Analysis of Error Processes in Computer Software
Abstract
A non-homogeneous Poisson process is
used to model the occurrence of errors detected
during functional testing of command and control
software. The parameters of the detection process
are estimated by using a combination of maximum
likelihood and weighted least squares methods.
Once parameter estimates are obtained, forecasts
can be made of cumulative number of detected
errors. Forecasting equations of cumulative corrected
errors, errors detected but not corrected,
and the time required to detect or correct a specified
number of errors, are derived from the detected
error function. The various forecasts provide
decision aids for managing software testing
activities. Naval Tactical Data System software
error data are used to evaluate several variations
~of the forecasting methodology and to test the
accuracy of the forecasting equations. Because of
changes which take place in the actual detected
error process, it was found that recent error observations
are more representative of future error
occurrences than are early observations. Based on
a limited test of the model, acceptable accuracy
was obtained when using the preferred forecasting
method.
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