【摘要】 故障定位的目的是帮助程序员寻找引发失效的原因或故障位置,以加快调试过程.故障和失效间的关系往往非常复杂,难以直接描述故障到失效的转化.分析了采用差异分析的方法,提出基于可疑模式,构建故障推理贝叶斯网络,节点由可疑模式及其方法调用者构成;介绍了贝叶斯网络构建算法,各个相关概率的定义及BBN(Bayesian BeliefNetwork)中各个边的条件概率计算公式.基于推理算法,得到包含故障的模块,并计算得到每个模块包含故障的概率.提出评价方法,并进行了实验验证,取得了平均0.761的定准率和0.737的定全率,定位结果良好有应用价值.更多还原

【Abstract】 Fault localization techniques help programmers find out the locations and the causes of the faults and accelerate the debugging process.The relation between the fault and the failure is usually complicated,making it hard to deduce how a fault causes the failure.At present,analysis of variance is broadly used in many recent correlative researches.A Bayesian belief network(BBN) for fault reasoning was constructed based on the suspicious pattern,whose nodes consist of the suspicious pattern and the callers of the methods that constitute the suspicious pattern.The constructing algorithm of the BBN,the correlative probabilities,and the formula for the conditional probabilities of each arc of the BBN were defined.A reasoning algorithm based on the BBN was proposed,through which the faulty module can be found and the probability for each module containing the fault can be calculated.An evaluation method was proposed.Experiments were executed to evaluation the fault localization technique.The data demonstrated that 0.761 in accuracy and 0.737 in recall on average were achieved by this technique.It is very effective in fault localization and has high practical value.更多还原