Temporal Specification Mining for Anomaly Analysis
Temporal Specification Mining for Anomaly Analysis
复制标题
用于异常分析的时间规范挖掘
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Chung
中科院分区：
文献类型：
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作者：
Farn Wang;Jung;Chung;Cheng;Chung
我们研究了如何使用规范挖掘技术进行程序异常分析。我们假设输入的是正跟踪(无执行异常)和负跟踪(有执行异常)。然后根据轨迹异常的特点，将轨迹划分为正群和负群，正群包含所有正迹和负群。我们提出了学习有限迹线性时态逻辑中时态性质的方法。我们建议挖掘区分负聚类和正聚类的FLTL性质。我们用来自Google Code和Google Play的5个Android应用程序进行了实验，将图形用户界面事件和崩溃的痕迹作为目标异常。具有高支持度或置信度的FLTL属性报告揭示了导致崩溃的图形用户界面跟踪中的时间模式。性能数据还表明，负面痕迹的聚类确实提高了为测试判决预测挖掘有意义的时间属性的准确性。
We investigate how to use specification mining techniques for program anomaly analysis. We assume the input of positive traces (with- out execution anomalies) and negative traces (with execution anomalies). We then partition the traces into the following clusters: a positive cluster that contains all positive traces and some negative clusters according to the characteristics of trace anomalies. We present techniques for learn- ing temporal properties in Linear Temporal Logic with finite trace se- mantics (FLTL). We propose to mine FLTL properties that distinguish the negative clusters from the positive cluster. We experiment with 5 Android applications from Google Code and Google Play with traces of GUI events and crashes as the target anomaly. The report of FLTL properties with high support or confidence reveal the temporal patterns in GUI traces that cause the crashes. The performance data also shows that the clustering of negative traces indeed enhances the accuracy in mining meaningful temporal properties for test verdict prediction.