Predicting Quality Attributes via Machine-Learning Algorithms
نویسندگان
چکیده
Software metrics provide quantitative means to control the software development and the quality of software products. Getting a set of valid and useful metrics is not only a matter of definition; the entire process includes, among other steps, theoretical and empirical validation of theses metrics to assure their utility. This work is about empirical validation of object-oriented metrics via machine learning algorithms; it aims at empirically verify the relationships between object-oriented design decisions and three quality attributes: change impact, fault-proneness, and, reusability. Several algorithms, belonging to various machine learning approaches, are selected and run on software data collected from medium size applications.
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تاریخ انتشار 2006