.7%,而LR、KNN和NB的准确率分别为96%、91.6%和88.8%。CNER模型的查全率为92.3%,查准率为93.3%,f1指标得分为92.8%。结果表明,从qar中自动识别QASs有可能取代耗时且容易出错的人工工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Quality Attribute Scenarios Identification and Generation from Quality Attribute Requirements
Identification and generation of Quality Attribute Scenarios (QASs) from Quality Attribute Requirements (QARs) is a critical software engineering technique for defining system specifications and is helpful in facilitating development of Software Architecture (SA) that meets the expected quality. However, identifying QAS types and extracting their components traditionally is a complex task that consumes time and energy. It also requires high budget and is an error-prone task, especially for inexperienced users. This study aims to develop an automatic QASs identification and generation model that extracts QASs from QARs. We used Natural Language Processing (NLP) to preprocess texts and Machine Learning (ML) approaches to identify QAS types, and we built a Custom Named Entity Recognition (CNER) model to generate QAS components. To evaluate the proposed identification model, we used five algorithms. Both SVM and Scholastic Gradient Descent (SGD) classifier algorithms scored 97.7 % accuracy, while LR, KNN, and NB scored 96%, 91.6 %, and 88.8%, respectively. The CNER model achieved 92.3% recall, 93.3% precision, and 92.8% F1-measure score. The results show that automatic identification of QASs from QARs has a potential to replace time taking and error-prone manual work.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信