With the growing importance of software
on which human lives increasingly depend, the
correctness requirement of th
e underlying software becomes especially critical. However,
the increasing complexities and sizes of modern software sy
stems pose special challenges
on the effectiveness as well as
efficiency of software verif
ication. Two major obstacles
include the quality of test generation in term
s of error detection in software testing and
the state space explosion problem in software formal verification
(
model checking
)
.
In this dissertation, we investigate several hy
brid techniques that explore dynamic (with
program execution), static (without program execution) as well as the synergies of
multiple approaches in software verification fr
om the perspectives of testing and model
checking. For software testing, a new simulatio
n-based internal variable range coverage
metric is proposed with the goal of enhanc
ing the error detection capability of the
generated test data when applied as the targ
et metric. For softwar
e model checking, we
utilize various dynamic analysis methods, su
ch as data mining, swarm intelligence (ant
colony optimization), to extract useful hi
gh-level information fr
om program execution
data. Despite being incomplete, dynamic program execution can still help to uncover
important program structure features and variable correlations. The extracted knowledge,
such as invariants in different forms, prom
ising control flows, etc., is then used to
facilitate code-level program abstraction
(
under-approximation/
over-approximation
)
,
and/or state space partition, which in turn improve the performance of property
verification.