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for searching and determining a near to optimal set
of test cases according to the data flow criterion.
Based on previous work, we extended a test case
generation system by integrating and exploiting the
functionality of the two graphs. Control flow graph
assists in dynamic code coverage, i.e. determines
executed code on the control flow on the fly. Data
flow graphs assist in extracting the data flow paths
according to the All_DU_Paths data flow criterion.
A test case generator uses the control flow graph to
identify the executed part of an All_DU_Path and
evaluate the test case. The test case generator
employs a genetic algorithm which uses the
feedback to adapt its behaviour and hence to come
closer to the appropriate set of test cases. In this
work, we also addressed the challenge of the
composite gene mutation and we proposed the
switch-mutation operation, which can mutate not
only the gene (i.e. a variable of the test case) as a
complete unit but also an element of the gene (i.e. an
element of an array which is treated as an input
variable). Experimental results revealed the
efficiency of our approach over a pool of standard
programs. These results indicated better
performance compared to similar studies, both in
terms of testing coverage and execution time, the
latter being calculated in terms of evaluation runs.
Currently we are carrying out more experiments
with larger and more complex programs which can
assess the performance of our framework in more
realistic environments. Future work will consider the
extension of the framework to support control flow
graph slicing so as to identify faulty parts of the
code. Future work will consider the modification of
the new mutation operator to support composite
genes of objects, i.e. when one or more objects are
given as input variables to a testing unit. In this case,
the test case generator may need to exercise the
following: (i) the object itself, e.g. by generating a
new object, (ii) the state of the object, e.g. by calling
some methods of the object before assigning it to the
gene and (iii) a descendent object (following
heritage tree) and hence the late binding, by creating
an object of a sub-class and up-casting it to the
target object. Finally, we plan to investigate the
incorporation of a new type of graph which will be
able to capture both the control flow graph and the
object oriented features.
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