app专项测试自动化测试方法思路与实现

2023-07-25 07:59:52

秉着个人意愿打算把python+rf接口自动进行彻底结束再做些其它方面的输出~但事与愿违，但领导目前注重先把专项测试方面完成，借此，先暂停python+rf(主要是与Jenkins集成+导入DB+微信告警)接口自动化，且目前个人觉得前面讲解的python+rf可以说基本完成了接口自动化测试前期和后续的核心工作了，转而介绍下app专项测试方面的指标检查~

介绍app专项自动化具体实现前，先谈一下我的思路（如下图），若有不妥，欢迎斧正~

步骤一：循环执行&指标获取，准确点来说是实现循环启动某个页面（adb shell am start）时指标数据获取

具体实现可以看下核心代码

__author__ = 'niuzhigang'

# -*- coding: utf-8 -*-

#encoding=utf-8

import os

import time

import datetime

import sys

import subprocess

import xlwt

from tempfile import TemporaryFile

from xlwt import Workbook

dir = r'C:\Users\niuzhigang\Desktop\packet\autoScript'

print dir

now_time = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')

print now_time

print (os.getcwd())

os.chdir(dir)

print (os.getcwd())

if os.path.exists("TotalTime.log")==True:

    os.remove("TotalTime.log")

if os.path.exists("StartAppDalvikPss.log")==True:

    os.remove("StartAppDalvikPss.log")

if os.path.exists("StartAppNativePss.log")==True:

    os.remove("StartAppNativePss.log")

if os.path.exists("StartAppTOTALPss.log")==True:

    os.remove("StartAppTOTALPss.log")

if os.path.exists("AppCpuThr.log")==True:

    os.remove("AppCpuThr.log")

restartAppCrashlog = os.popen("adb logcat | findstr /I XXX | findstr /I Crash >> XXXCrash.log")

restartAppAlllog = os.popen("adb logcat | findstr /I XXX  >> XXXAll.log")

restartAllCrashlog = os.popen("adb logcat | findstr /I Crash >> AllCrash.log")

for i in  range(1000):

    try:

        restartAppTotalTime = os.popen("adb shell am start -W  -S com.XXX.app.ui/.homepage.LaunchActivity | findstr TotalTime >> TotalTime.log")

        time.sleep(5)

        #print restartAppTotalTime.read();

        for x in range(5):

            StartAppTOTALPss = os.popen("adb shell dumpsys meminfo -a com.XXX.app.ui | findstr TOTAL >> StartAppTOTALPss.log")

            #print StartAppTOTALPss.read();

            StartAppTOTALPss = os.popen("adb shell dumpsys meminfo -a com.XXX.app.ui | findstr Native  | findstr Heap >> StartAppNativePss.log")

            #print StartAppTOTALPss.read();

            StartAppTOTALPss = os.popen("adb shell dumpsys meminfo -a com.XXX.app.ui | findstr Dalvik  | findstr Heap >> StartAppDalvikPss.log")

            #print StartAppTOTALPss.read();

            restartAppCpuThr = os.popen("adb shell top  -d 1 -n 2 -m 1 -s cpu | findstr com.XXX.app.ui >> AppCpuThr.log")

            #print restartAppCpuThr.read();

        time.sleep(2)

        #强制杀死进程

        StopApp = os.popen("adb shell am force-stop com.XXX.app.ui")

        time.sleep(1)

        StartApp = os.popen("adb shell am start -W  -n com.XXX.app.ui/.homepage.LaunchActivity")

        time.sleep(5)

        OnceStopApp = os.popen("adb shell am force-stop com.XXX.app.ui")

        time.sleep(1)

    except Exception,e:

        print Exception,":",e

        #print "在没有出现异常的情况下执行的循环次数为："+i

        #出现异常点击返回键退出APP程序

        BackKeyStart = os.popen("adb shell input keyevent 4")

        time.sleep(1)

        BackKeyEnd = os.popen("adb shell input keyevent 4")

        #出现异常按home键

        HomeKeyStart = os.popen("adb shell input keyevent 3")

