S
oftware testing is complex and time consuming. One way to
reduce the effort associated with testing is to generate test data automatically.
This thesis is divided into three parts. In the first part a
mixed-integer constraint solver developed by Gupta et. al is studied. The
solver, referred to as the Unified Numerical Approach (una), is an important
part of their generator and it is responsible for solving equation
systems that correspond to the program path currently under test.
In this thesis it is shown that, in contrast to traditional optimization
methods, the una is not bounded by the size of the solved equation
system. Instead, it depends on how the system is composed. That is, even
for very simple systems consisting of one variable we can easily get more
than a thousand iterations. It is also shown that the una is not complete,
that is, it does not always find a mixed-integer solution when there is one.
It is found that a better approach is to use a traditional optimization
method, like the simplex method in combination with branch-and-bound
and/or a cutting-plane algorithm as a constraint solver.