Evaluation of GP Model for Software Reliability

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@InProceedings{Paramasivam:2009:ICSPS,

• author = "S. Paramasivam and M. Kumaran",
• title = "Evaluation of GP Model for Software Reliability",
• booktitle = "2009 International Conference on Signal Processing Systems",
• year = "2009",
• month = may,
• pages = "758--761",
• 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",
• DOI = "doi:10.1109/ICSPS.2009.104",
• 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.",
• notes = "Also known as \cite{5166890}",

}

Genetic Programming entries for S Paramasivam M Kumaran

Citations