        #强制杀死进程

        StopApp = os.popen("adb shell am force-stop com.XXX.app.ui")

        time.sleep(1)

        continue

首先针对这个专项目前我只收集了cpu、Thr、totaltime、jni层和java层的pss、crash

感兴趣的同学可以收集battery，network等~

再说明下第一次force-stop了为什么我又做了app的重启操作之后再force-stop app呢？

原因1：不属于重启的异常导致手机异常没办法再次拉起app（系统异常），原因2：内部异常也可能导致无法下次start正常以至于程序出现假死的情况。

因此在正常start的情况下收集完本次循环中指标数据又做了下面的start和force-stop操作，当然这次启动我是不记录指标的~

最后说明下为什么做了except操作，可能系统导致程序运行出现异常的情况下也是有可能的，所以做了一系列的手机回到home操作后重启app后，跳出本次异常继续执行下一个循环~

当然，上面的except不一定都是出现这个情况，可以根据实际情况来下，当然写的多了异常考虑我觉得会更好~因为不能在设定的循环过程中没执行完就结束本次循环~

步骤二：指标处理&指标导入，准确点来说就是通过adb命令无法把每项具体的指标以一个list方法展现，因此我们要对搜集到的指标数据按照一定格式进行处理，把每个指标进行剥离后导入excel或者DB

说明下，我写的导入DB的脚本不是从txt里面读取的数据~而是excel

首先大家可以看下从txt读入excel（主要包括数据剥离和数据计算）

__author__ = 'niuzhigang'

# -*- coding: utf-8 -*-

#encoding=utf-8

import os

import time

import datetime

import xlwt

from tempfile import TemporaryFile

from xlwt import Workbook

dir = r'C:\Users\niuzhigang\Desktop\packet\autoScript'

print dir

now_time = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')

print now_time

print (os.getcwd())

os.chdir(dir)

print (os.getcwd())

#创建文件

file = Workbook(encoding='utf-8')

#创建sleet

tableTotalTime = file.add_sheet('TotalTime')

#每列给出名称

tableTotalTime.write(0,0,'TotalTime')

#写出第二列的平均值名称

tableTotalTime.write(0,1,'AvgTotalTime')

TotalTimefpath = r'C:\Users\niuzhigang\Desktop\packet\autoScript\TotalTime.log'

#打开文件并读取

f = open(TotalTimefpath,'r')

line = f.readlines()

len = 1

SumTotalTime = 0

for item in line:

    #转为list

    list = item.split( )

    TotalTime = list[1]

    print u"TotalTime耗时为："+TotalTime+"ms"

    tableTotalTime.write(len,0,float(TotalTime))

    len = len + 1

        #获取totaltime总值

        SumTotalTime += float(TotalTime)

print u"TotalTime总耗时为："+str(SumTotalTime)

#求平均值

AvgTotalTimeint = SumTotalTime/(len-1)

print  AvgTotalTimeint

#获取TotalTime的平均值且保留2位小数

AvgTotalTime = float('%.2f' % AvgTotalTimeint)

tableTotalTime.write(1,1,AvgTotalTime)

print AvgTotalTime

f.close()

#保存excel并命名

file.save('TotalTime.xlsx')

#创建文件

file = Workbook(encoding='utf-8')

#创建sleet

tableTOTALPss = file.add_sheet('TOTALPss')

#每列给出名称

tableTOTALPss.write(0,0,'TOTALPss')

#写出第二列的平均值名称

tableTOTALPss.write(0,1,'AvgTOTALPss')

TOTALPssfpath = r'C:\Users\niuzhigang\Desktop\packet\autoScript\StartAppTOTALPss.log'

#打开文件并读取

f = open(TOTALPssfpath,'r')

line = f.readlines()

len = 1

SumTOTALPss = 0

for item in line:

    #转为list

    list = item.split( )

    TOTALPss = list[1]

    print u"TOTALPss占用大小为："+TOTALPss+"Kb"

    # print TOTALPss 并存入excel为整数类型

    tableTOTALPss.write(len,0,float(TOTALPss))

    len = len + 1

         #获取TOTALPss总值

        SumTOTALPss += float(TOTALPss)

    print len

print u"SumTOTALPss总pss为："+str(SumTOTALPss)

