  Dedicated surveys searching for Fast Radio Bursts (FRBs) are subject to
selection effects which bias the observed population of events. Software
injection systems are one method of correcting for these biases by injecting a
mock population of synthetic FRBs directly into the realtime search pipeline.
The injected population may then be used to map intrinsic burst properties onto
an expected signal-to-noise ratio (SNR), so long as telescope characteristics
such as the beam model and calibration factors are properly accounted for. This
paper presents an injection system developed for the Canadian Hydrogen
Intensity Mapping Experiment Fast Radio Burst project (CHIME/FRB). The system
was tested to ensure high detection efficiency, and the pulse calibration
method was verified. Using an injection population of ~85,000 synthetic FRBs,
we found that the correlation between fluence and SNR for injected FRBs was
consistent with that of CHIME/FRB detections in the first CHIME/FRB catalog. We
also noted that the sensitivity of the telescope varied strongly as a function
of the broadened burst width, but not as a function of the dispersion measure.
We conclude that some of the machine-learning based Radio Frequency
Interference (RFI) mitigation methods used by CHIME/FRB can be re-trained using
injection data to increase sensitivity to wide events, and that planned
upgrades to the presented injection system will allow for determining a more
accurate CHIME/FRB selection function in the near future.
