Object-oriented systems that undergo repeated modifications commonly
endure a loss of quality and design decay. This problem is often
remedied by applying refactorings. Refactoring is one of the most
important and commonly used techniques to improve the quality of the
code by eliminating redundancy and reducing complexity; frequently
refactored code is believed to be easier to understand, maintain and
test. Object-oriented metrics provide an easy means to extract useful
and measurable information about the structure of a software
system. Metrics have been used to identify refactoring opportunities,
detect refactorings that have previously been applied and gauge
quality improvements after the application of refactorings.
This thesis provides an in-depth analytical study of the relationship
between metrics and refactorings. For this purpose we analyzed 136
versions of 4 different open source projects. We used
RefactoringCrawler, an automatic refactoring detection tool to
identify refactorings and then analyzed various metrics to study
whether metrics can be used to (1) reliably identify refactoring
opportunities, (2) detect refactorings that were previously
applied, and (3) estimate the impact of refactoring on software
quality.
In conclusion, our study showed that metrics cannot be reliably used
to either identify refactoring opportunities or detect
refactorings. It is very difficult to use metrics to estimate the
impact of refactoring, however studying the evolution of metrics at a
system level indicates that refactoring does improve software quality
and reduce complexity. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2009-05-147 |
| Date | 03 September 2009 |
| Creators | Iyer, Suchitra S. |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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