TEST UPPH WBS V1.2
LTP性能检验工具详细介绍
LTP工具说明1LTP测试套件 (4)1.1简介 (4)1.2 源目录结构 (4)2 LTP安装 (5)2.1 下载 (5)2.2 编译 (5)2.3 安装说明 (7)3 LTP测试套件结构说明 (8)3.1 概述 (8)3.2 目录介绍 (8)3.3 LTP执行原理 (8)4 LTP测试套件测试内容 (9)4.1 LTP测试套件测试内容 (9)4.1.1 commands (9)4.1.2 kernel (10)4.1.3 kdump (10)4.1.4 network (11)4.1.5 realtime (11)4.1.6 open_posix_testsuite (11)4.1.7 misc (11)4.2 测试方法说明 (11)4.2.1 commands模块内容描述及实现方法 (11)4.2.2 kernel (14)4.2.3 network (23)4.2.4 open_posix_testsuite (26)4.2.5 realtime (27)5 LTP测试套件配置详细 (28)5.1 networktests.sh脚本配置 (28)5.2 networkstress.sh配置 (33)5.3 open_posix_testsuite测试套件 (37)5.4 realtime配置 (39)5.5 mm脚本的配置 (40)5.6 io脚本配置 (40)5.7 filecaps的配置 (40)5.8 tpm_tools的配置 (41)5.9 tcore的配置 (41)5.10 io_floppy的配置 (41)5.11 io_cd 的配置 (42)5.12 cpuhotplug的配置 (42)5.13 adp.sh的配置 (43)5.14 autofs1.sh和autofs4.sh的配置 (44)5.15 exportfs.sh的配置 (44)5.16 isofs.sh的配置 (45)5.17 ltpdmmapper.sh的配置 (46)5.18 ltpfslvm.sh的配置及要求 (46)5.19 ltpfsnolvn.sh的配置及要求 (47)5.20 ltp-scsi_debug.sh的配置及要求 (48)5.21 sysfs.sh的配置及要求 (48)5.22 rpctirpc的配置及要求 (48)5.23 test_selinux.sh的配置及要求 (50)5.24 smack的配置和要求 (51)5.25 perfcounters的配置及要求 (52)5.26 can的配置及要求 (52)5.27 test_robind.sh的配置 (53)6 LTP测试套件使用说明 (54)6.1 概述 (54)6.2 初始测试 (55)6.2.1 runltp使用说明 (55)6.2.2 runalltests.sh脚本说明 (58)表1 LTP源代码结构2 LTP安装2.1 下载LTP是一项动态工程，LTP源包命名方式一般为：ltp-yyyymmdd。
ptest包的说明文档说明书
Package‘ptest’October14,2022Type PackageTitle Periodicity Tests in Short Time SeriesVersion1.0-8Date2016-11-12Author Yuanhao Lai and A.I.McLeodMaintainer A.I.McLeod<***************>Depends R(>=3.0),Description Implements p-value computations using an approximation to the cumulative distribu-tion function for a variety of tests for periodicity.These tests include harmonic regres-sion tests with normal and double exponential errors as well as modifica-tions of Fisher's g test.An accompanying vignette illustrates the application of these tests. License GPL(>=2)LazyData trueNeedsCompilation yesImports quantreg(>=5.0)RoxygenNote5.0.1Suggests knitr,boot,lattice,rmarkdown,GeneCycle,VignetteBuilder knitrRepository CRANDate/Publication2016-11-1221:41:37R topics documented:alpha (2)B1 (3)B2 (4)B3 (5)Cc (6)cdc15 (7)cdc28 (7)12alpha fitHReg (8)pgram (9)ptestg (10)ptestReg (12)simHReg (14)Index16 alpha Microarray time series experiment for yeast cell cycle from alpha ex-perimentDescription6,178yeast genes expression measures(log-ratios)with series length18from the alpha factor ex-periment.Usagedata(alpha)FormatMatrix with6178rows and18columns.Some missing data.Rows and columns are labelled.-attr(*,"dimnames")=List of2..$:chr[1:6178]"Y AL001C""Y AL002W""Y AL003W""Y AL004W".....$:chr[1:18]"alpha0""alpha7""alpha14""alpha21"...SourceThe data is extracted from the ExpressionSet of the R package yeastCC.ReferencesSpellman,P.T.,Sherlock,G.,Zhang,M.Q.,Iyer,V.R.,Anders,K.,Eisen,M.B.,...&Futcher,B.(1998).Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomycescerevisiae by microarray hybridization.Molecular biology of the cell,9(12),3273-3297.Dudoit S(2016).yeastCC:Spellman et al.(1998)and Pramila/Breeden(2006)yeast cell cycle microarray data.R package version1.12.0.Examplesdata(alpha)qqnorm(colMeans(alpha,na.rm=TRUE))qqnorm(rowMeans(alpha,na.rm=TRUE))B13 B1Benchmark set B1DescriptionList for yeast genes which are most likely to be periodic(the benchmark set1in de Lichtenberg et al.(2005)).Usagedata(B1)FormatA vector containg113genes’names.DetailsA total of113genes previously identified as periodically expressed in small-scale experiments.Theset encompasses the104genes used by Spellman et al.(1998)and nine genes added by Johansson et al.(2003).SourceThe raw data can be downloaded from http://www.cbs.dtu.dk/cellcycle/yeast_benchmark/ benchmark.php.ReferencesDe Lichtenberg,U.,Jensen,L.J.,Fausboll,A.,Jensen,T.S.,Bork,P.,&Brunak,S.(2005).Compar-ison of computational methods for the identification of cell cycle-regulated genes.Bioinformatics, 21(7),1164-1171.Examplesdata(alpha)data(B1)alphaB1<-alpha[rownames(alpha)\%in\%B1,]4B2 B2Benchmark set B2DescriptionList for yeast genes which are most likely to be periodic(the benchmark set2in de Lichtenberg et al.(2005)).Usagedata(B2)FormatA vector containg352genes’names.DetailsGenes whose promoters were bound(P-value below0.01)by at least one of nine known cell cycle transcription factors in both of the Chromatin IP studies by Simon et al.(2001)and Lee et al.(2002).To obtain a benchmark set that is independent of B1,we removed all genes included in B1(50).The resulting benchmark set,B2,consists of352genes of which many should be expectedto be cell cycle regulated,since their promoters are associated with known stage-specific cell cycle transcription factors.SourceThe raw data can be downloaded from http://www.cbs.dtu.dk/cellcycle/yeast_benchmark/ benchmark.php.ReferencesDe Lichtenberg,U.,Jensen,L.J.,Fausboll,A.,Jensen,T.S.,Bork,P.,&Brunak,S.