Artículo
Finding Defective Software Modules by Means of Data Mining Techniques
Autor/es | Riquelme Santos, José Cristóbal
Ruiz Sánchez, Roberto Aguilar Ruiz, Jesús Salvador Rodríguez García, Daniel |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2009-07 |
Fecha de depósito | 2023-05-04 |
Publicado en |
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Resumen | The characterization 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. ... The characterization 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 datasets from the PROMISE repository1, we first applied feature selection to work only with those attributes from the datasets capable of predicting defective modules. Then, a genetic algorithm search for rules characterising subgroups with a high probability of being defective. This algorithm overcomes the problem of unbalanced datasets where the number of non-defective samples in the dataset highly outnumbers the defective ones. |
Agencias financiadoras | Comisión Interministerial de Ciencia y Tecnología (CICYT). España |
Identificador del proyecto | TIN2007-68084-C02-00 |
Cita | Riquelme Santos, J.C., Ruiz Sánchez, R., Aguilar Ruiz, J.S. y Rodríguez García, D. (2009). Finding Defective Software Modules by Means of Data Mining Techniques. IEEE Latin America Transactions, 7 (3), 377-382. https://doi.org/10.1109/TLA.2009.5336637. |
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