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首页> 外文会议>International Symposium on Software Reliability Engineering >CoLUA: Automatically Predicting Configuration Bug Reports and Extracting Configuration Options
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CoLUA: Automatically Predicting Configuration Bug Reports and Extracting Configuration Options

机译：COLUA：自动预测配置错误报告和提取配置选项

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摘要

Configuration bugs are among the dominant causes of software failures. Software organizations often use bug tracking systems to manage bug reports collected from developers and users. In order for software developers to understand and reproduce configuration bugs, it is vital for them to know whether a bug in the bug report is related to configuration issues, this is not often easily discerned due to a lack of easy to spot terminology in the bug reports. In addition, to locate and fix a configuration bug, a developer needs to know which configuration options are associated with the bug. To address these two problems, we introduce CoLUA, a two-step automated approach that combines natural language processing, information retrieval, and machine learning. In the first step, CoLUA selects features from the textual information in the bug reports, and uses various machine learning techniques to build classification models, developers can use these models to label a bug report as either a configuration bug report or a non-configuration bug report. In the second step, CoLUA identifies which configuration options are involved in the labeled configuration bug reports. We evaluate CoLUA on 900 bug reports from three large open source software systems. The results show that CoLUA predicts configuration bug reports with high accuracy and that it effectively identifies the root causes of configuration options.
机译：配置错误是软件故障的主导原因之一。软件组织经常使用错误跟踪系统来管理从开发人员和用户收集的错误报告。为了使软件开发人员了解和重现配置错误，他们要知道错误报告中的错误是否与配置问题有关，因此由于缺乏缺乏遗漏术语在错误中缺乏易于发现术语，这并不容易辨别。报告。此外，要定位和修复配置错误，开发人员需要知道与错误相关联的配置选项。为了解决这两个问题，我们介绍了Colua，这是一种两步自动化方法，它结合了自然语言处理，信息检索和机器学习。在第一步中，COLUA从错误报告中选择文本信息中的功能，并使用各种机器学习技术来构建分类模型，开发人员可以使用这些模型将错误报告标记为配置错误报告或非配置错误报告。在第二步中，COLUA识别标记的配置错误报告中涉及哪些配置选项。我们评估来自三个大型开源软件系统的900个错误报告的Colua。结果表明，COLUA以高精度预测配置错误报告，其有效地识别了配置选项的根本原因。

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