使用深度神经网络构建使用概况
Building Usage Profiles Using Deep Neural Nets
摘要：
为了提高软件质量，需要构建类似于软件产品在现场使用的测试场景。当产品的客户群庞大而多样化时，这项任务就变得具有挑战性。在这种情况下，现有的分析方法，如操作分析，很难应用。在这项工作中，我们考虑公开提供的视频教程的产品配置文件的使用。我们的目标是构建一个自动的方法来提取信息的用户动作从教学视频。为了实现这一目标，我们使用深度卷积神经网络（DCNN）来识别用户动作。我们的试点研究表明，经过训练以识别视频中用户动作的DCNN可以在236个公开的Microsoft Word教程视频（发布在YouTube上）中对五种不同的动作进行分类。在我们的实证评估中，我们报告了所有操作的平均精度为94.42%。这项研究证明了基于DCNN的方法从视频中提取软件使用信息的有效性。此外，这种方法可以帮助其他需要关于产品的客户使用的信息的软件工程活动。
英文摘要：
To improve software quality, one needs to build test scenarios resembling the usage of a software product in the field. This task is rendered challenging when a product's customer base is large and diverse. In this scenario, existing profiling approaches, such as operational profiling, are difficult to apply. In this work, we consider publicly available video tutorials of a product to profile usage. Our goal is to construct an automatic approach to extract information about user actions from instructional videos. To achieve this goal, we use a Deep Convolutional Neural Network (DCNN) to recognize user actions. Our pilot study shows that a DCNN trained to recognize user actions in video can classify five different actions in a collection of 236 publicly available Microsoft Word tutorial videos (published on YouTube). In our empirical evaluation we report a mean average precision of 94.42% across all actions. This study demonstrates the efficacy of DCNN-based methods for extracting software usage information from videos. Moreover, this approach may aid in other software engineering activities that require information about customer usage of a product.
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