(2005).Compar-ison of computational methods for the identification of cell cycle-regulated genes.Bioinformatics, 21(7),1164-1171.Examplesdata(alpha)data(B2)alphaB2<-alpha[rownames(alpha)\%in\%B2,]B35 B3Benchmark set B3DescriptionList for yeast genes which are less likely to be periodic(the benchmark set3in de Lichtenberg et al.(2005)).Usagedata(B3)FormatA vector containg518genes’names.DetailsGenes annotated in MIPS(Mewes et al.,2002)as’cell cycle and DNA processing’.From these,we removed genes annotated specifically as’meiosis’and genes included in B1(67),leaving518genes.As a large number of genes involved in the cell cycle are not subject to transcriptional regulation (not periodic),and because B1was explicitly removed,a relatively small fraction of these genes should be expected to be periodically expressed.SourceThe raw data can be downloaded from http://www.cbs.dtu.dk/cellcycle/yeast_benchmark/ benchmark.php.ReferencesDe Lichtenberg,U.,Jensen,L.J.,Fausboll,A.,Jensen,T.S.,Bork,P.,&Brunak,S.(2005).Compar-ison of computational methods for the identification of cell cycle-regulated genes.Bioinformatics, 21(7),1164-1171.Examplesdata(alpha)data(B3)alphaB3<-alpha[rownames(alpha)\%in\%B3,]6Cc Cc Microarray time series experiment for Caulobacter crescentus bacte-rial cell cycleDescriptionIn this microarray experiment there are3062genes measured every1hour.There are19is missing gene labels and these have been given labels ORFna1,...,ORFna19.There310with duplicate labels.Of these duplicate labels,295are duplicated twice,12are duplicated3times and3are duplicated 4times.Duplicate labels are renamed ORF...to ORF...a and ORF...b etc.Usagedata(Cc)FormatMatrix with3062rows and11columns.Some missing data.Rows and columns are labelled.-attr(*,"dimnames")=List of2..$:chr[1:3062]"ORF06244a""ORF03152a""ORF03156a""ORF03161a".....$:chr[1:11]"1""2""3""4"...DetailsGene expression from synchronized cultures of the bacterium Caulobacter crescentus(Laub et al., 2000).(Laub et al.,2000)identified553genes whose messenger RNA levels varied as a function of the cell cycle but their statistical analysis was not very sophisticated and they probably identified too many genes.Wichert et al.(2004)found that44genes were found which displayed periodicity based on the Fisher’s g-test using a FDR with q=0.05.ReferencesLaub,M.T.,McAdams,H.H.,Feldblyum,T.,Fraser,C.M.and Shapiro,L.(2000)Global analysis of the genetic network controlling a bacterial cell cycle Science,290,2144-2148.Wichert,S.,Fokianos K.and Strimmer K.(2004)Identifying periodically expressed transcrips in microarray time series data.Bioinformatics,18,5-20.Examplesdata(Cc)qqnorm(colMeans(Cc,na.rm=TRUE))qqnorm(rowMeans(Cc,na.rm=TRUE))cdc157 cdc15Microarray time series experiment for yeast cell cycle from cdc15ex-perimentDescription6,178yeast genes expression measures(log-ratios)with series length24from the cdc15experiment. Usagedata(cdc15)FormatMatrix with6178rows and24columns.Some missing data.Rows and columns are labelled.-attr(*,"dimnames")=List of2..$:chr[1:6178]"Y AL001C""Y AL002W""Y AL003W""Y AL004W".....$:chr[1:24]"cdc15.10""cdc15.30""cdc15.50""cdc15.70"...SourceThe data is extracted from the ExpressionSet of the R package yeastCC.ReferencesSpellman,P.T.,Sherlock,G.,Zhang,M.Q.,Iyer,V.R.,Anders,K.,Eisen,M.B.,...&Futcher,B.(1998).Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomycescerevisiae by microarray hybridization.Molecular biology of the cell,9(12),3273-3297.Dudoit S(2016).yeastCC:Spellman et al.(1998)and Pramila/Breeden(2006)yeast cell cycle microarray data.R package version1.12.0.Examplesdata(cdc15)qqnorm(colMeans(cdc15,na.rm=TRUE))qqnorm(rowMeans(cdc15,na.rm=TRUE))cdc28Microarray time series experiment for yeast cell cycle from cdc28ex-perimentDescription6,178yeast genes expression measures(log-ratios)with series length17from the cdc28experiment. Usagedata(cdc28)8fitHRegFormatMatrix with6178rows and17columns.Some missing data.Rows and columns are labelled.-attr(*,"dimnames")=List of2..$:chr[1:6178]"Y AL001C""Y AL002W""Y AL003W""Y AL004W".....$:chr[1:17]"cdc28.0""cdc28.10""cdc28.20""cdc28.30"...SourceThe data is extracted from the ExpressionSet of the R package yeastCC.ReferencesSpellman,P.T.,Sherlock,G.,Zhang,M.Q.,Iyer,V.R.,Anders,K.,Eisen,M.B.,...&Futcher,B.(1998).Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomycescerevisiae by microarray hybridization.Molecular biology of the cell,9(12),3273-3297.Dudoit S(2016).yeastCC:Spellman et al.