#求平均值

AvgTOTALPssint = SumTOTALPss/(len-1)

print  AvgTOTALPssint

#获取TOTALPss的平均值且保留2位小数

AvgTOTALPss = float('%.2f' % AvgTOTALPssint)

tableTOTALPss.write(1,1,AvgTOTALPss)

print AvgTOTALPss

f.close()

#保存excel并命名

file.save('TOTALPss.xlsx')

#创建文件

file = Workbook(encoding='utf-8')

#创建sleet

tableNativePss= file.add_sheet('NativePss')

#每列给出名称

tableNativePss.write(0,0,'NativePss')

#写出第二列的平均值名称

tableNativePss.write(0,1,'AvgNativePss')

NativePssfpath = r'C:\Users\niuzhigang\Desktop\packet\autoScript\StartAppNativePss.log'

#打开文件并读取

f = open(NativePssfpath,'r')

line = f.readlines()

len = 1

SumNativePss = 0

for item in line:

    #转为list

    list = item.split( )

    NativePss = list[2]

    print u"NativePss占用大小为："+NativePss+"Kb"

    # print Cpu  并存入excel为整数类型

    tableNativePss.write(len,0,float(NativePss))

    len = len + 1

        #获取TOTALPss总值

        SumNativePss += float(NativePss)

print u"SumNativePss总pss为："+str(SumNativePss)

#求平均值

AvgNativePssint = SumNativePss/(len-1)

print  AvgNativePssint

#获取TOTALPss的平均值且保留2位小数

AvgNativePss = float('%.2f' % AvgNativePssint)

tableNativePss.write(1,1,AvgNativePss)

print AvgNativePss

f.close()

#保存excel并命名

file.save('NativePss.xlsx')

#创建文件

file = Workbook(encoding='utf-8')

#创建sleet

tableDalvikPss= file.add_sheet('DalvikPss')

#每列给出名称

tableDalvikPss.write(0,0,'DalvikPss')

#写出第二列的平均值名称

tableDalvikPss.write(0,1,'AvgDalvikPss')

DalvikPssfpath = r'C:\Users\niuzhigang\Desktop\packet\autoScript\StartAppDalvikPss.log'

#打开文件并读取

f = open(DalvikPssfpath,'r')

line = f.readlines()

len = 1

SumDalvikPss = 0

for item in line:

    #转为list

    list = item.split( )

    DalvikPss = list[2]

    print u"DalvikPss占用大小为："+DalvikPss+"Kb"

    # print Cpu  并存入excel为整数类型

    tableDalvikPss.write(len,0,float(DalvikPss))

    len = len + 1

        #获取TOTALPss总值

        SumDalvikPss += float(DalvikPss)

print u"SumDalvikPss总pss为："+str(SumDalvikPss)

#求平均值

AvgDalvikPssint = SumDalvikPss/(len-1)

print  AvgDalvikPssint

#获取TOTALPss的平均值且保留2位小数

AvgDalvikPss = float('%.2f' % AvgDalvikPssint)

tableDalvikPss.write(1,1,AvgDalvikPss)

print AvgDalvikPss

f.close()

#保存excel并命名

file.save('DalvikPss.xlsx')

#创建文件

file = Workbook(encoding='utf-8')

#创建sleet

tableCpu = file.add_sheet('AppCpuResult')

tableThr = file.add_sheet('AppThrResult')

#每列给出名称

tableCpu.write(0,0,'%Cpu')

tableThr.write(0,0,'Thr')

#写出第二列的平均值名称

tableCpu.write(0,1,'AvgCpu')

tableThr.write(0,1,'AvgThr')

AppCpuThrfpath = r'C:\Users\niuzhigang\Desktop\packet\autoScript\AppCpuThr.log'

#打开文件并读取

f = open(AppCpuThrfpath,'r')

line = f.readlines()

len = 1

SumCpu = 0

SumThr = 0

for item in line:

    #转为list

    list = item.split( )

    cpu = list[2]

    Thr = list[4]

    print u"cpu利用率为："+cpu+u" 线程数为："+ Thr

    # 截取字符串%

    Cpu = cpu.rstrip('%')

    # print Cpu  并存入excel为整数类型

    tableCpu.write(len,0,float(Cpu))

    tableThr.write(len,0,float(Thr))

    len = len + 1

        #获取cpu总值

        SumCpu += float(Cpu)

