Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison

Abstract

Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. As a biological heuristic algorithm, BAS (Beetle Antennae Search Algorithm) has fast convergence speed and is easy to implement. ABC (Artificial Bee Colony Algorithm) is better in optimization and has strong robustness. In this paper, the BAS-ABC hybrid algorithm is proposed by mixing the two algorithms and the goal of the proposed method is to improve the convergence and stability of the algorithm. Five datasets were used to carry out the experiments, and the data results showed that the hybrid algorithm was more accurate than the single algorithm, with stronger convergence and stability, so it was more suitable for parameter estimation of the software reliability model. Meanwhile, this paper implemented the comparison between hybrid BAS + ABC and hybrid PSO + SSA, and the result shows that BAS + ABC has better performance both in convergence and stability. The comparison result shows the strong ability in estimation and prediction of software defects hybrid BAS and ABC.