Modeling Software Reliability Growth with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7954

@InProceedings{conf/issre/CostaVPS05,

• title = "Modeling Software Reliability Growth with Genetic Programming",
• author = "Eduardo Oliveira Costa and Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo and Gustavo A. {de Souza}",
• year = "2005",
• pages = "171--180",
• booktitle = "16th International Symposium on Software Reliability Engineering (ISSRE 2005)",
• address = "Chicago, IL, USA",
• month = "8-11 " # nov,
• publisher = "IEEE Computer Society",
• bibdate = "2006-01-03",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/issre/issre2005.html#CostaVPS05",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-7695-2482-6",
• DOI = "doi:10.1109/ISSRE.2005.29",
• abstract = "Reliability Models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to estimate the reliability growth, however, none of them has proven to perform well considering different project characteristics. In this work, we explore Genetic Programming (GP) as an alternative approach to derive these models. GP is a powerful machine learning technique based on the idea of genetic algorithms and has been acknowledged as a very suitable technique for regression problems. The main motivation to choose GP for this task is its capability of learning from historical data, discovering an equation with different variables and operators. experiment were conducted to confirm this hypotheses and the results were compared with traditional and Neural Network models.",

}

Genetic Programming entries for Eduardo Oliveira Costa Silvia Regina Vergilio Aurora Trinidad Ramirez Pozo Gustavo Antonio de Souza Goll

Citations