用户会在我的应用中改变什么?总结应用程序评论,以推荐软件更改

Andrea Di Sorbo, Sebastiano Panichella, Carol V. Alexandru, Junji Shimagaki, C. A. Visaggio, G. Canfora, Harald C. Gall
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引用次数: 32

摘要

手机应用开发者不断监测用户评论的反馈,以改进他们的手机应用,更好地满足用户的期望。因此,文献中提出了自动化方法,目的是通过根据特定主题进行自动分类/优先级排序,减少分析用户评论中包含的反馈所需的工作量。在本文中,我们介绍了SURF(用户评论反馈总结器),这是一种新颖的方法,可以浓缩流行应用开发者必须管理的大量信息,这些信息是由于每天收到的用户反馈。SURF依赖于捕获用户需求的概念模型,这对执行维护和演进任务的开发人员很有用。然后,它使用复杂的总结技术来总结成千上万的评论,并生成一个交互式的、结构化的、浓缩的推荐软件更改议程。我们对17款手机应用(其中5款由Sony mobile开发)的用户评论进行了端到端的SURF评估,共有23名开发者和研究人员参与其中。结果表明SURF在总结综述方面具有较高的准确性和推荐的更改的有效性。在评估我们的方法时,我们发现SURF帮助开发人员更好地理解用户需求,与手工分析用户(变更)请求和计划未来的软件变更相比,大大减少了开发人员所需的时间。
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What would users change in my app? summarizing app reviews for recommending software changes
Mobile app developers constantly monitor feedback in user reviews with the goal of improving their mobile apps and better meeting user expectations. Thus, automated approaches have been proposed in literature with the aim of reducing the effort required for analyzing feedback contained in user reviews via automatic classification/prioritization according to specific topics. In this paper, we introduce SURF (Summarizer of User Reviews Feedback), a novel approach to condense the enormous amount of information that developers of popular apps have to manage due to user feedback received on a daily basis. SURF relies on a conceptual model for capturing user needs useful for developers performing maintenance and evolution tasks. Then it uses sophisticated summarisation techniques for summarizing thousands of reviews and generating an interactive, structured and condensed agenda of recommended software changes. We performed an end-to-end evaluation of SURF on user reviews of 17 mobile apps (5 of them developed by Sony Mobile), involving 23 developers and researchers in total. Results demonstrate high accuracy of SURF in summarizing reviews and the usefulness of the recommended changes. In evaluating our approach we found that SURF helps developers in better understanding user needs, substantially reducing the time required by developers compared to manually analyzing user (change) requests and planning future software changes.
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