Research on Test Cases Distribution for Performance Evaluation of 5G Communication Base Station Management System

LI Yizhen; SUN Yiding; ZOU Jinzi; LIU Yuhang; QIN Tao; LI Huan; YANG Xiaoyan; LIANG Taotao

doi:10.7652/xjtuxb202303018

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Research on Test Cases Distribution for Performance Evaluation of 5G Communication Base Station Management System

更新时间：2025-07-09

- Research on Test Cases Distribution for Performance Evaluation of 5G Communication Base Station Management System
- Vol. 57, Issue 3, Pages: 193-201(2023)
- 作者机构：
  
  1. 西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
  2. 华为技术有限公司西安研究所,西安,710075
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202303018
  CLC： TP391.9
- Online First：10 March 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
LI Yizhen, SUN Yiding, ZOU Jinzi, et al. Research on Test Cases Distribution for Performance Evaluation of 5G Communication Base Station Management System[J]. 2023, 57(3): 193-201.
DOI：

LI Yizhen, SUN Yiding, ZOU Jinzi, et al. Research on Test Cases Distribution for Performance Evaluation of 5G Communication Base Station Management System[J]. 2023, 57(3): 193-201. DOI： 10.7652/xjtuxb202303018.

摘要

针对通信基站管理系统目前多采用融合人工经验的测试方法

无法模拟长周期且发生时间合理的测试用例问题

提出了一种基于基站告警日志的测试用例建模方法。首先

以基站日志产生时间间隔为研究对象

从数据整体、设备类型、日志类型、设备和日志类型等维度分析发现日志产生时间间隔服从幂律分布的规律; 然后

对比分析最小二乘法、极大自然估计与最大后验估计3种不同的参数估计算法

以估算误差为依据

发现最小二乘法的拟合度更高且残差最小

拟合优度与平均绝对百分误差的平均值分别为0.96和3.5%; 最后

采用最小二乘法对日志时间间隔进行幂指数估算

保留局点拟合优度大于0.7的告警组成测试用例。该模型应用于全国多个不同城市通信基站的告警日志间隔分布估算

实验结果表明:所提模型可以实现85%以上日志分布规律计算

为编排贴近实际运行环境的测试用例奠定基础

进一步提升测试结果的可靠度。

Abstract

The communication base station management system mostly adopts an empirical test method at present

which cannot simulate the test cases with a long period and reasonable occurrence time. To solve this problem

a test case modeling method based on the base station alarm log is proposed. Firstly

taking the log generation time interval of the base station as the research object

it is found that the log generation time interval obeys power-law distribution by analyzing data entirety

device type

log type

and device combination log type. Then

the least square method

maximum likelihood estimation

and maximum posterior estimation are compared and analyzed. Based on the estimation error

it is found that the least square method has the fitting of a higher degree and the smallest residual error

the value of goodness of fit is 0.96%

and mean absolute percentage error is 3.5%. Finally

the least square method is used to estimate the log time interval by power exponent

and the alarms with the value of goodness of fit greater than 0.7 are reserved to form test cases within a location. The model is applied to estimate the interval distribution of alarm logs of communication base stations in different cities throughout the country. The experimental results show that the proposed model can calculate more than 85% of log distribution law

which lays a foundation for arranging test cases close to the actual operating environment and further improves the reliability of test results.

关键词

Keywords

references

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