Removing all function errors is critical for making successful mobile apps. Since app testing may miss some function errors given limited time and resource, the user reviews of mobile apps are very important to developers for learning the uncaught errors. Unfortunately, manually handling each review is time-consuming and even error-prone. Existing studies on mobile apps’ reviews could not help developers effectively locate the problematic code according to the reviews, because the majority of such research focus on review classification, requirements engineering, sentiment analysis, and summarization [1]. They do not localize the function errors described in user reviews in apps’ code. Moreover, recent studies on mapping reviews to problematic source files look for the matching between the words in reviews and that in source code, bug reports, commit messages, and stack traces, thus may result in false positives and false negatives since they do not consider the semantic meaning and part of speech tag of each word. In this paper, we propose a novel approach to localize function errors in mobile apps by exploiting the context information in user reviews and correlating the reviews and bytecode through their semantic meanings. We realize our new approach as a tool named ReviewSolver, and carefully evaluate it with reviews of real apps. The experimental result shows that ReviewSolver has much better performance than the state-of-the-art tools (i.e., ChangeAdvisor and Where2Change).

中文翻译：

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通过用户评论自动定位移动应用程序中的功能错误


消除所有功能错误对于制作成功的移动应用程序至关重要。由于时间和资源有限，应用程序测试可能会遗漏一些功能错误，因此移动应用程序的用户评论对于开发人员了解未捕获的错误非常重要。不幸的是，手动处理每个审核非常耗时，甚至容易出错。现有对移动应用评论的研究无法帮助开发人员根据评论有效定位有问题的代码，因为此类研究大多数集中在评论分类、需求工程、情感分析和摘要上[1]。他们不会将用户评论中描述的功能错误本地化到应用程序代码中。此外，最近关于将评论映射到有问题的源文件的研究寻找评论中的单词与源代码、错误报告、提交消息和堆栈跟踪中的单词之间的匹配，因此可能会导致误报和漏报，因为它们没有考虑每个单词的语义和词性标记。在本文中，我们提出了一种新方法，通过利用用户评论中的上下文信息并通过语义将评论和字节码关联起来，来定位移动应用程序中的功能错误。我们将我们的新方法实现为名为 ReviewSolver 的工具，并通过对真实应用程序的评论仔细评估它。实验结果表明，ReviewSolver 比最先进的工具（即 ChangeAdvisor 和Where2Change）具有更好的性能。