Improving faulty interaction localization using logistic regression

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.