探究了不同碳纤维/玄武岩纤维(carbon fiber/basalt fiber,CF/BF)配比的混杂层合板在弹道冲击及冲击后压缩(compression after impact,CAI)性能方面的表现,结果表明,玄武岩纤维显著提升了混杂层合板的能量吸收能力。采用C扫描、电子显...探究了不同碳纤维/玄武岩纤维(carbon fiber/basalt fiber,CF/BF)配比的混杂层合板在弹道冲击及冲击后压缩(compression after impact,CAI)性能方面的表现,结果表明,玄武岩纤维显著提升了混杂层合板的能量吸收能力。采用C扫描、电子显微镜和扫描电镜分析了材料的损伤机制,揭示了材料性能提升的内在机理。另一方面,混杂层合板的初始压缩强度随玄武岩纤维含量的增加而下降。在混杂层合板的能量吸收能力增强与初始压缩强度下降的共同作用下,CAI测试中受损层合板的残余压缩强度呈局部波动趋势。研究结果可为轻量化、高抗冲击复合材料的结构设计提供指导。展开更多
In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they oft...In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique） ＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling） ＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.展开更多
文摘探究了不同碳纤维/玄武岩纤维(carbon fiber/basalt fiber,CF/BF)配比的混杂层合板在弹道冲击及冲击后压缩(compression after impact,CAI)性能方面的表现,结果表明,玄武岩纤维显著提升了混杂层合板的能量吸收能力。采用C扫描、电子显微镜和扫描电镜分析了材料的损伤机制,揭示了材料性能提升的内在机理。另一方面,混杂层合板的初始压缩强度随玄武岩纤维含量的增加而下降。在混杂层合板的能量吸收能力增强与初始压缩强度下降的共同作用下,CAI测试中受损层合板的残余压缩强度呈局部波动趋势。研究结果可为轻量化、高抗冲击复合材料的结构设计提供指导。
基金This work is supported by the National Natural Science Foundation of China under Grant Nos. 61602403 and 61402406 and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2015BAH17F01.
文摘In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique） ＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling） ＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.
