This article proposes a novel approach to quantitative software reliability assessment ensuring high interplay coverage for software components and decentralized (sub-)systems. The generation of adequate test cases is based on the measurement of their operational representativeness, stochastic independence and interaction coverage. The underlying multi-objective optimization problem is solved by genetic algorithms. The resulting automatic test case generation supports the derivation of conservative reliability measures as well as high interaction coverage. The practicability of the approach developed is finally demonstrated in the light of an interaction-intensive example.