TY - GEN
T1 - Improving faulty interaction localization using logistic regression
AU - Nishiura, Kinari
AU - Choi, Eun Hye
AU - Mizuno, Osamu
N1 - Funding Information:
This work is partly supported by JSPS KAKENHI Grant Number 16K12415.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/11
Y1 - 2017/8/11
N2 - Combinatorial testing is a widely used technique to detect failures caused by interactions of system under test (SUT) parameters. Faulty interaction localization (FIL) is a problem to locate parameter-value combinations that trigger failures from combinatorial test cases and their testing results. FIL is important for debugging, but is expensive for large test suites and SUTs since the number of candidates of faulty interactions increases exponentially with the number of parameters and the size of interactions. To address this problem, this paper proposes a method employing logistic regression. The proposed FIL based on Regression coefficients Of loGistic regression analysis (called FROG) computes the suspiciousness of each parameter-value combination to be included in a faulty interaction from its corresponding regression coefficient. We evaluate the proposed method by applying FROG to combinatorial t-way test cases (2 ≤ t ≤ 4) for real application SUT models, e.g. TCAS, GCC, and Apache. Our experiment results show that FROG can effectively locate faulty interactions injected while efficiently reducing the number of candidates of potential faulty interactions to be checked.
AB - Combinatorial testing is a widely used technique to detect failures caused by interactions of system under test (SUT) parameters. Faulty interaction localization (FIL) is a problem to locate parameter-value combinations that trigger failures from combinatorial test cases and their testing results. FIL is important for debugging, but is expensive for large test suites and SUTs since the number of candidates of faulty interactions increases exponentially with the number of parameters and the size of interactions. To address this problem, this paper proposes a method employing logistic regression. The proposed FIL based on Regression coefficients Of loGistic regression analysis (called FROG) computes the suspiciousness of each parameter-value combination to be included in a faulty interaction from its corresponding regression coefficient. We evaluate the proposed method by applying FROG to combinatorial t-way test cases (2 ≤ t ≤ 4) for real application SUT models, e.g. TCAS, GCC, and Apache. Our experiment results show that FROG can effectively locate faulty interactions injected while efficiently reducing the number of candidates of potential faulty interactions to be checked.
KW - Combinatorial testing
KW - Faulty interaction
KW - Faulty interaction localization
KW - Logistic regression
KW - Regression coefficient
KW - T-way testing
UR - http://www.scopus.com/inward/record.url?scp=85029448281&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029448281&partnerID=8YFLogxK
U2 - 10.1109/QRS.2017.24
DO - 10.1109/QRS.2017.24
M3 - Conference contribution
AN - SCOPUS:85029448281
T3 - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017
SP - 138
EP - 149
BT - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Software Quality, Reliability and Security, QRS 2017
Y2 - 25 July 2017 through 29 July 2017
ER -