(1998)and Pramila/Breeden(2006)yeast cell cycle microarray data.R package version1.12.0.Examplesdata(cdc28)qqnorm(colMeans(cdc28,na.rm=TRUE))qqnorm(rowMeans(cdc28,na.rm=TRUE))fitHReg Fits Three Parameter Harmonic RegressionDescriptionEstimates A,B and f in the harmonic regression,y(t)=mu+A*cos(2*pi*f*t)+B*sin(2*pi*f*t)+e(t).The default algorithm is enumerative but an exact non-linear LS option is also provided.UsagefitHReg(y,t=1:length(y),algorithm=c("enumerative","exact"))Argumentsy series.t Time points.algorithm method for the optimizationDetailsProgram is interfaced to C for efficient computation.ValueObject of class"HReg"produced.pgram9 Author(s)A.I.McLeod and Yuanhao LaiExamplesset.seed(193)z<-simHReg(10,f=2.5/10,1,1)ans<-fitHReg(z)ans$freq#optimal frequency=0.2376238##ORF06806in Cc dataset.z<-c(0.42,0.89,1.44,1.98,2.21,2.04,0.82,0.62,0.56,0.8,1.33)ans2<-fitHReg(z,algorithm="exact")sum(resid(ans2)^2)#0.2037463ans1<-fitHReg(z)sum(resid(ans1)^2)#0.242072#compare with nls()t<-1:length(z)ans<-nls(z~mu+alpha*cos(2*pi*lambda*t+phi),start=list(mu=1,alpha=1,lambda=0.1,phi=0.0))coefficients(ans)sum(resid(ans)^2)#0.2037pgram Periodogram computationDescriptionThe periodogram is computed.Usagepgram(z,fr="default",method=c("periodogram","regression"))Argumentsz time series vector of length n,say.fr use"default"for usual Fourier frequencies,1/n,...,floor(n/2)/n.Set fr=N,to evaluate the periodogram at the Fourier frequencies corresponding to a time se-ries of length N.Finally set fr to any desired set of frequencies.Note frequenciesare in cycles per unit time sometimes called temoral frequency to distinguishfrom angular frequency.Both are widely used in time series.method either periodogram or regression10ptestg DetailsUses FFT.So if the length of z is a highly composite number,the computation is very efficient.Otherwise the usual DFT is used.ValuePeriodogram evaluated at the Fourier frequencies or R-square.Author(s)A.I.McLeod and Yuanhao LaiExamplesz<-sunspot.yearn<-length(z)I<-pgram(z)f<-I[,1]I<-I[,2]plot(f,I,xlab="f",ylab="f",type="l")title(main="Periodogram for Annual Sunpots,1700-1988")#z<-c(0.42,0.89,1.44,1.98,2.21,2.04,0.82,0.62,0.56,0.8,1.33)fr<-(1:50)/101pgram(z)pgram(z,fr=101)pgram(z,fr=fr)pgram(z,method="regression")pgram(z,method="regression",fr=101)pgram(z,method="regression",fr=fr)ptestg Test short time series for periodicity based on periodogramsDescriptionThis function is used to test the existence of the periodicity for a short time series(length<=100).Several methods based on periodograms are provided with the response surface method imple-mented for efficiently obtaining accurate p-values.Usageptestg(z,method=c("Fisher","robust","extended","extendedRobust","FisherRSR"),multiple=FALSE)ptestg11Argumentsz A series or a matrix containg series as columnsmethod The statistical test to be used.See details for more information.multiple Indicating whether z contains multiple series.DetailsThe null hypothesis is set as no peridicities,H0:f=0.Discriptions of different test statistics(meth-ods)are as follow:Fisher:The Fisher’s g test statistic.The p-value is computed directly from the exact distribution.robust:The robust g test proprosed in Ahdesmaki et al.(2005),where the p-value is computed bythe response surface regression method.extended:The extended Fisher’s g test statistic,which extend the Fisher’s g test by enlarging thesearching region of the frequency from the fourier frequencies to be En=j/101|j=1,...,50andj/101≥1/n.The p-value is computed by the response surface regression method.extendedRobust:Extend the frequency searching region of the robust En=j/101|j=1,...,50andj/101≥1/n.The p-value is computed by the response surface regression method.FisherRSR:Only for experimental purposes,the Fisher;s g test with p-value computed form theresponse surface regression method.ValueObject of class"Htest"produced.An object of class"Htest"is a list containing the following components:obsStat Vector containing the observed test statistics.pvalue Vector containing the p-values of the selected tests.freq Vector containing the estimated frequencies.Author(s)Yuanhao Lai and A.I.McLeodReferencesFisher,R.A.(1929).Tests of significance in harmonic analysis.Proc.Roy.Soc.A,125,54-59.Ahdesmaki,M.,Lahdesmaki,H.,Pearson,R.,Huttunen,H.,and Yli-Harja O.(2005).BMC Bioin-formatics6:117./1471-2105/6/117.MacKinnon,James(2001):Computing numerical distribution functions in econometrics,Queen’sEconomics Department Working Paper,No.1037.See AlsoptestRegExamples#Simulate the harmonic regression model with standard Gaussian error termsset.seed(193)##Non-Fourier frequencyz<-simHReg(n=14,f=2/10,A=2,B=1,model="Gaussian",sig=1)ptestg(z,method="Fisher")ptestg(z,method="robust")ptestg(z,method="extended")ptestg(z,method="extendedRobust")ptestg(z,method="FisherRSR")#Performe tests on the alpha factor experimentdata(alpha)##Eliminate genes with missing observationsalpha.nonNA<-alpha[complete.cases(alpha),]##Using the multiple option to do the test for all the genes##Transpose the data set so that each column stands for a genealpha.nonNA<-t(alpha.nonNA)result<-ptestg(alpha.nonNA,method="extended",multiple=TRUE)str(result)#The movtivating example:gene ORF06806in Ccdata(Cc)x<-Cc[which(rownames(Cc)=="ORF06806"),]plot(1:length(x),x,type="b",main="ORF06806",xlab="time",ylab="Gene