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Searching for rules to detect defective modules: A subgroup discovery approach

Opened Access Searching for rules to detect defective modules: A subgroup discovery approach

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Autor: Rodríguez, Daniel
Ruíz, Roberto
Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2012
Publicado en: Information Sciences, 191, 14-30.
Tipo de documento: Artículo
Resumen: Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsistency, redundancy) not all classification algorithms are capable of dealing with this task conveniently. To deal with these problems, Subgroup Discovery (SD) algorithms can be used to find groups of statistically different data given a property of interest. We propose EDER-SD (Evolutionary Decision Rules for Subgroup Discovery), a SD algorithm based on evolutionary computation that induces rules describing only fault-prone modules. The rules are a well-known model representation that can be easily understood and applied by project managers and quality engineers. Thus, rules can help t...
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Cita: Rodríguez, D., Ruíz, R., Riquelme Santos, J.C. y Aguilar Ruiz, J.S. (2012). Searching for rules to detect defective modules: A subgroup discovery approach. Information Sciences, 191, 14-30.
Tamaño: 516.7Kb
Formato: PDF

URI: http://hdl.handle.net/11441/43445

DOI: http://dx.doi.org/10.1016/j.ins.2011.01.039

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