Title: A hybrid grid-based many-objective optimisation algorithm for software defect prediction

Authors: Junyan Wang

Addresses: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China

Abstract: How to apply limited test resources to detect error module is one of the challenges of software defect prediction problem. To solve the problem, a many-objective software defect prediction model is proposed by considering the probability of detection and false alarm rate, the Balance value and F-measure as defect prediction objectives. At the same time, a hybrid grid-based many-objective optimisation algorithm is designed to solve the model. In the designed algorithm, the adaptive dominant region operator is introduced into the grid-based many-objective optimisation algorithm to improve the performance of algorithm in balancing dynamically the convergence and diversity of population. The simulation results show that the proposed algorithm has better performance in solving many-objective the software defect prediction problem.

Keywords: software defect prediction problem; the probability of detection; false alarm rate; adaptive dominant region operator; convergence; diversity; many-objective optimisation.

DOI: 10.1504/IJCSM.2020.112675

International Journal of Computing Science and Mathematics, 2020 Vol.12 No.4, pp.374 - 384

Received: 03 Mar 2020
Accepted: 16 Apr 2020
Published online: 26 Jan 2021 *

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