  Test generation and test data selection are difficult tasks for model based
testing. Tests for a program can be meld to a test suite. A lot of research is
done to quantify the quality and improve a test suite. Code coverage metrics
estimate the quality of a test suite. This quality is fine, if the code
coverage value is high or 100%. Unfortunately it might be impossible to achieve
100% code coverage because of dead code for example. There is a gap between the
feasible and theoretical maximal possible code coverage value. Our review of
the research indicates, none of current research is concerned with exact gap
computation. This paper presents a framework to compute such gaps exactly in an
ISO-C compatible semantic and similar languages. We describe an efficient
approximation of the gap in all the other cases. Thus, a tester can decide if
more tests might be able or necessary to achieve better coverage.