expression")ptestg(x,method="Fisher")#Fail to detect the periodicityptestg(x,method="robust")ptestg(x,method="extended")ptestReg Test short time series for periodicity with maximum likelihood ratiotestsDescriptionThis function is used to test the existence of the periodicity for a short time series(length<=100).Likelihood ratio tests under the Gaussian or the Laplace assumptions are provided with the response surface method implemented for efficiently obtaining accurate p-values.UsageptestReg(z,method=c("LS","L1"),multiple=FALSE)Argumentsz A series or a matrix containg series as columnsmethod The statistical test to be used.See details for more information.multiple Indicating whether z contains multiple series.DetailsThe null hypothesis is set as no peridicities,H0:f=0.Discriptions of different test statistics(meth-ods)are as follow:LS:The-2loglikelihood ratio test statistic based on the likelihood ratio test with normal noises, where the p-values are efficiently computed by the response surface method.L1:The-2loglikelihood ratio test statistic based on the likelihood ratio test with Laplace noises, where the p-values are efficiently computed by the response surface method.ValueObject of class"Htest"produced.An object of class"Htest"is a list containing the following components:obsStat Vector containing the observed test statistics.pvalue Vector containing the p-values of the selected tests.freq Vector containing the estimated frequencies.Author(s)Yuanhao Lai and A.I.McLeodReferencesIslam,M.S.(2008).Peridocity,Change Detection and Prediction in Microarrays.Ph.D.Thesis,The University of Western Ontario.Li,T.H.(2010).A nonlinear method for robust spectral analysis.Signal Processing,IEEE Trans-actions on,58(5),2466-2474.MacKinnon,James(2001):Computing numerical distribution functions in econometrics,Queen’s Economics Department Working Paper,No.1037.See AlsofitHReg,ptestgExamples#Simulate the harmonic regression model with standard Gaussian error termsset.seed(193)#Non-Fourier frequencyz<-simHReg(n=14,f=2/10,A=2,B=1,model="Gaussian",sig=1)ptestReg(z,method="LS")#Normal likelihood ratio testptestReg(z,method="L1")#Laplace likelihood ratio testfitHReg(z,algorithm="exact")#the nls fitted result#Performe tests on the alpha factor experimentdata(alpha)##Eliminate genes with missing observationsalpha.nonNA<-alpha[complete.cases(alpha),]##Using the multiple option to do the test for all the genes##Transpose the data set so that each column stands for a genealpha.nonNA<-t(alpha.nonNA)result<-ptestReg(alpha.nonNA,method="LS",multiple=TRUE)str(result)#The movtivating example:gene ORF06806in Ccdata(Cc)x<-Cc[which(rownames(Cc)=="ORF06806"),]plot(1:length(x),x,type="b",main="ORF06806",xlab="time",ylab="Gene expression")ptestg(x,method="Fisher")#Fail to detect the periodicityptestReg(x,method="LS")#The periodicity is significantly not zeroptestReg(x,method="L1")#The periodicity is significantly not zerosimHReg Simulate harmonic regression modelsDescriptionSimulates a harmonic regression.Possible types of models are normal,t(5),Laplace,cubic and AR1.UsagesimHReg(n,f,A,B,model=c("Gaussian","t5","Laplace","cubic","AR1"),phi=0,sig=1)Argumentsn Length of series.f Frequency.A Cosine amplitude.B Sine amplitude.model The model used for generating the error term.See details.phi Only used if AR1error distribution is selected.sig The standard error of the series.DetailsGenerate a harmonic series y with length n,where y t=A∗cos(2∗pi∗f∗t)+B∗sin(2∗pi∗f∗t)+sig∗e t,t=1,...,n,and e comes from one of the following specified distributions with mean0and standard error1:Gaussian:A standard normal distribution(i.i.d.).t5:A t distribution with5degrees of freedom(i.i.d.,standardized to mean0and variance1).Laplace:A Laplace(double exponential)distribution(i.i.d.,standardized to mean0and variance1).cubic:A standard normal distribution for e,but y=y3this time.AR1:An AR(1)series with autocorrelation paramater phi(standardized to mean0and variance1).ValueVector of length n,simulated harmonic series.Author(s)A.I.McLeod and Yuanhao LaiReferencesMcLeod,A.I.,Yu,Hao and Krougly,Z.(2007),Algorithms for Linear Time Series Analysis:With R Package,Journal of Statistical Software23,51-26.See AlsofitHReg,ptestRegExamples#Simulate the harmonic regression model with standard Gaussian error termsz<-simHReg(10,f=2/10,1,2,model="Gaussian",sig=1)#Fourier Frequencyplot(1:10,z,type="b")#Simulate the AR(1)errorsz<-simHReg(10,f=0/10,0,0,model="AR1",phi=0.2,sig=1)acf(z)Index∗datasetsalpha,2B1,3B2,4B3,5Cc,6cdc15,7cdc28,7∗tsfitHReg,8pgram,9ptestg,10ptestReg,12simHReg,14alpha,2B1,3B2,4B3,5Cc,6cdc15,7cdc28,7fitHReg,8,13,15pgram,9ptestg,10,13ptestReg,11,12,15simHReg,1416。
Inspector+操作规程
Inspector+操作规程标题：Inspector+操作规程引言概述：Inspector+是一种常用的软件工具，用于检查和验证代码的质量和合规性。
本文将详细介绍Inspector+的操作规程，包括安装、配置、使用和常见问题解决等方面。
