摘要
软件开发日趋多样化、复杂化,在此过程中,分布式系统得到了普遍应用。然而,分布式系统的复杂性也带来了高缺陷率的问题,这对软件质量维护提出了更高的要求。本文详细探讨了基于深度学习的分布式软件缺陷预测模型的开发,包括模型设计、数据预处理与特征提取方法、模型训练以及性能评估。本文研究旨在提高分布式软件开发中的缺陷检测效率和准确性,为软件工程领域提供一种有效的技术支持和解决方案。
Software development is becoming increasingly diverse and complex,and in this process,distributed systems have been widely applied.But,the complexity of distributed systems also brings about the problem of high defect rates,which puts higher demands on software quality maintenance.This article discusses in detail the development of a distributed software defect prediction model based on deep learning,including model design,data preprocessing and feature extraction methods,model training,and performance evaluation.This study aims to improve the efficiency and accuracy of defect detection in distributed software development,providing an effective technical support and solution for the field of software engineering.
作者
郭俨锐
GUO Yanrui(School of Software,Henan University,Kaifeng Henan 475000)
出处
《软件》
2024年第11期56-58,共3页
Software
关键词
深度学习
分布式
软件
缺陷预测
deep learning
distributed
software
defect prediction
