Extracting Software Change Requests from Mobile App Reviews

Muhammad Nadeem, Khurram Shahzad, N. Majeed
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引用次数: 1

Abstract

The use of mobile apps is increasing rapidly. These apps have thousands of reviews which are widely acknowledged as a valuable resource for the community involved in the development of mobile apps. In this study, we contend that these reviews can be used to generate software change request document for improving mobile apps. A pre-requisite for generating such a document is the identification of Software Change Requests (SCR) from the user reviews. However, the manual processing of these large number of reviews to identify SCRs is a resource intensive task. However, most of the existing studies have focused on the identification of bugs. Whereas, a few studies have been conducted to identify change requests and its localization from mobile apps review, which substantially different from extracting SCR. To that end, we have scrapped review of seven Mobile Apps and developed a dataset that can be used for training of machine learning techniques for the automatic identification of SCRs. A key feature of the approach is that we have documented the annotation guidelines that are used to distinguish between SCR and non-SCR sentences. These guidelines can be used to enhance the developed dataset, as well as to develop new datasets. As another contribution, we have evaluated the effectiveness of five supervised learning techniques for their ability to identify SCR sentences from user reviews. The study shows that Logistic Regression achieved a nearly perfect F1 score of 0.97 for extracting SCR from textual reviews.
从手机应用评论中提取软件变更请求
移动应用程序的使用正在迅速增加。这些应用有成千上万的评论,这些评论被广泛认为是参与手机应用开发的社区的宝贵资源。在本研究中,我们认为这些评论可以用来生成软件变更请求文档,以改进移动应用程序。生成这样一个文档的先决条件是从用户评审中识别软件变更请求(SCR)。然而,手工处理这些大量的评审以识别scr是一项资源密集型的任务。然而,现有的大多数研究都集中在细菌的识别上。然而,已经进行了一些研究,从移动应用程序审查中识别变更请求及其本地化,这与提取SCR有很大不同。为此,我们放弃了对七个移动应用程序的审查,并开发了一个数据集,可用于训练机器学习技术,以自动识别scr。该方法的一个关键特性是我们已经记录了用于区分SCR和非SCR句子的注释指南。这些指南可用于增强已开发的数据集,也可用于开发新的数据集。作为另一个贡献,我们评估了五种监督学习技术从用户评论中识别SCR句子的能力的有效性。研究表明,Logistic回归在提取文本评论的SCR方面取得了接近完美的F1分数0.97。
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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