One of the primary mechanisms by which developers receive feedback about in-field failures of software from users is through bug reports. Unfortunately, the quality of manually written bug reports can vary widely due to the effort required to include essential pieces of information, such as detailed reproduction steps (S2Rs). Despite the difficulty faced by reporters, few existing bug reporting systems attempt to offer automated assistance to users in crafting easily readable, and conveniently reproducible bug reports. To address the need for proactive bug reporting systems that actively aid the user in capturing crucial information, we introduce a novel bug reporting approach called EBug . EBug assists reporters in writing S2Rs for mobile applications by analyzing natural language information entered by reporters in real-time, and linking this data to information extracted via a combination of static and dynamic program analyses. As reporters write S2Rs, EBug is capable of automatically suggesting potential future steps using predictive models trained on realistic app usages. To evaluate EBug , we performed two user studies based on 20 failures from 11 real-world apps. The empirical studies involved ten participants that submitted ten bug reports each and ten developers that reproduced the submitted bug reports. In the studies, we found that reporters were able to construct bug reports 31% faster with EBug as compared to the state-of-the-art bug reporting system used as a baseline. EBug 's reports were also more reproducible with respect to the ones generated with the baseline. Furthermore, we compared EBug 's prediction models to other predictive modeling approaches and found that, overall, the predictive models of our approach outperformed the baseline approaches. Our results are promising and demonstrate the feasibility and potential benefits provided by proactively assistive bug reporting systems.

中文翻译：

---

通过实时了解重现步骤增强移动应用程序错误报告

开发人员从用户那里接收有关软件现场故障的反馈的主要机制之一是通过错误报告。不幸的是，由于包含基本信息（例如详细的重现步骤 (S2R)）所需的努力，手动编写的错误报告的质量可能会有很大差异。尽管记者面临困难，但很少有现有的错误报告系统试图为用户提供自动化帮助，以制作易于阅读且方便重现的错误报告。为了满足主动帮助用户捕获关键信息的主动错误报告系统的需求，我们引入了一种新的错误报告方法，称为错误。EBug 通过实时分析记者输入的自然语言信息，并将这些数据链接到通过静态和动态程序分析相结合提取的信息，帮助记者为移动应用程序编写 S2R。当记者撰写 S2R 时，EBug 能够使用根据实际应用程序使用情况训练的预测模型自动建议潜在的未来步骤。评估EBug ，我们基于 11 个真实世界应用程序的 20 次失败进行了两项用户研究。实证研究涉及十名参与者，每人提交十份错误报告，十名开发人员重现提交的错误报告。在研究中，我们发现记者能够构建错误报告 31%更快EBug 与用作基线的最先进的错误报告系统相比。EBug 的报告也相对于使用基线生成的那些更具可重复性。此外，我们比较了将 EBug 的预测模型与其他预测建模方法进行对比，发现总体而言，我们方法的预测模型优于基线方法。我们的结果很有希望，并证明了主动辅助错误报告系统提供的可行性和潜在好处。