Pinpoint Minimal Failure-Inducing Mode using Itemset Mining under Constraints

doi:10.23940/ijpe.18.06.p21.13001307

Abstract

Abstract:

A minimal failure-inducing mode (MFM) based on a t-way combinatorial test set and its test results can help programmers identify root causes of failures that are triggered by combination bugs. However, an MFM for systems containing many parameters may be affected by masking effects to result in coincidences correct in practice, which makes pinpointing MFS more difficult. An approach for pinpointing MFM and an iterative framework are proposed. The identifying MFM approach first collects combinatorial test cases and their testing results, then mines the frequent itemset (suspicious MFM) in failed test cases, and finally computes suspiciousness for each MFM belonged to close pattern via contrasting frequency in failed test cases and successful test cases. Through the iterative framework, MFM is pinpointed until a certain stopping criterion is satisfied. Preliminary results of simulation experiments show that this approach is effective.

Submitted on March 21, 2018; Revised on April 27, 2018; Accepted on May 28, 2018
References: 13

Yong Wang, Liangfen Wei, Yuan Yao, Zhiqiu Huang, Yong Li, Bingwu Fang, and Weiwei Li. Pinpoint Minimal Failure-Inducing Mode using Itemset Mining under Constraints [J]. Int J Performability Eng, 2018, 14(6): 1300-1307.

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References 0

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