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基于混合群智能和深度学习的软件缺陷预测。

Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning.

机构信息

School of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Reliability and Systems Engineering Open Group, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

出版信息

Comput Intell Neurosci. 2021 Dec 28;2021:4997459. doi: 10.1155/2021/4997459. eCollection 2021.

DOI:10.1155/2021/4997459
PMID:34992647
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8727112/
Abstract

In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. According to the respective advantages and disadvantages of the particle swarm algorithm and the wolf swarm algorithm, the two algorithms are mixed to realize the complementary advantages of the algorithms. At the same time, the hybrid algorithm is used in the search of model hyperparameter optimization, the loss function of the model is used as the fitness function, and the collaborative search ability of the swarm intelligence population is used to find the global optimal solution in multiple local solution spaces. Through the analysis of the experimental results of six data sets, compared with the traditional hyperparameter optimization method and a single swarm intelligence algorithm, the model using the hybrid algorithm has higher and better indicators. And, under the processing of the autoencoder, the performance of the model has been further improved.

摘要

为提高软件质量和测试效率，本文基于深度学习实现软件缺陷预测。根据粒子群算法和狼群算法各自的优缺点，将两种算法混合，实现算法优势互补。同时，将混合算法应用于模型超参数优化的搜索中，将模型的损失函数作为适应度函数，利用群体智能种群的协同搜索能力在多个局部解空间中寻找全局最优解。通过对六个数据集的实验结果进行分析，与传统的超参数优化方法和单一的群智能算法相比，使用混合算法的模型具有更高、更好的指标。并且，经过自动编码器的处理，模型的性能得到了进一步的提高。

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