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首页> 外文期刊>ACM transactions on software engineering and methodology >Recommending New Features from Mobile App Descriptions
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Recommending New Features from Mobile App Descriptions

机译：从移动应用说明中推荐新功能

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摘要

The rapidly evolving mobile applications (apps) have brought great demand for developers to identify new features by inspecting the descriptions of similar apps and acquire missing features for their apps. Unfortunately, due to the huge number of apps, this manual process is time-consuming and unscalable. To help developers identify new features, we propose a new approach named SAFER. In this study, we first develop a tool to automatically extract features from app descriptions. Then, given an app, we leverage the topic model to identify its similar apps based on the extracted features and API names of apps. Finally, we design a feature recommendation algorithm to aggregate and recommend the features of identified similar apps to the specified app. Evaluated over a collection of 533 annotated features from 100 apps, SAFER achieves a Hit@15 score of up to 78.68% and outperforms the baseline approach KNN+ by 17.23% on average. In addition, we also compare SAFER against a typical technique of recommending features from user reviews, i.e., CLAP. Experimental results reveal that SAFER is superior to CLAP by 23.54% in terms of Hit@15.
机译：快速发展的移动应用程序对开发人员提出了很高的要求，即通过检查相似应用程序的描述并获取其应用程序缺少的功能来识别新功能。不幸的是，由于应用程序数量众多，此手动过程耗时且不可扩展。为了帮助开发人员识别新功能，我们提出了一种名为SAFER的新方法。在这项研究中，我们首先开发了一种从应用程序描述中自动提取功能的工具。然后，对于给定的应用程序，我们利用主题模型根据提取的功能和应用程序的API名称来识别其相似的应用程序。最后，我们设计了一种功能推荐算法，以将识别出的相似应用程序的功能汇总并推荐给指定应用程序。对100个应用程序中的533个带注释功能进行了评估，SAFER的Hit @ 15得分高达78.68％，并且比基准方法KNN +平均高出17.23％。此外，我们还将SAFER与从用户评论中推荐功能的一种典型技术（即CLAP）进行了比较。实验结果表明，SAFE在Hit @ 15方面比CLAP优越23.54％。

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