Exploring Genetic Programming and Boosting Techniques to Model Software Reliability
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- @Article{Costa:2007:ieeeTR,
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author = "Eduardo Oliveira Costa and
Gustavo Alexandre {de Souza} and Aurora Trinidad Ramirez Pozo and
Silvia Regina Vergilio",
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title = "Exploring Genetic Programming and Boosting Techniques
to Model Software Reliability",
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journal = "IEEE Transactions on Reliability",
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year = "2007",
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volume = "56",
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number = "3",
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pages = "422--434",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Fault
prediction, machine learning techniques, software
reliability models",
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DOI = "doi:10.1109/TR.2007.903269",
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ISSN = "0018-9529",
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abstract = "Software reliability models are used to estimate the
probability that a software fails at a given time. They
are fundamental to plan test activities, and to ensure
the quality of the software being developed. Each
project has a different reliability growth behaviour,
and although several different models have been
proposed to estimate the reliability growth, none has
proven to perform well considering different project
characteristics. Because of this, some authors have
introduced the use of Machine Learning techniques, such
as neural networks, to obtain software reliability
models. Neural network-based models, however, are not
easily interpreted, and other techniques could be
explored. In this paper, we explore an approach based
on Genetic Programming, and also propose the use of
Boosting techniques to improve performance. We conduct
experiments with reliability models based on time, and
on test coverage. The obtained results show some
advantages of the introduced approach. The models adapt
better to the reliability curve, and can be used in
projects with different characteristics.",
- }
Genetic Programming entries for
Eduardo Oliveira Costa
Gustavo Antonio de Souza Goll
Aurora Trinidad Ramirez Pozo
Silvia Regina Vergilio
Citations