        #获取cpu总值

        SumThr += float(Thr)

print u"SumCpu总cpu为："+str(SumCpu)

print u"SumThr总thr为："+str(SumThr)

#求平均值

AvgCpuint = SumCpu/(len-1)

AvgThrint = SumThr/(len-1)

print  AvgCpuint

print  AvgThrint

#获取Cpu和Thr的平均值且保留2位小数

AvgCpu = float('%.2f' % AvgCpuint)

AvgThr = float('%.2f' % AvgThrint)

tableCpu.write(1,1,AvgCpu)

tableThr.write(1,1,AvgThr)

print AvgCpu

print AvgThr

f.close()

#保存excel并命名

file.save('AppCpuThrResult.xlsx')

if os.path.exists("TotalTime.log")==True:

    os.rename("TotalTime.log",now_time+"TotalTime.log")

if os.path.exists("StartAppTOTALPss.log")==True:

    os.rename("StartAppTOTALPss.log",now_time+"StartAppTOTALPss.log")

if os.path.exists("StartAppNativePss.log")==True:

    os.rename("StartAppNativePss.log",now_time+"StartAppNativePss.log")

if os.path.exists("StartAppDalvikPss.log")==True:

    os.rename("StartAppDalvikPss.log",now_time+"StartAppDalvikPss.log")

if os.path.exists("AppCpuThr.log")==True:

    os.rename("AppCpuThr.log",now_time+"AppCpuThr.log")

这个脚本我目前一方面主要是完成数据剥离和计算，另一方面进行了保存，保留了历史记录！！！详细的说明都是excel操作，我就不多说了，不懂的可以私信或者留下评论~

好了，数据导入excel，数据从txt转入excel大家可以从下图(拿CPU举例~)视觉上看下变化，数据怎么剥离、怎么导入、怎么计算的~

TXT格式的数据截图：

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" alt="" />

EXCEL格式的数据截图：

aaarticlea/png;base64,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" alt="" />

一般来说接下来就要考虑根据数据出图~那我们就按照一般的思路来出图，根据excel列表数据画图~

借助matplotlib插件库，这个我就粗略的介绍下根据excel列表数据如何自动化画出伸缩图，就给大家晒下py脚本吧~

不多余介绍，都是简单方法的使用完成图的自动伸缩，因为这个方式感觉很笨拙~为什么笨拙？下面就会讲~

__author__ = 'niuzhigang'

# -*- coding: utf-8 -*-

#encoding=utf-8

import numpy as np

import matplotlib.pyplot as plt

#X，Y轴数据

y = [20,59,11,12,16,20,15,12,16,21,34,48,11,15,18,16,17,17,11,25,16,9,10,18,16,18,18]

#计算list y的长度

ylen = len(y)

#print ylen

#(开始值、终值 、元素个数作为X坐标目的实现X轴自动伸缩)

xArray = np.linspace(0,ylen,ylen,endpoint=False)

#list与array互相转换，转为list

x = xArray.tolist()

print x

#创建绘制图像像素大小

#plt.figure(figsize=(15,10))

#在当前绘图对象绘图(XY轴数据，红色实线，线宽度)

plt.plot(x,y,"c",linewidth=1)

#X轴标题

#plt.xlabel("line")

#Y轴标题

plt.ylabel("date")

#图标题

plt.title("Cpu%")

#显示网格

plt.grid(True)

#显示图

plt.show()

#保存图

plt.savefig(r"C:\Users\niuzhigang\Desktop\packet\autoScript\Cpu.png")

说了通过excel画图很笨拙，为什么？原因一：不是UI的方式展现，看起来不方便（想想如果做成报表是不是很好）原因二：死的就是死，没有你想的维度查看、对比等等~

那么接下来，我就讲下导入DB的操作

步骤三：导入DB，具体脚本如下,目前主要从平均值、具体版本执行过程中抓取的详细数据

平均值的目的暂时是做成不同版本之间比较，详细数据目的是检查本版本此指标的走势~

有个问题说下：为什么设置版本（var）为变量，因为目前没有什么好的办法主动获取版本号~

如果其他上神有思路的话可以提供下~

__author__ = 'niuzhigang'

# -*- coding: utf-8 -*-

#encoding=utf-8

import MySQLdb

import xlrd

#版本号

ver = "'9.1.0'"

