  Dedicated surveys using different detection pipelines are being carried out
at multiple observatories to find more Fast Radio Bursts (FRBs). Understanding
the efficiency of detection algorithms and the survey completeness function is
important to enable unbiased estimation of the underlying FRB population
properties. One method to achieve end-to-end testing of the system is by
injecting mock FRBs in the live data-stream and searching for them blindly.
Mock FRB injection is particularly effective for machine-learning-based
classifiers, for which analytic characterisation is impractical. We describe a
first-of-its-kind implementation of a real-time mock FRB injection system at
the upgraded Molonglo Observatory Synthesis Telescope (UTMOST) and present our
results for a set of 20,000 mock FRB injections. The injections have yielded
clear insight into the detection efficiencies and have provided a survey
completeness function for pulse width, fluence and DM. Mock FRBs are recovered
with uniform efficiency over the full range of injected DMs, however the
recovery fraction is found to be a strong function of the width and
Signal-to-Noise (SNR). For low widths ($\lesssim 20$ ms) and high SNR
($\gtrsim$ 9) the recovery is highly effective with recovery fractions
exceeding 90%. We find that the presence of radio frequency interference causes
the recovered SNR values to be systematically lower by up to 20% compared to
the injected values. We find that wider FRBs become increasingly hard to
recover for the machine-learning-based classifier employed at UTMOST. We
encourage other observatories to implement live injection set-ups for similar
testing of their surveys.
