For a space compactor, degradation of fault detection capability caused by the masking effects from unknown values is much more serious than that caused by error masking (i.e. aliasing). In this paper, we first propose a mathematical framework to estimate the percentage of observable responses under unknown-induced masking for a space compactor. We further develop a prediction scheme which can correlate the percentage of observable responses with the modeled-fault coverage and with a n-detection metric for a given test set. As a result, the quality of a space compactor can be measured directly based on its test quality, instead of based on indirect metrics such as the number of tolerated unknowns or the aliasing probability. With the prediction scheme above, we propose a construction flow for space compactors to achieve the desired level of test quality while maximizing the compaction ratio.