Evaluation of GP Model for Software Reliability
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- @InProceedings{Paramasivam:2009:ICSPS,
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author = "S. Paramasivam and M. Kumaran",
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title = "Evaluation of GP Model for Software Reliability",
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booktitle = "2009 International Conference on Signal Processing
Systems",
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year = "2009",
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month = may,
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pages = "758--761",
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keywords = "genetic algorithms, genetic programming, SBSE, GP
model, fault count data prediction, industrial project,
software metrics, software quality, software
reliability growth model, software metrics, software
quality, software reliability",
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DOI = "
doi:10.1109/ICSPS.2009.104",
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abstract = "There has been a number of software reliability growth
models (SRGMs) proposed in literature. Due to several
reasons, such as violation of models' assumptions and
complexity of models, the practitioners face
difficulties in knowing which models to apply in
practice. This paper presents a comparative evaluation
of traditional models and use of genetic programming
(GP) for modeling software reliability growth based on
weekly fault count data of three different industrial
projects. The motivation of using a GP approach is its
ability to evolve a model based entirely on prior data
without the need of making underlying assumptions. The
results show the strengths of using GP for predicting
fault count.",
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notes = "Also known as \cite{5166890}",
- }
Genetic Programming entries for
S Paramasivam
M Kumaran
Citations