  Detecting fast radio bursts (FRBs) requires software pipelines to search for
dispersed single pulses of emission in radio telescope data. In order to enable
an unbiased estimation of the underlying FRB population, it is important to
understand the algorithm efficiency with respect to the search parameter space
and thus the survey completeness. The Fast Real-time Engine for Dedispersing
Amplitudes (FREDDA) search pipeline is a single pulse detection pipeline
designed to identify radio pulses over a large range of dispersion measures
(DM) with low latency. It is used on the Australian Square Kilometre Array
Pathfinder (ASKAP) for the Commensal Real-time ASKAP Fast Transients (CRAFT)
project . We utilise simulated single pulses in the low- and high-frequency
observation bands of ASKAP to analyse the performance of the pipeline and infer
the underlying FRB population. The simulation explores the Signal-to-Noise
Ratio (S/N) recovery as a function of DM and the temporal duration of FRB
pulses in comparison to injected values. The effects of intra-channel
broadening caused by dispersion are also carefully studied in this work using
control datasets. Our results show that for Gaussian-like single pulses, $> 85
\%$ of the injected signal is recovered by pipelines such as FREDDA at DM <
3000 $\mathrm{pc\ cm^{-3}}$ using standard boxcar filters compared to an ideal
incoherent dedispersion match filter. Further calculations with sensitivity
implies at least $\sim 10\%$ of FRBs in a Euclidean universe at target
sensitivity will be missed by FREDDA and HEIMDALL, another common pipeline, in
ideal radio environments at 1.1 GHz.
