- author = "Yi Liu",
- title = "Software Reliability Engineering with Genetic Programming",
- school = "Computer Science, Florida Atlantic University",
- year = "2003",
- address = "Boca Raton, Florida, USA",
- month = aug,
- keywords = "genetic algorithms, genetic programming, SBSE",
-
URL = "
http://search.proquest.com/docview/305323504",
- broken = "http://digitool.fcla.edu/dtl_publish/25/12047.html",
- size = "243 pages",
- isbn13 = "978-0-496-42656-0",
-
abstract = "Software reliability engineering plays a vital role in
managing and controlling software quality. As an
important method of software reliability engineering,
software quality estimation modelling is useful in
defining a cost-effective strategy to achieve a
reliable software system. By predicting the faults in a
software system, the software quality models can
identify high-risk modules, and thus, these high-risk
modules can be targeted for reliability enhancements.
Strictly speaking, software quality modeling not only
aims at lowering the misclassification rate, but also
takes into account the costs of different
misclassifications and the available resources of a
project. As a new search-based algorithm, Genetic
Programming (GP) can build a model without assuming the
size, shape, or structure of a model. It can flexibly
tailor the fitness functions to the objectives chosen
by the customers. Moreover, it can optimise several
objectives simultaneously in the modelling process, and
thus, a set of multi-objective optimisation solutions
can be obtained. This research focuses on building
software quality estimation models using GP. Several
GP-based models of predicting the class membership of
each software module and ranking the modules by a
quality factor were proposed. The first model of
categorising the modules into fault-prone or not
fault-prone was proposed by considering the
distinguished features of the software quality
classification task and GP. The second model provided
quality-based ranking information for fault-prone
modules. A decision tree-based software classification
model was also proposed by considering accuracy and
simplicity simultaneously. This new technique provides
a new multi-objective optimization algorithm to build
decision trees for real-world engineering problems, in
which several trade-off objectives usually have to be
taken into account at the same time. The fourth model
was built to find multi-objective optimisation
solutions by considering both the expected cost of
misclassification and available resources. Also, a new
goal-oriented technique of building module-order models
was proposed by directly optimizing several goals
chosen by project analysts. The issues of
GP , bloating and overfitting, were also addressed in our research. Data were collected from three industrial projects, and applied to validate the performance of the models. Results indicate that our proposed methods can achieve useful performance results. Moreover, some proposed methods can simultaneously optimize several different objectives of a software project management team.", -
notes = "www.fau.edu/dsr/researchnews0903.pdf page 6 Major
Professor: Taghi M. Khoshgoftaar
UMI 3095028",