一、安装Inspector+1.1 下载Inspector+安装包1.2 运行安装程序1.3 完成安装并进行基本配置二、配置Inspector+2.1 设置代码检查规则2.2 配置检查器选项2.3 配置检查结果输出方式三、使用Inspector+3.1 打开Inspector+工具3.2 导入代码文件3.3 运行代码检查四、分析Inspector+检查结果4.1 查看代码违规项4.2 理解违规项的意义4.3 修复代码违规项五、常见问题解决5.1 Inspector+无法启动的解决办法5.2 Inspector+检查结果不许确的解决办法5.3 Inspector+与其他工具冲突的解决办法正文内容：Inspector+是一款功能强大的代码检查工具，能够匡助开辟人员提高代码质量和合规性。
下面将详细介绍Inspector+的操作规程，匡助读者快速上手使用。
一、安装Inspector+1.1 下载Inspector+安装包：在官方网站上下载最新版本的Inspector+安装包。
1.2 运行安装程序：双击安装包，按照提示进行安装，选择安装路径和相关配置选项。
1.3 完成安装并进行基本配置：安装完成后，根据自己的需求进行基本配置，如选择默认检查规则和输出方式等。
二、配置Inspector+2.1 设置代码检查规则：根据项目需求，可以选择不同的代码检查规则，如代码风格、安全性、性能等方面的规则。
2.2 配置检查器选项：根据具体情况，可以对检查器选项进行配置，如设置检查的文件类型、忽略特定目录等。
2.3 配置检查结果输出方式：可以选择将检查结果输出到控制台、文件或者集成到开辟环境中。
提高Android测试效率的云测试平台推荐
提高Android测试效率的云测试平台推荐Android测试是移动应用开发中至关重要的环节之一，通过测试可以发现和纠正应用中的bug和问题，确保应用的质量和稳定性。
然而，传统的本地测试方法会受限于硬件资源和网络条件，效率较低。
为了提高Android测试的效率，推荐使用云测试平台，本文将介绍几个值得推荐的云测试平台。
一、蒲公英云测蒲公英云测是一家专业的移动云测试平台，对于Android测试提供了强大的支持。
它提供了各种测试场景和设备环境的模拟器，用户可以根据实际需求选择相应的设备进行测试。
通过蒲公英云测，测试人员可以远程上传测试包进行自动化测试，同时还支持多人协作和实时沟通，方便团队协作。
另外，蒲公英云测还提供了丰富的测试报告和分析工具，帮助开发人员更好地了解应用在不同设备上的表现和问题。
二、Testin云测Testin云测是国内领先的移动云测试服务提供商，也是Android测试的不错选择。
Testin云测拥有全球领先的真机库，覆盖了大量的设备型号和系统版本，确保了用户可以进行广泛的测试。
此外，Testin云测还提供了高效的自动化测试工具，可以根据用户的需求进行灵活的设置和调整。
Testin云测还拥有强大的性能测试和压力测试功能，可以模拟各种复杂的网络环境和用户行为，帮助开发人员及时发现潜在的性能问题。
三、Firebase Test LabFirebase Test Lab是谷歌推出的云测试平台，专门为Android应用提供测试服务。
通过Firebase Test Lab，开发人员可以轻松地上传应用进行各种测试，包括功能测试、兼容性测试和性能测试等。
Firebase Test Lab提供了广泛的设备和系统版本的覆盖，测试人员可以根据实际需求选择适合的设备进行测试。
此外，Firebase Test Lab还与Firebase平台紧密集成，开发人员可以方便地查看测试结果和监控应用的性能情况。
四、AWS Device FarmAWS Device Farm是亚马逊推出的移动云测试服务。
【PIPEPHASE软件】使用说明
【PIPEPHASE软件】使用说明【PIPEPHASE软件】使用说明1：简介1.1 项目背景1.2 软件目标2：系统要求2.1 硬件要求2.2 软件要求3：安装和配置3.1 安装准备3.2 安装文件3.3 运行安装程序3.4 进行配置4：用户登录与账户管理4.1 创建新账户4.2 登录系统4.3 修改密码4.4 忘记密码5：菜单导航与功能介绍5.1 主界面概述5.2 菜单导航5.2.1 文件管理5.2.2 工程管理5.2.3 模拟计算5.2.4 结果分析5.2.5 报告输出5.3 功能介绍5.3.1 文件创建与保存 5.3.2 工程数据管理 5.3.3 模拟计算设置 5.3.4 结果可视化5.3.5 报告与导出6：模拟计算设置6.1 模拟类型选择6.2 输入参数设置6.2.1 流体性质参数 6.2.2 管道几何参数 6.2.3 边界条件设置 6.2.4 计算控制参数 6.3 模拟结果检查7：结果分析与可视化7.1 数据导入与处理7.2 数据分析与计算7.3 结果可视化方式7.3.1 曲线图展示 7.3.2 热力图展示 7.3.3 三维模型展示8：报告输出与导出8.1 报告编辑8.2 报告导出格式选择8.3 报告与保存9：常见问题解答9.1 安装问题9.2 登录问题9.3 模拟计算问题9.4 结果分析问题10：反馈与支持10：1 反馈渠道10：2 技术支持附件:附件1：PIPEPHASE软件安装包附件2：用户手册法律名词及注释：1：版权：指对作品享有的法律保护和控制权利。
2：许可证：指获得某项特定活动的批准或许可的文件。
3：商标：指一种在商品上所使用的标记，用以区分同类商品的来源和质量。
4：保密协议：指签订的双方同意在一定时间内保守对方提供的机密信息的文件。
ResultsPlus 用户指南说明书
ResultsPlus – BTEC Nationals Step-by-step processStep 1 – Login to ResultsPlus using your EdexcelOnline credentialsLeave the default authentication mode as EOL (EdexcelOnline)Step 2 – If your centre has subsites, select the relevant subsite from the drop-down menu at the top of the screen where your learners are registered, e.g. 99999AStep 3 – Select the BTEC Analysis tabStep 4 – You can search for results either by Student or by Cohort .Get student resultsStep 1 – To search by Individual Student , enter the learner’s details.You can search by either the student registration numberYou can search by their personal details (e.g. forename, surname).If you enter the students’ registration number , you will be directed to their personal record.If you entered their personal details , such as a name, you will be presented with a list of all students matching your search criteria as displayed below.orStep 4Student results overview Press the VIEW tab to view a detailed breakdown of the learner’s external assessment performanceStep 3 – A breakdown of the learners’ performance will be displayed, including:•Qualification grade if certificated •Date external assessment(s) undertaken •Exam or Task based •Unit score achieved • External assessment grade achievedStep 4 – Press the VIEW tab next to the external assessment grade to view the unit performance in more detail, such as by individual questionUnit AnalysisThe top section provides an overview of the students’ details, such as:•Name •Registration number •Qualification details •Unit details •Session test undertaken • Unit mark achievedAdditionally, you will have access to a wide range of information by using the tabs to navigate between:Step 2 – Select VIEW on the student results you wish to view.• Unit analysis• Highlight report• Learning aims (which show how performance links to specific topics and skills)• Unit docs (papers, mark schemes and examiner reports)• For each question you can see the score achieved by this student and the maximum score• For most papers you will need to scroll down the page to see all scores• The percentage/performance column helps you see at a glance how well the student performed on each question• You can sort any column to quickly identify strengths and weaknesses• Pearson averages help you compare your students’ performance with all BTEC candidates• Variance helps you see quickly where your students outperformed or underperformed against Pearson averages. If the student achieved a higher average score than the BTEC average, the variance will appear green. Red indicates a score lower than theBTEC average• For subjects with learning aims reports, you can see which topic or skill was tested in that questionHighlight reportThe Highlight report screen helps you to filter question analysis to show the best and worst areas of performance for each student.