Software maintenance reportedly accounts for much of the total cost associated
with developing software. These costs occur because modifying software is a highly
error-prone task. Changing software to correct faults or add new functionality
can cause existing functionality to regress, introducing new faults. To avoid such
defects, one can re-test software after modifications, a task commonly known as
regression testing.
Regression testing typically involves the re-execution of test cases developed for
previous versions. Re-running all existing test cases, however, is often costly and
sometimes even infeasible due to time and resource constraints. Re-running test
cases that do not exercise changed or change-impacted parts of the program carries
extra cost and gives no benefit. The research community has thus sought ways to
optimize regression testing by lowering the cost of test re-execution while preserving
its effectiveness. To this end, researchers have proposed selecting a subset of test
cases according to a variety of criteria (test case selection) and reordering test cases
for execution to maximize a score function (test case prioritization).
This dissertation presents a novel framework for optimizing regression testing
activities, based on a probabilistic view of regression testing. The proposed framework
is built around predicting the probability that each test case finds faults in the
regression testing phase, and optimizing the test suites accordingly. To predict such
probabilities, we model regression testing using a Bayesian Network (BN), a powerful
probabilistic tool for modeling uncertainty in systems. We build this model using
information measured directly from the software system. Our proposed framework
builds upon the existing research in this area in many ways. First, our framework
incorporates different information extracted from software into one model, which
helps reduce uncertainty by using more of the available information, and enables
better modeling of the system. Moreover, our framework provides flexibility by
enabling a choice of which sources of information to use. Research in software
measurement has proven that dealing with different systems requires different techniques
and hence requires such flexibility. Using the proposed framework, engineers
can customize their regression testing techniques to fit the characteristics of their
systems using measurements most appropriate to their environment.
We evaluate the performance of our proposed BN-based framework empirically.
Although the framework can help both test case selection and prioritization, we
propose using it primarily as a prioritization technique. We therefore compare our
technique against other prioritization techniques from the literature. Our empirical
evaluation examines a variety of objects and fault types. The results show that the
proposed framework can outperform other techniques on some cases and performs
comparably on the others.
In sum, this thesis introduces a novel Bayesian framework for optimizing regression
testing and shows that the proposed framework can help testers improve the
cost effectiveness of their regression testing tasks.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/3936 |
| Date | January 2008 |
| Creators | Mir arabbaygi, Siavash |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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