#页面activity

pageActivity = "'homepage.LaunchActivity'"

#连接数据库

conn= MySQLdb.connect(

        host='10.10.30.200',

        port = 3306,

        user='mobtest',

        passwd='XXX520',

        db ='test',

        )

#创建游标目的操作数据库

cur = conn.cursor()

#通过游标cur 操作execute方法来创建表

cur.execute("create table if NOT EXISTS AutoTest_AvgTotalTime(id int NOT NULL auto_increment primary key ,totalTimeAvgResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AllTotalTime(id int NOT NULL auto_increment primary key ,totalTimeAllResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AvgTOTALPss(id int NOT NULL auto_increment primary key ,totalPssAvgResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AllTOTALPss(id int NOT NULL auto_increment primary key ,totalPssAllResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AvgNativePss(id int NOT NULL auto_increment primary key ,totalPssAvgResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AllNativePss(id int NOT NULL auto_increment primary key ,nativePssAllResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AvgDalvikPss(id int NOT NULL auto_increment primary key ,totalPssAvgResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AllDalvikPss(id int NOT NULL auto_increment primary key ,dalvikPssAllResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AvgCpu(id int NOT NULL auto_increment primary key ,cpuAvgResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AllCpu(id int NOT NULL auto_increment primary key ,cpuAllResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AvgThr(id int NOT NULL auto_increment primary key ,thrAvgResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

cur.execute("create table if NOT EXISTS AutoTest_AllThr(id int NOT NULL auto_increment primary key ,thrAllResult varchar(255) ,excVersion varchar(255),excPage varchar(255),excTerminal varchar(255) NOT  NULL DEFAULT 'App',creatTime timestamp  NOT NULL DEFAULT NOW() )")

#读取平均值并插入mysql

path = r'C:\Users\niuzhigang\Desktop\packet\autoScript\TotalTime.xlsx'

wb = xlrd.open_workbook(path)

tableTotalTime = wb.sheets()[0]

TotalTimeValue = tableTotalTime.cell(1, 1).value

TotalTime = str(TotalTimeValue)

print u"启动耗时为："+ TotalTime

#插入totaltime平均值

cur.execute("insert into AutoTest_AvgTotalTime values(DEFAULT," + TotalTime + "," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

#捕获到有效数据的行数

nrows=tableTotalTime.nrows

line_num = 0

for i in range(nrows):

    #获取一行的所有值，每一列的值以列表项存在

    if i > 0:

        line_num += 1

        allTotalTimeValue=tableTotalTime.cell(line_num,0).value

        allTotalTime = str(allTotalTimeValue)

        print allTotalTime

        #插入本迭代执行所有totaltime

        cur.execute("insert into AutoTest_AllTotalTime values(DEFAULT,"+allTotalTime+"," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

    else:

        print u"数据为空"

#读取平均值并插入mysql

path = r'C:\Users\niuzhigang\Desktop\packet\autoScript\TOTALPss.xlsx'

wb = xlrd.open_workbook(path)

tableTOTALPss = wb.sheets()[0]

TOTALPssValue = tableTOTALPss.cell(1, 1).value

TOTALPss = str(TOTALPssValue)

print u"TOTALPss为："+ TOTALPss

#插入TOTALPss平均值

cur.execute("insert into AutoTest_AvgTOTALPss values(DEFAULT," + TOTALPss + "," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

#捕获到有效数据的行数

nrows=tableTOTALPss.nrows

line_num = 0

for i in range(nrows):

    #获取一行的所有值，每一列的值以列表项存在

    if i > 0:

        line_num += 1

        allTOTALPssValue=tableTOTALPss.cell(line_num,0).value

        allTOTALPss = str(allTOTALPssValue)

        print allTOTALPss

        #插入本迭代执行所有TOTALPss

        cur.execute("insert into AutoTest_AllTOTALPss values(DEFAULT,"+allTOTALPss+"," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

    else:

        print u"数据为空"

