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Pinpoint Minimal Failure-Inducing Mode using Itemset Mining under Constraints

Volume 14, Number 6, June 2018, pp. 1300-1307
DOI: 10.23940/ijpe.18.06.p21.13001307

Yong Wanga, Liangfen Weib, Yuan Yaoa, Zhiqiu Huangc, Yong Lic, Bingwu Fangc, and Weiwei Lic

aSchool of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China
bDepartment of Computer Engineering, Anhui Sanlian University, Heifei, 230601, China
cKey Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, China, 210000

(Submitted on March 21, 2018; Revised on April 27, 2018; Accepted on May 28, 2018)


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.


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