Hybrid algorithm for two-objective software defect prediction problem. (2017)

• Record Type: Journal Article
• Authors: Rong, Xiaotao; Cui, Zhihua
• Is Part Of: International journal of innovative computing and applications. Volume 8:Number 4(2017)
• Page Start: 207
• Page End: 212
• Publication Date: 2017
• Subjects: support vector machine -- SVM -- software defect prediction -- multi-objective cuckoo search -- MOCS; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.