#读取平均值并插入mysql

path = r'C:\Users\niuzhigang\Desktop\packet\autoScript\NativePss.xlsx'

wb = xlrd.open_workbook(path)

tableNativePss = wb.sheets()[0]

NativePssValue = tableNativePss.cell(1, 1).value

NativePss = str(NativePssValue)

print u"NativePss为："+ NativePss

#插入NativePss平均值

cur.execute("insert into AutoTest_AvgNativePss values(DEFAULT," + NativePss + "," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

#捕获到有效数据的行数

nrows=tableNativePss.nrows

line_num = 0

for i in range(nrows):

    #获取一行的所有值，每一列的值以列表项存在

    if i > 0:

        line_num += 1

        allNativePssValue=tableNativePss.cell(line_num,0).value

        allNativePss = str(allNativePssValue)

        print allNativePss

        #插入本迭代执行所有NativePss

        cur.execute("insert into AutoTest_AllNativePss values(DEFAULT,"+allNativePss+"," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

    else:

        print u"数据为空"

#读取平均值并插入mysql

path = r'C:\Users\niuzhigang\Desktop\packet\autoScript\DalvikPss.xlsx'

wb = xlrd.open_workbook(path)

tableDalvikPss = wb.sheets()[0]

DalvikPssValue = tableDalvikPss.cell(1, 1).value

DalvikPss = str(DalvikPssValue)

print u"DalvikPss为："+ DalvikPss

#插入DalvikPss平均值

cur.execute("insert into AutoTest_AvgDalvikPss values(DEFAULT," + DalvikPss + "," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

#捕获到有效数据的行数

nrows=tableDalvikPss.nrows

line_num = 0

for i in range(nrows):

    #获取一行的所有值，每一列的值以列表项存在

    if i > 0:

        line_num += 1

        allDalvikPssValue=tableDalvikPss.cell(line_num,0).value

        allDalvikPss = str(allDalvikPssValue)

        print allDalvikPss

        #插入本迭代执行所有DalvikPss

        cur.execute("insert into AutoTest_AllDalvikPss values(DEFAULT,"+allDalvikPss+"," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

    else:

        print u"数据为空"

#读取平均值并插入mysql

path = r'C:\Users\niuzhigang\Desktop\packet\autoScript\AppCpuThrResult.xlsx'

wb = xlrd.open_workbook(path)

tableCpu = wb.sheets()[0]

cpuValue = tableCpu.cell(1, 1).value

cpu = str(cpuValue)

print u"cpu利用率为："+ cpu

tableThr = wb.sheets()[1]

thrValue = tableThr.cell(1, 1).value

thr = str(thrValue)

print u"thr数为："+ thr

#插入Cpu平均值

cur.execute("insert into AutoTest_AvgCpu values(DEFAULT," + cpu + "," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

#插入Thr平均值

cur.execute("insert into AutoTest_AvgThr values(DEFAULT,"+thr+"," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

#捕获到有效数据的行数

nrows=tableCpu.nrows

line_num = 0

for i in range(nrows):

    #获取一行的所有值，每一列的值以列表项存在

    if i > 0:

        line_num += 1

        allCpuValue=tableCpu.cell(line_num,0).value

        allCpu = str(allCpuValue)

        print allCpu

        #插入本迭代执行所有cpu

        cur.execute("insert into AutoTest_AllCpu values(DEFAULT,"+allCpu+"," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

    else:

        print u"数据为空"

#捕获到有效数据的行数

nrows=tableThr.nrows

line_num = 0

for i in range(nrows):

    #获取一行的所有值，每一列的值以列表项存在

    if i > 0:

        line_num += 1

        allThrValue=tableThr.cell(line_num,0).value

        allThr = str(allThrValue)

        print allThr

        #插入本迭代执行所有thr

        cur.execute("insert into AutoTest_AllThr values(DEFAULT,"+allThr+"," + ver + "," + pageActivity + ",DEFAULT,DEFAULT)")

    else:

        print u"数据为空"

#关闭游标

cur.close()

#提交

conn.commit()

#关闭数据库连接

conn.close(

好了，导入数据sql的数据如下

平均值方面：

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" 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详细数据方面：

