基于机器学习的通信软件缺陷预测研究
Research on Defect Prediction of Communication Software Based on Machine Learning
张延磊1
作者信息
- 1. 诺基亚通信系统技术(北京)有限公司,北京 100013
- 折叠
摘要
提出一种基于机器学习的通信软件缺陷预测方法,通过分析历史数据和应用模型预测,在开发阶段提前识别潜在缺陷,提升软件的质量与可靠性.基于通信软件的复杂性及缺陷特征,定义缺陷密度和缺陷率作为评估软件质量的重要指标.采用随机森林算法进行预测,实验结果表明,所提模型在不同规模数据集上的预测准确率均较高,尤其在处理复杂代码模块时表现出良好的稳定性与健壮性.所提方法为通信软件开发过程中的缺陷检测和质量优化提供了有效的技术支持,有助于降低维护成本并提高系统的安全性与可靠性.
Abstract
This article proposes a machine learning based communication software defect prediction method,which aims to identify potential defects in advance during the development stage and improve the quality and reliability of software by analyzing historical data and applying model predictions.In the study,based on the complexity and defect characteristics of communication software,defect density and defect rate were defined as important indicators for evaluating software quality.The random forest algorithm was used for prediction,and the experimental results showed that the model had high prediction accuracy on datasets of different sizes,especially when dealing with complex code modules,demonstrating good stability and robustness.This method provides effective technical support for defect detection and quality optimization in the development process of communication software,helping to reduce maintenance costs and improve system security and reliability.
关键词
机器学习/通信软件/缺陷预测/随机森林Key words
machine learning/communication software/defect prediction/random forest引用本文复制引用
出版年
2024
NETL
NSTL
万方数据