• The drop-down menus allow you to select between 1 and 10 best/worst questions• You can choose whether to view a student’s best/worst questions in relation to the Pearson average, or in absolute terms• Where learning aims are available, scrolling further down the page allows you to sort the results according to a student’s best and worst skill/topic areaLearning aimsThe analysis for many BTEC qualifications includes a learning aims detail which link students’ performance on assessments to topics and skills from the specification.• Learning aims allow you to see how your students performed on topic or skill areas.• Topics and skills are arranged in a tree-structure. You can click on headings to see more detail, or contract the view to just see the main topics.• For each topic or skill, you can see how many marks your student scored and the maximum number of marks available.• Pearson averages and variance help you to see how your students have performed in relation to other learners.Accessing external assessment documentsThe Unit docs tab provides access to downloadable pdf versions of examination papers, lead examiner feedback and mark schemes.Click on the document that you wish to access to download it straight to your computer.Get whole cohort resultsCohort paper analysis allows you to analyse your whole cohort’s performance for a particular external test.Step 1 – To search by a Cohort, use the calendar to select the month and year in which the test was undertaken and press SEARCH.You will be presented with a list of all qualifications and units for your centre for the session you have selected.Cohort analysis works in the same way as that for an individual student and provides similar information, such as date, assessment type, score, etc.The number (e.g. 4) within the Students column indicates how many students are within the cohort. You can view a list of the students within any specific cohort by pressingthis number.Step 2 – Press the VIEW tab next to the external assessment unit to view a breakdownof the cohort unit performance.The top section provides a breakdown by number of students and grade achieved.Move the cursor over the grade bar to display the number of students who achieved thevarious grades.As with individual student analysis, you will have access to a wide range of information byusing the tabs to navigate between:• Unit analysis• Highlight report• Learning aims (which show how performance links to specific topics and skills)• Unit docs (papers, mark schemes and examiner reports)9。
PSASP基础教程
PSASP基础教程PSASP（Power System Analysis Software Package）是一种用于进行电力系统分析的软件包。
它是一个强大的工具，用于模拟和评估电力系统的运行情况，并提供决策支持和系统规划。
本文将介绍PSASP的基本原理和使用方法。
一、简介1.1PSASP简介1.2PSASP的功能-最优潮流分析：用于确定系统中最小损耗和最优发电机设置。
-稳态稳定性分析：用于评估系统在各种异常和扰动下的稳定性。
-瞬态稳定性分析：用于模拟和评估系统在大扰动下的瞬态响应。
-谐波分析：用于评估电力系统中谐波的影响。
-电力负荷流动分析：用于确定电力负荷在系统中的分布。
-电力市场分析：用于模拟和评估电力市场中的各种交易。
二、PSASP的使用方法2.1安装PSASP2.2创建电力系统模型2.3运行分析完成电力系统模型后，可以运行各种分析操作。
PSASP提供了多个命令和操作，可以根据需要选择适当的分析方法。
-最优潮流分析：通过运行最优潮流命令，PSASP可以确定系统中的最优潮流分布，并计算出最小损耗和最优发电机设置。
-稳态稳定性分析：通过运行稳态稳定性命令，PSASP可以评估系统在各种异常情况下的稳定性，如突然断电或发电机失去同步等。
-瞬态稳定性分析：通过运行瞬态稳定性命令，PSASP可以模拟和评估系统在大扰动下的瞬态响应，如短路故障或大功率负荷变化等。
-谐波分析：通过运行谐波分析命令，PSASP可以评估系统中谐波的影响，并提供相应的补偿措施。
2.4结果分析和报告生成运行分析后，PSASP会生成各种结果数据，并提供丰富的图形界面和工具来分析和可视化这些结果。
用户可以根据需要导出结果数据，并生成详细的报告。
三、注意事项和常见问题-在创建电力系统模型时，要确保模型的准确性和完整性，以便获得可靠的分析结果。
-在运行分析之前，要仔细设置各个参数和选项，以便得到精确的分析结果。
-在分析过程中，如果遇到错误或异常情况，可以查阅PSASP的帮助文档或官方论坛，寻求帮助和解决方案。
Pearson BTEC Level 2在线测试指南说明书
Onscreen Tested Vocational Qualifications Centre GuidancePurposeThis document is intended to provide centres delivering the Pearson BTEC Level 2 in Retail Knowledge with information and guidance to help prepare learners for onscreen assessments.This guidance includes information about the onscreen test delivery software and details about the format, structure and coverage of the tests.This information should only be used in relation to onscreen testing and is not to be used for any other form of assessment. Further documents and forms relating to Pearson Onscreen Platform (POP) which is used to deliver onscreen tests can be found on our website here.Approval and registrationIn order to gain access to the onscreen assessment, you must first be a Pearson centre with approval to operate the programme. Please refer to the Information Manual on our website for further information about becoming an approved Pearson centre.You should also complete and return the Pearson Onscreen Platform Declaration form available on our website here. This form is used to request to be approved to run our onscreen