Hybrid algorithm for two-objective software defect prediction problem Online publication date: Mon, 27-Nov-2017
by Xiaotao Rong; Zhihua Cui
International Journal of Innovative Computing and Applications (IJICA), Vol. 8, No. 4, 2017
Abstract: Static software defect prediction problem is one crucial problem in software test, to measure the performance, several indexes are introduced. In this paper, a two-objective software defect prediction model is employed, while probability of false alarm rate and probability of detection are taken as two objectives. To solve this model, one hybrid algorithm combined with support vector machine (SVM) and cuckoo search algorithm is designed. SVM is one general tool for this problem, and the performance is significantly influenced by two parameters. To provide a good classification results, one multi-objective cuckoo search algorithm is designed to optimise these two parameters. In this algorithm, the global best position is extended to be one collection including all non-dominated solutions, and the local search manner is changed to increase the local search speed. Simulation results show our hybrid algorithm is effective.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Innovative Computing and Applications (IJICA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook