{"title":"Mining User Reviews for Software Requirements of A New Mobile Banking Application","authors":"Andika Elok Amalia, Muhammad Zidny Naf’an","doi":"10.1109/ISRITI54043.2021.9702813","DOIUrl":null,"url":null,"abstract":"Migration to the new system or application is very challenging, especially if the users have to adapt to a new application that is implemented with direct conversion technique. It triggers many user reactions, one of them is their opinions and rate about the application in play store (Google Play Store for example). Application reviews can be used to elicit user requirements or to verify requirements. This paper demonstrated the result of mining application reviews to support software requirements elicitation. It motivated by research area natural language processing (NLP) for requirement engineering (RE). Training and testing conducted to a dataset contains about 1200 application reviews of a new mobile banking application by classifying them into two classes (req and other) using Multinomial Naïve Bayes algorithm. Req is for opinions that contain requirement such as feature addition or user interface (UI) request while other is label for opinions/reviews contain non-requirements. The classification performance measured are accuracy score 0,8220 and one of class that has higher classifier performance is “other” class with value precision 0.83, recall 0.94 and F1 0.99. Even though, the result is not optimal yet, especially for “req” class, this research already implemented all categories of NLP technologies such as NLP techniques, NLP tools, and NLP resources.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRITI54043.2021.9702813","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Migration to the new system or application is very challenging, especially if the users have to adapt to a new application that is implemented with direct conversion technique. It triggers many user reactions, one of them is their opinions and rate about the application in play store (Google Play Store for example). Application reviews can be used to elicit user requirements or to verify requirements. This paper demonstrated the result of mining application reviews to support software requirements elicitation. It motivated by research area natural language processing (NLP) for requirement engineering (RE). Training and testing conducted to a dataset contains about 1200 application reviews of a new mobile banking application by classifying them into two classes (req and other) using Multinomial Naïve Bayes algorithm. Req is for opinions that contain requirement such as feature addition or user interface (UI) request while other is label for opinions/reviews contain non-requirements. The classification performance measured are accuracy score 0,8220 and one of class that has higher classifier performance is “other” class with value precision 0.83, recall 0.94 and F1 0.99. Even though, the result is not optimal yet, especially for “req” class, this research already implemented all categories of NLP technologies such as NLP techniques, NLP tools, and NLP resources.
迁移到新系统或应用程序是非常具有挑战性的,特别是当用户必须适应使用直接转换技术实现的新应用程序时。它会触发许多用户反应,其中之一便是他们在play store(游戏邦注:如Google play store)中对应用的看法和评价。应用程序审查可用于引出用户需求或验证需求。本文演示了挖掘应用程序审查的结果,以支持软件需求的获取。它是由需求工程领域的自然语言处理(NLP)研究驱动的。通过使用多项式Naïve贝叶斯算法将新手机银行应用程序分为两类(req和其他),对包含约1200个应用程序评论的数据集进行了训练和测试。Req是包含需求的意见,如特性添加或用户界面(UI)请求,而other是包含非需求的意见/审查的标签。所测量的分类性能为准确率得分0,8220,分类性能较高的类别之一是“other”类,其值精度为0.83,召回率为0.94,F1为0.99。尽管结果还不是最优的,特别是对于“req”类,本研究已经实现了所有类别的NLP技术,如NLP技术,NLP工具和NLP资源。