Fault location method of complex software based on community mining

Rui Li, Minyan Lu, Qian Ye
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Abstract

The explosion by amount of codes as well as the swelling logic complexity have stifled the performance of the traditional fault-location methods since the resource adopted during this process is unacceptable. Under such a situation, a scheme to locate the faults in complex software more effectively has been proposed in this paper based on networks community theory. First, on the base of establishing the dependency network of every module in software, dependency relationships between various community blocks can be obtained by applying clustering algorithms to mine the community structure. Second, with status reports generated by monitoring-codes planted into the center node of every community, we can distinguish whether communities work in a normal way, thus the fault searching area can be shrunk smaller by repeating the steps above. Third, once the searching area has been shrunk to several modules, the traditional methods like Tarantula is eligible for finding the fault since the number of suspected codes has been compressed to an acceptable level. At last, the experiment by applying the method we come up has been conducted and the statistic has been collected as well as analyzed to compare with the main-trend solutions, during which the speed superiorities and acceptable accuracy of our method have been confirmed.
基于群体挖掘的复杂软件故障定位方法
由于编码量的爆炸式增长和逻辑复杂度的不断膨胀,传统的故障定位方法所使用的资源是不可接受的。在这种情况下,本文提出了一种基于网络社区理论的复杂软件故障更有效定位方案。首先,在建立软件各模块依赖网络的基础上,利用聚类算法挖掘社区结构,得到各社区块之间的依赖关系。其次,将监控代码生成的状态报告植入到每个社区的中心节点,可以区分社区是否正常工作,从而通过重复上述步骤缩小故障搜索区域。第三,一旦搜索区域缩小到几个模块,由于可疑代码的数量被压缩到可接受的水平,传统的方法如Tarantula就有资格找到故障。最后,应用本文提出的方法进行了实验,并进行了统计和分析,与主流解决方案进行了比较,验证了本文方法的速度优势和可接受的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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