System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Finding Defective Software Modules by Means of Data Mining Techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Riquelme, J.C. ; Dept. de Lenguajes y Sist., Univ. de Sevilla, Sevilla, Spain ; Ruiz, R. ; Rodriguez, D. ; Aguilar-Ruiz, J.S.

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 repository 1, 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.

Published in:

Latin America Transactions, IEEE (Revista IEEE America Latina)  (Volume:7 ,  Issue: 3 )