tests and confirming that you meet the technical requirements needed to successfully run the software. Once we receive this we will provide you with your username and password for you to be able to access your onscreen tests.Registrations must be made on Edexcel Online. Registered learners will then need to be entered for particular testing occasions. You should ensure that learners are adequately prepared before each testing attempt. Please refer to the Schedule of Fees for information regarding fees. Learners are allowed up to three attempts for each test. Please note that further test attempts may incur additional fees.Onscreen test deliveryTests are available through the Pearson Onscreen Platform which you will be required to install and use for the delivery of onscreen tests.The Pearson Onscreen Platform Edexcel Onscreen Testing System has a useful help facility which provides guidance on the functions and layout of the system. Assessors and invigilators should familiarise themselves with the screen and ensure that there is time for learners to fully explore the information on the help screen before starting the test.All centres offering onscreen assessment must comply with the current Instructions for the Conduct of Examinations (ICE) document.Overview of testsThe tests will operate on a test banking system. Where a group of learners is taking a test at the same time, different learners will be presented with different tests from the bank. Each year all the tests will be reviewed and updated.Each test will have a set number of questions each worth 1 mark. Please refer to the Test structure section of this guidance for more detailed guidance. The main question format is to choose the correct response from one of four answers, either through answering a question or completing a statement. There is no use of questions with more than one right answer.No questions will require specific manipulation, such as “drag and drop” and there are no videos. Some images are used and may be presented in colour.The tests may use images both for the context of a question (e.g. showing a situation) or for the answer options (e.g. selecting the correct sign). The learner will be asked to select the correct picture for the right answer.Question typesThe tests will be comprised of both recall and application question types.Recall questions test the learner’s knowledge of the subject area. They are typically lower level questions and as such there will be more recall questions on a Level 2 test than there will be on a Level 3 test. An example of a recall question is: “When should work tasks be prioritised?”Application questions test whether the learner can apply the knowledge of the subject area to a situation given in the question. These questions are higher level questions as they are testing more than just knowledge. As such, there will be more application questions on a Level 3 test than in a Level 2 test. An example of an application question is: “Time is running out on a project. What action should be taken?”Test structureFor the purposes of assessment, all the content of the published specification will be considered to be open to testing in detail against any of the related assessment criteria statements. Each test will provide a broad test of key principles and typical situations found in an adult social care environment. Learners will be assessed across all the learning outcome statements to provide adequate evidence of learning and achievement.The unit content found in the specification details the knowledge and understanding required in order for learners to be successful in the onscreen test. While all the knowledge cannot be tested within one test, the different versions of the test will all cover this knowledge. Therefore it is essential that learners are deemed to have a full knowledge of the test specification content before being entered for the onscreen test.Test items will not necessarily be sequenced in the order of the criteria. No test item will rely on or directly follow on from another test item.Learners are advised to use the time allocated for the test carefully. All questions in the test should be attempted. Learners are advised to use the “flag” facility to mark questions that they wish to return to when they have answered the other questions in the test.All tests are graded pass/fail.After completing the test, each learner will receive a score report which will show the learner’s individual strengths and weaknesses against the areas covered on the test. Unsuccessful learners should use this information when revising to re-take the test.Learners who are unsuccessful will be eligible to re-take the test on the following day. However, it is strongly recommended that a period of revision against weak areas identified on the score report takes place before the test is attempted again.The tables below give some guidance on which units are assessed as part of each test, the amount of questions and the duration of the tests. The number of questions in a test is related to the unit being assessed, the level and credit rating.Pearson BTEC Level 2 in Retail KnowledgeFeedback∙For queries relating to onscreen tests, guidance information can be found on our website.