Automatic Classification of Non-Functional Requirements from Augmented App User Reviews

Mengmeng Lu, Peng Liang
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引用次数: 127

Abstract

Context: The leading App distribution platforms, Apple App Store, Google Play, and Windows Phone Store, have over 4 million Apps. Research shows that user reviews contain abundant useful information which may help developers to improve their Apps. Extracting and considering Non-Functional Requirements (NFRs), which describe a set of quality attributes wanted for an App and are hidden in user reviews, can help developers to deliver a product which meets users' expectations. Objective: Developers need to be aware of the NFRs from massive user reviews during software maintenance and evolution. Automatic user reviews classification based on an NFR standard provides a feasible way to achieve this goal. Method: In this paper, user reviews were automatically classified into four types of NFRs (reliability, usability, portability, and performance), Functional Requirements (FRs), and Others. We combined four classification techniques BoW, TF-IDF, CHI2, and AUR-BoW (proposed in this work) with three machine learning algorithms Naive Bayes, J48, and Bagging to classify user reviews. We conducted experiments to compare the F-measures of the classification results through all the combinations of the techniques and algorithms. Results: We found that the combination of AUR-BoW with Bagging achieves the best result (a precision of 71.4%, a recall of 72.3%, and an F-measure of 71.8%) among all the combinations. Conclusion: Our finding shows that augmented user reviews can lead to better classification results, and the machine learning algorithm Bagging is more suitable for NFRs classification from user reviews than Naïve Bayes and J48.
从增强的应用程序用户评论中自动分类非功能需求
背景:苹果App Store、b谷歌Play和Windows Phone Store等主要应用发行平台拥有超过400万款应用。研究表明,用户评论包含了大量有用的信息,可以帮助开发者改进他们的应用。提取和考虑非功能需求(NFRs),它描述了应用程序所需的一组质量属性,隐藏在用户评论中,可以帮助开发人员交付满足用户期望的产品。目标:开发人员需要在软件维护和发展期间从大量用户评论中了解NFRs。基于NFR标准的自动用户评论分类为实现这一目标提供了一种可行的方法。方法:在本文中,用户评论被自动分为四类NFRs(可靠性、可用性、可移植性和性能)、功能需求(Functional Requirements, FRs)和其他。我们将四种分类技术BoW、TF-IDF、CHI2和AUR-BoW(在这项工作中提出)与三种机器学习算法Naive Bayes、J48和Bagging结合起来对用户评论进行分类。我们进行了实验,比较了所有技术和算法组合的分类结果的f -measure。结果:我们发现,在所有组合中,AUR-BoW与Bagging的组合效果最好(精密度为71.4%,召回率为72.3%,F-measure为71.8%)。结论:我们的研究结果表明,增强用户评论可以获得更好的分类结果,机器学习算法Bagging比Naïve Bayes和J48更适合从用户评论中分类NFRs。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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