SWPM: An Incremental Fault Localization Algorithm Based on Sliding Window with Preprocessing Mechanism

Abstract

Most fault localization techniques are based on time windows. The sizes of time windows impact on the accuracy of fault localization greatly. This paper takes weighted bipartite graph as fault propagation model and proposes a heuristic fault localization approach based on sliding window with preprocessing mechanism (SWPM) to alleviate the shortcomings. First, SWPM defines the concept of symptom extension ratio and partitions observed symptoms into three segments: analyzed segment, analyzing segment, preprocessing segment. Then it determines the most probable fault set through incrementally computing Bayesian suspected degree (BSD) of the three segments and combining their results. Simulations show that the algorithm can reduce the impacts on the accuracy affected by improper window sizes. The algorithm which has a polynomial computational complexity can be applied to large scale communication network.