Though metrics from past test experiences are the
optimum basis for error guessing, these may not be available.
Examples of error prone situations include:
? initialization of data, (e.g., repeat a process to see if data is
properly removed),
? wrong kind of data, (e.g., negative numbers, non-numeric versus
numeric),
? handling of real data, (i.e., test using data created through the
system or real records, because programmers tend to create data that
reflects what they are expecting),
? error management, (e.g., proper prioritization of multiple
errors, clear error messages, proper retention of data when an error is
received, processing continues after an error if it is supposed to),
? calculations, (e.g., hand calculate items for comparison),
? restart/recovery, (i.e., use data that will cause a batch program
to terminate before completion and determine if the restart/recovery
process works properly),
? proper handling of concurrent processes, (i.e., for event driven
applications, test multiple processes concurrently).
