Geribesleme güdümlü adaptif kombinasyonel test etme yaklaşımı

The configuration space of a software system forms a combinatorial space, whose large size generally makes exhaustive testing infeasible. Combinatorial interaction testing (CIT) approaches systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches, such as covering arrays, is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture however that in practice many such behaviors are not actually tested in the presence of what we call masking effects failures perturbing behavior in ways that prevent some intended behaviors from being tested. In this work we present a feedback driven adaptive combinatorial testing approach aimed at detecting and working around masking effects. At each iteration of this approach, we detect potential masking effects, heuristically isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We, furthermore, empirically assess the effectiveness of the proposed approach by using a large widely used open source software system as our subject application