ARdoc: app reviews development oriented classifier

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI:10.1145/2950290.2983938

Sebastiano Panichella, Andrea Di Sorbo, Emitzá Guzmán, C. A. Visaggio, G. Canfora, H. Gall

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引用次数: 95

Abstract

Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and F-Measure (ranging between 84% and 89%). While evaluating our tool developers of our study confirmed the usefulness of ARdoc in extracting important maintenance tasks for their mobile applications. Demo URL: https://youtu.be/Baf18V6sN8E Demo Web Page: http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html

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ARdoc:面向应用评论开发的分类器

Google Play、苹果App Store和Windows Phone Store都是知名的分销平台，用户可以在这些平台上下载手机应用，对它们进行评分，并对自己使用的应用发表评论。之前的研究表明，这些评论包含了重要的信息，可以帮助开发者改进他们的应用。然而，由于每天发布的大量评论，评论的非结构化性质及其质量参差不齐，分析评论是具有挑战性的。在这个演示中，我们展示了ARdoc，一个结合了三种技术的工具:(1)自然语言解析，(2)文本分析和(3)情感分析，用于自动分类应用评论中包含的有用反馈，这些反馈对于执行软件维护和发展任务很重要。我们的定量和定性分析(涉及移动专业开发人员)表明，ARdoc正确地将用户评论中对维护观点有用的反馈分类为高精度(范围在84%到89%之间)、召回率(范围在84%到89%之间)和F-Measure(范围在84%到89%之间)。在评估我们的工具时，我们研究的开发人员证实了ARdoc在为他们的移动应用程序提取重要维护任务方面的有用性。演示网址:https://youtu.be/Baf18V6sN8E演示网页:http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html

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Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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