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首页> 外文会议>IEEE International Conference on Software Maintenance and Evolution >Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques
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Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques

机译：使用深度学习技术来准确重复的重复错误检索

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摘要

Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.
机译：重复的错误检测是识别新报告的错误是否是系统中现有错误的副本，以及从过去检索原始或类似错误的副本。这是需要避免昂贵的重新发现和冗余工作。在典型的软件项目中，报告的重复错误数量可能达到数千次，在手动干预的成本和时间方面使其昂贵。这使得软件工程域中的重要性或类似错误检测的问题。然而，尽管使用各种机器学习技术的许多方法，但是在实践中，同样的自动化解决方案尚未完全准确。在这项工作中，我们使用暹罗卷积神经网络（CNN）和长短期内存（LSTM）提出了检索和分类模型，以便准确地检测和检索重复和类似的错误。我们报告了接近90 ％的准确性，并且倒回到80 ％的召回速率，这使得这种系统的实际使用可以实现。我们详细描述了我们的模型以及深度学习领域的相关讨论。通过呈现详细的实验结果，我们说明了模型在实际系统中的有效性，包括监督培训数据的存储库。

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