Ponencia
Searching for Rules to find Defective Modules in Unbalanced Data Sets
Autor/es | Rodríguez García, Daniel
Riquelme Santos, José Cristóbal ![]() ![]() ![]() ![]() ![]() ![]() ![]() Ruiz Sánchez, Roberto Aguilar Ruiz, Jesús Salvador |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2009-05 |
Fecha de depósito | 2023-05-09 |
Publicado en |
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ISBN/ISSN | 978-0-7695-3675-0 |
Resumen | The characterisation of defective modules in software engineering remains a challenge. In this work, we use data mining techniques to search for rules that indicate modules with a high probability of being defective. Using ... The characterisation of defective modules in software engineering remains a challenge. In this work, we use data mining techniques to search for rules that indicate modules with a high probability of being defective. Using data sets from the PROMISE repository, we first applied feature selection (attribute selection) to work only with those attributes from the data sets capable of predicting defective modules. With the reduced data set, a genetic algorithm is used to search for rules characterising modules with a high probability of being defective. This algorithm overcomes the problem of unbalanced data sets where the number of non defective samples in the data set highly outnumbers the defective ones |
Agencias financiadoras | Ministerio de Ciencia Y Tecnología (MCYT). España |
Identificador del proyecto | TIN2007-68084-C02-00
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Cita | Rodríguez García, D., Riquelme Santos, J.C., Ruiz Sánchez, R. y Aguilar Ruiz, J.S. (2009). Searching for Rules to find Defective Modules in Unbalanced Data Sets. En 1st International Symposium on Search Based Software Engineering (89-92), Windsor, UK: IEEE Xplore. |
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