arXiv 2103.080832021-03-15cs.SEcs.AI

EnHMM：使用Encourage Hacker和Stack Traces来预测Bug报告字段的重新分配

EnHMM: On the Use of Ensemble HMMs and Stack Traces to Predict the Reassignment of Bug Report Fields

Concordia University（康考迪亚大学）；Polytechnique Montreal（蒙特利尔理工学院）；Washington State University（华盛顿州立大学）

Md Shariful Islam, Abdelwahab Hamou-Lhadj, Korosh K. Sabor, Mohammad Hamdaqa, Haipeng Cai

备注：: Published in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2021), 11 pages, 7 figures

摘要：

Bug报告（BR）包含重要的信息，可以帮助分类团队确定优先级，并将Bug分配给将提供修复的开发人员。然而，研究表明，BR字段通常包含需要重新分配的错误信息，这会延迟bug修复过程。存在用于预测BR字段是否应当被重新分配的方法。这些研究主要使用BR描述和传统的机器学习算法（SVM，KNN等）。因此，它们不能完全受益于BR数据中的信息的顺序次序，诸如BR堆栈跟踪中的函数调用序列，这对于提高预测准确度可能是有价值的。在本文中，我们提出了一种新的方法，称为EnHMM，用于预测BR字段的重新分配使用集成隐马尔可夫模型（HHMM），训练堆栈轨迹。EnHMM利用HMM表示顺序数据的自然能力，对BR堆栈跟踪中的函数调用的时间顺序进行建模。当应用于Eclipse和Gnome BR存储库时，EnHMM在Eclipse数据集上实现了54%，76%和60%的平均精确度，召回率和F-测量，在Gnome数据集上实现了41%，69%和51%。我们还发现，EnHMM比最好的单一HMM提高了36%（Eclipse）和76%（Gnome）。最后，当将EnHMM与该领域最近的方法Im.ML.KNN进行比较时，我们发现EnHMM的平均F-度量得分将Im.ML.KNN的平均F-度量提高了6.80%，并将Im.ML.KNN的平均召回率提高了36.09%。然而，EnHMM的平均精度低于Im.ML.KNN的平均精度（53.93%对56.71%）。

英文摘要：

Bug reports (BR) contain vital information that can help triaging teams prioritize and assign bugs to developers who will provide the fixes. However, studies have shown that BR fields often contain incorrect information that need to be reassigned, which delays the bug fixing process. There exist approaches for predicting whether a BR field should be reassigned or not. These studies use mainly BR descriptions and traditional machine learning algorithms (SVM, KNN, etc.). As such, they do not fully benefit from the sequential order of information in BR data, such as function call sequences in BR stack traces, which may be valuable for improving the prediction accuracy. In this paper, we propose a novel approach, called EnHMM, for predicting the reassignment of BR fields using ensemble Hidden Markov Models (HMMs), trained on stack traces. EnHMM leverages the natural ability of HMMs to represent sequential data to model the temporal order of function calls in BR stack traces. When applied to Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall, and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on Gnome dataset. We also found that EnHMM improves over the best single HMM by 36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to Im.ML.KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im.ML.KNN by 6.80% and improves the average recall of Im.ML.KNN by 36.09%. However, the average precision of EnHMM is lower than that of Im.ML.KNN (53.93% as opposed to 56.71%).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。