  Automated testing tools typically create test cases that are different from
what human testers create. This often makes the tools less effective, the
created tests harder to understand, and thus results in tools providing less
support to human testers. Here, we propose a framework based on cognitive
science and, in particular, an analysis of approaches to problem-solving, for
identifying cognitive processes of testers. The framework helps map test design
steps and criteria used in human test activities and thus to better understand
how effective human testers perform their tasks. Ultimately, our goal is to be
able to mimic how humans create test cases and thus to design more human-like
automated test generation systems. We posit that such systems can better
augment and support testers in a way that is meaningful to them.
