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 them to develop software systems that can be justifiably trusted. Contrary to
other approaches in SD, our algorithm has the advantage of working with continuous variables
as the conditions of the rules are defined using intervals. We describe the rules
obtained by applying our algorithm to seven publicly available datasets from the PROMISE
repository showing that they are capable of characterising subgroups of fault-prone modules.
We also compare our results with three other well known SD algorithms and the
EDER-SD algorithm performs well in most cases.Ministerio de Educación y Ciencia TIN2007-68084-C02-00Ministerio de Educación y Ciencia TIN2010-21715-C02-0
