Using Boosting Techniques to Improve Software Reliability Models Based on Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{conf/ictai/CostaPV06,
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title = "Using Boosting Techniques to Improve Software
Reliability Models Based on Genetic Programming",
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author = "Eduardo Oliveira Costa and Aurora Pozo and
Silvia Regina Vergilio",
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year = "2006",
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booktitle = "18th IEEE International Conference on Tools with
Artificial Intelligence (ICTAI'06)",
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pages = "643--650",
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address = "Washington, D.C, USA",
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month = nov # " 13-15",
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publisher = "IEEE Computer Society",
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bibdate = "2007-01-04",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ictai/ictai2006.html#CostaPV06",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICTAI.2006.117",
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abstract = "Software reliability models are used to estimate the
probability of a software fails along the time. They
are fundamental to plan test activities and to ensure
the quality of the software being developed. Two kind
of models are generally used: time or test coverage
based models. In our previous work, we successfully
explored Genetic Programming (GP) to derive reliability
models. However, nowadays Boosting techniques (BT) have
been successfully applied with other Machine Learning
techniques, including GP. BT merge several hypotheses
of the training set to get better results. With the
goal of improving the GP software reliability models,
this work explores the combination GP and BT. The
results show advantages in the use of the proposed
approach.",
- }
Genetic Programming entries for
Eduardo Oliveira Costa
Aurora Trinidad Ramirez Pozo
Silvia Regina Vergilio
Citations