Adjusting Software Failure Rates that are Estimated from Test Data

Software test environments are almost always different from field environments. Using test data exclusively to estimate a field failure rate will usually give a pessimistic estimate. In this paper, we extend a previously published calibration methodology for adjusting the failure rate estimate obtained from analyzing test data. In addition to scaling the estimated failure rate of a fault, we propose scaling the estimated number of residual faults as well. We also derive likelihood ratio tests to formally determine (from previous releases of the software) if test and field environments are significantly different. We apply our new results to two telecommunications case studies.