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" alt="" />

后面就是APM读取数据且支持维度查询了~APM暂时不是我来搞，所以~~~……后续截图大家可以看下~

补充截图：平均趋势图如下：

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" alt="" />

另外指标采集的数据比较多，画出来的详细图，相对来说不易观察，且点与点堆积比较密集，视觉上很不理想，因此对数据做了聚合之后再导入EXCEL以及DB的！

处理方式为每10项数据求和后得出来的平均值导入EXCEL和DB!（不满足10条没在做平均值而是把不满足10条的数据没做处理直接导入）

具体实现如下：

__author__ = 'niuzhigang'

# -*- coding: utf-8 -*-

#encoding=utf-8

import os

import time

import datetime

import xlwt

from tempfile import TemporaryFile

from xlwt import Workbook

dir = r'C:\Users\niuzhigang\Desktop\packet\autoScript'

print dir

now_time = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')

print now_time

print (os.getcwd())

os.chdir(dir)

print (os.getcwd())

#创建文件

file = Workbook(encoding='utf-8')

#创建sleet

tableTotalTime = file.add_sheet('TotalTime')

#每列给出名称

tableTotalTime.write(0,0,'TotalTime')

#写出第二列的平均值名称

tableTotalTime.write(0,1,'AvgTotalTime')

TotalTimefpath = r'C:\Users\niuzhigang\Desktop\packet\autoScript\TotalTime.log'

#打开文件并读取

f = open(TotalTimefpath,'r')

line = f.readlines()

a = 1

SumTotalTime = 0

TotalTimeArry = []

TotalTimeArryNew = []

offset = 0

#每几项聚合求平均值

step = 10

for item in line:

    #转为list

    list = item.split()

    TotalTime = list[1]

        TotalTimeArry.append(int(TotalTime))

    # print u"TotalTime耗时为："+TotalTime+"ms"

# print TotalTimeArry

length = len(TotalTimeArry)

while offset < length:

    tmp = TotalTimeArry[offset:offset + step]

    # print tmp

    if len(tmp) == step:

            avg = 0

            for t in tmp:

                avg += t

            avg /= float(step)

            TotalTimeArryNew.append(avg)

    else:

        for t in tmp:

            TotalTimeArryNew.append(t)

    offset += step

print TotalTimeArryNew

#计算聚合后的总值

SumTotalTime = 0

for x in TotalTimeArryNew:

    SumTotalTime += x

    tableTotalTime.write(a,0,float(x))

    a += 1

# print a

print  SumTotalTime

#求平均值

AvgTotalTimeint = SumTotalTime/(a-1)

# print  AvgTotalTimeint

#获取TotalTime的平均值且保留2位小数

AvgTotalTime = float('%.2f' % AvgTotalTimeint)

tableTotalTime.write(1,1,AvgTotalTime)

print AvgTotalTime

f.close()

#保存excel并命名

file.save('TotalTime.xlsx')

