When failures occur during software testing, automated software fault localization helps to diagnose their root causes and identify the defective components of a program to support debugging. Diagnosis is carried out by selecting test cases in such way that their pass or fail information will narrow down the set of fault candidates, and, eventually, pinpoint the root cause. An essential ingredient of effective and efficient fault localization is the knowledge about the intermittency of occurring failures, i.e., the rate at which defective components of a program will exhibit failures. In current fault localization processes, intermittency is either ignored completely, or merely estimated a posteriori as part of the diagnosis. In this paper, we study the reduction in testing and diagnosis effort when intermittency is known a priori. We deduce intermittency from testability, following the propagation-infection-execution (PIE) approach. Experiments with synthetic and real programs suggest significant improvement in the combined testing and diagnosis process. When compared to the next best technique, testability-based intermittency information reduces the average number of tests required to reach the same diagnosis quality by 55%, and provides an effort reduction for fault localization of 30% for the same testing effort.