执行结果前与后的对比：

一：数量对比

聚合前：

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fCqH8mHzfOzfESJTyWx4dEFC3aeT1tLYoCndzzxPPmhVE10JSmS4vnVo2WGM/L40OWtwbme7XYvjPiWamXSDgq9JoMes8AgFsyxrP+RO2Zed0hoaa3H+ZGoOsl0WxDo6N6Z8svVnrr9rWECnRyzxXPWvupDZAazy0aLaX3rMynHwB+v9e+75QJoinUCeJHhVqTQe8ZAHCLFs9/c8rcR2Lo13/OMGGBTu5T07M9b7ds1Hh2oSaD3jMA4Ban4vnPvx76aM5/PPeRyJjOxHMexDMAuCyK5xcvXjwYuPqvfhpcAf3pr+oZZyrQyX1qeran7ZfdixcvvHge+H73ajLoPQMAbtHi+a/FVKCT+9T07KBby6fGsws1GfSeAQC3RPH8/PnzvxRTgU7uU9Ozg24t3/Pnz714Hvh+92oy6D0DAG7R4vn/iqlAJ/ep6dlBt5ZPjWcXajLoPQMAbhHeObrow6CbMauBNxQDAwMDQyEGMejAAgAAJuIZAADnEM8AADiHeAYAwDnEMwAAzmkdz81m8+ErydBsNvuwPwAAkMQz8QwAcBDxTDwDAJxDPBPPAADnEM/EMwDAOcQz8QwAcE6OeK5/HNySWZyoR+UPZvaH5eLQ2Qfq9OpT+fDBzP4DM6uv5MNXjbMHjInlw6WKODCz+kpePiFMwepWf38oeNw4e6By+ZV8+EpePiH2/75BPAMA3gFtxvNSRQjx8VL01E/EpYqWsg9m9keTtYjnj0/4eWzEs5LufgCbsy9VhDi0/0Tl4wNeMDfOHtBXRDwDAIpJvPrvXvrQbDZv7sibO/LmTv2wEIevyuCpTCu/WhGi8jt/7KFT95RR92b2Dc9c3pE3dxqnhsXhq41Tw2LfZCOa0R8bLtxbjj/87piyrnsz+9RVX62IY/VY9bozEM8AgL5pJ57N4JSp5WEqt4xnPWVbxHPj1HC4NG+UMkG0ZOIZAFBg4uWr/6UPeeM5iN4s8bwjL08e8nvbLeLZ7EzH3hOkjCWeAQDFkCmeV568XXnydmXhEzF8/g/eY3Wwl2+dGj54au3typPrh4X3IBjWzu/zp986NSwOL4TTCzF2Pba064fFJ1XLvNYhXGn3B+IZANA37cRzPGij8jBllcz2Y1XNYCPO9VFr5/cJcXgsNZ7Np/GapIwlngEAxSB2X/43fWg2mzcev/WGS789KMTBT+/6T28sfLLvt1uW8rvn9wnx4YI61yfn/IVc/1AIb64bj7c+HY4mC6YUYvj8pcdh4fUPo3m9p8qKjOHu+X3axN0ciGcAQN+0F89eJEc/RB67rkdjyEzQc2PRuCCbLfHslaTG89tzY8Yset3UKhHPAIBiEi92X6UPzWbzi6c/OjSsn//J8PlLllFbnw4f/HS9V+slngEAfSOev3iZPjSbzS/+9aNTw6XfHBS/vG4Unvul+Mlvtnq3UuIZANA3meJ5+d9vGIhnAEDfiB+e76YPzWZz+Z9vGIhnAEDfiP/88CJ9aDabf9p+zUA8AwD6hngmngEAzhFNZDbonQUAeF+ILDdS/NmcZHj27NmgdxYA4H1BPBPPAADnEM/EMwDAOcQz8QwAcA7xTDwDAJwTxbMQ9qgmnolnAECfafFsTejixfOKlFJ+taIVfhVs8KXP8xQSzwCAfjLjOZ7QxYrnS0+lfCq/eqrF86Wn8pv7UXKfbbOQeAYA9Jm4uSO9QSjCwps7BYvnMI+jeP5cfqMErT8qeyHxDADoO3s8qwld+HhekfIf0aiz/5Df3G+nkHgGAPRdYjyHCV30eP7FfUvoZi8kngEA/SdWnrz1BiObw/KixzO9ZwBA4djjOSx8R+L5qfyFMSp7IfEMAOg7cePxW28Iszks8YbCx7P6VAng7IXEMwCgz8x4NrK5cPF86am2eX7Wfi6/CUqi30plLySeAQD9Jb54+qM3CBE9VodixXPvBuIZANA34ot//Zg+EM/EMwCgz8Tyv9+kD8Qz8QwA6DOx/M836QPxTDwDAPpM/Gn7dfpAPBPPAIA+I56JZwCAc0Qzg2d49uwZ8QwA6Bcx6AoAAAAT8QwAgHOIZwAAnEM8AwDgHOIZAADnEM8AADiHeAYAwDnEMwAAziGeAQBwDvEMAIBziGcAAJxDPAMA4BziGQAA5xDPAAA4h3gGAMA5xDMAAM4hngEAcA7xDACAc4hnAACcQzwDAOAc4hkAAOcQzwAAOId4BgDAOcQzAADOIZ4BAHDO/wPbAFFtFuHOoQAAAABJRU5ErkJggg==" alt="" />

聚合后：

aaarticlea/png;base64,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" 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二：数值对比：

聚合前：

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" 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聚合后：

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" alt="" />

码农公寓

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