∙For general queries about BTEC tested qualifications, please email:************************∙For information about registering for onscreen testing, or for any technical queries, please contact your dedicated account specialist you can find contact information on our website.To provide us feedback on live test content, please email ****************************Please include as much detail as possible (without emailing any secure content); including the qualification title, question number, test name/number, centre number, candidate number, and date/time that test was taken.For information about Edexcel, BTEC or LCCI qualifications visit Pearson Education Limited. Registered in England and Wales No. 872828Registered Office: 80 Strand, London WC2R 0RLVAT Reg No GB 278 537。
网络测试工具使用方法二：安装与设置步骤解析(二)
网络测试工具使用方法二：安装与设置步骤解析随着互联网的飞速发展，我们越来越依赖网络来进行工作和生活。
在大数据时代，网络性能的稳定与可靠性对于许多行业来说至关重要。
为了确保网络的正常运行，网络测试工具成为了必不可少的工具之一。
在上一篇文章中，我们了解了网络测试工具的基本概念和分类。
本文将继续介绍如何安装和设置这些工具。
一、安装网络测试工具安装网络测试工具可以通过多种方式，我们可以从官方网站下载安装包，也可以通过命令行工具来进行安装。
以下是一些常见的网络测试工具的安装方法：1. iPerfiPerf是一款用于测量网络带宽性能的工具。
你可以在iPerf的官方网站上找到最新的安装包，并按照指示进行安装。
如果你使用的是Linux系统，可以通过包管理器来安装，例如在Debian/Ubuntu系统中，可以使用以下命令进行安装：sudo apt-get install iperf。
2. WiresharkWireshark是一个强大的网络协议分析工具。
你可以在Wireshark 的官方网站上下载适合你所使用操作系统的版本，并按照指示进行安装。
在Windows系统中，你只需运行exe 安装程序，并按照向导进行操作即可。
3. NmapNmap是一个网络端口扫描工具，用于检测网络主机和开放的端口。
你可以在Nmap的官方网站上找到适合你所使用操作系统的安装包，并按照指示进行安装。
如果你使用的是Linux系统，可以使用包管理器进行安装，例如在Debian/Ubuntu系统中，可以使用以下命令进行安装：sudo apt-get install nmap。
二、设置网络测试工具安装完网络测试工具之后，我们还需要进行一些设置来确保工具能正常运行并提供准确的测量结果。
以下是一些常见的网络测试工具设置步骤：1. iPerf 设置首先，我们需要确定一台机器作为服务器，另一台机器作为客户端。
在服务器上运行iPerf程序，并以服务器模式启动。
pvs-studio使用方法
PVS-Studio是一款用于检测C、C++、C#和Java程序中的错误和漏洞的静态代码分析工具。
它可以帮助开发人员提高代码质量，减少bug并提高程序的稳定性。
下面将介绍PVS-Studio的使用方法。
一、安装PVS-Studio1. 下载PVS-Studio安装包，并按照安装向导进行安装。
2. 安装完成后，打开PVS-Studio，输入许可证密钥进行激活。
二、配置项目1. 在PVS-Studio中创建一个新的分析项目，选择要分析的源代码文件或文件夹。
2. 配置项目的参数，如选择分析的编译器、语言、评台等。
三、进行静态代码分析1. 在PVS-Studio中选择“开始分析”按钮，工具会对项目中的代码进行静态分析，并输出分析结果。
2. 分析结果将包括代码中的错误、警告、优化建议等信息，开发人员可以根据这些信息对代码进行优化和改进。
四、查看分析报告1. PVS-Studio会生成详尽的分析报告，将代码中的问题进行分类并给出详细的说明。
2. 开发人员可以根据报告中的提示逐一解决代码中的问题，并进行重新分析。
五、集成到开发环境1. PVS-Studio提供了与多个主流集成开发环境（IDE）的插件，如Visual Studio、CLion等，开发人员可以在IDE中直接使用PVS-Studio进行静态代码分析。
2. 在IDE中，开发人员可以看到代码中的问题，并快速定位并解决。
六、定制化设置1. PVS-Studio提供了丰富的设置选项，用户可以根据自己的需求定制分析参数，如排除特定文件、设置规则等。
2. 定制化设置可以帮助用户更精准地进行静态代码分析，减少误报和漏报。
七、与团队协作1. PVS-Studio支持多人协作，团队成员可以共享分析结果、报告和设置。
2. 这样可以帮助团队成员更好地了解代码质量，并进行统一的优化和改进。
总结PVS-Studio是一款强大的静态代码分析工具，它可以帮助开发人员发现并解决代码中的错误和漏洞，提高代码质量并降低bug率。
bunintest使用说明
bunintest使用说明bunintest是一个用于测试软件或应用程序的工具。
它提供了一套简单易用的功能，帮助开发人员和测试人员进行软件测试。
下面是关于如何使用bunintest的详细指南：1. 下载和安装：首先，您需要从官方网站上下载bunintest的安装程序。
安装程序可以在Windows、Mac和Linux操作系统上运行。
请确保您的计算机满足最低系统要求，并且具有管理员权限。
2. 准备测试环境：在使用bunintest之前，您需要设置和准备好要测试的环境。
这包括安装任何依赖项、配置必要的参数和准备测试数据等。
确保环境设置正确，并且您具有运行测试所需的权限。
3. 创建测试用例：接下来，您可以使用bunintest创建测试用例。
测试用例是一组定义了期望行为的步骤，从而验证软件是否按预期工作。
您可以使用bunintest 提供的图形化界面或命令行界面创建测试用例。
4. 运行测试：一旦您创建了测试用例，您可以开始运行测试。
bunintest将按照测试用例的定义自动执行一系列步骤，并记录每个步骤的结果。
您可以随时中断测试过程，查看中间结果，或者重新运行失败的测试。
5. 分析结果：测试完成后，您可以使用bunintest的结果分析功能来评估每个测试步骤的执行结果。
它将显示每个步骤的成功或失败状态，并提供详细的日志和报告。
您可以根据结果调整软件或应用程序的开发和测试策略。
6. 清理和维护：测试完成后，请确保清理测试环境并保持其良好状态。
删除临时文件、重置配置文件和清理数据是保持环境一致性的重要步骤。
定期维护和更新bunintest也是保持其性能和功能的关键。
通过遵循以上步骤，您可以有效地使用bunintest进行软件测试工作。
无论是初学者还是有经验的测试人员，bunintest都为您提供了一个简化和自动化测试过程的工具。
请记住，良好的测试实践和细致的测试方法是确保软件质量的关键。
UPNP测试方案20120203
UPNP功能测试方案Prepared by拟制范步泰Date日期2012-2-3Reviewed by 审核人Date 日期Authorized by批准Date 日期SHENZHEN ZOWEE TECHNOLOGY Co., Ltd.深圳卓翼科技股份有限公司All rights reserved版权所有侵权必究（仅供内部使用)1、概述UPnP协议统一即插即用英文是Universal Plug and Play，缩写为UPnP。
支持Upnp 协议的设备可以被所有网络上的设备发现, 彼此能通讯, 更能控制使用, 真正的即插即用，UPnP 检测协议是基于简单服务发现协议（SSDP）.经典应用: NAT 穿越1)Router:打开Upnp功能, 能被网络上的设备识别和控制.2)支持Upnp的P2P软件(如: BitComet:V1.31)BitComet的UPnP功能可以通过Upnp协议控制路由器, 自动进行端口映射，将内网电脑的端口打开, 便于外网访问。
2、测试步骤2.1 DUT设置1)DUT上打开UPnp开关。
2.2 Winxp系统设置为了启用UPnP，请按照以下步骤操作：1）点击"开始"，点击"控制面板"，然后点击"添加或删除程序"。
2）在"添加或删除程序"对话框中，点击"添加/删除Windows组件"。
3）在"Windows组件向导"中，点击"网络服务"，点击"详细"，然后选择"Upnp用户界面"复选框。
4）点击"确定"，然后点击"Windows组件向导"对话框中的"下一步"。
您可能需要提供您的Windows XP安装CD。
5) 添加程序完成后，查看网络连接能观察到新增一个Internet网关。
常用系统测试工具的快捷键大全
常用系统测试工具的快捷键大全系统测试是软件开发过程中非常重要的一项工作，通过对系统进行全面的功能性、兼容性、性能等方面的测试，可以保证软件的质量和稳定性。
在进行系统测试时，熟练使用快捷键可以提高工作效率和操作准确性。
本文将为大家介绍一些常用系统测试工具的快捷键，方便测试人员在测试过程中快速、准确地完成各项操作。
1. Web 浏览器测试工具1.1 Google Chrome- 打开开发者工具：Ctrl + Shift + I- 刷新页面：Ctrl + R 或 F5- 清除缓存并刷新页面：Ctrl + Shift + R- 在新标签页中打开链接：Ctrl + 左键- 调出控制台：Ctrl + Shift + J- 关闭当前标签页：Ctrl + W- 打开任务管理器：Shift + Esc1.2 Mozilla Firefox- 打开开发者工具：Ctrl + Shift + I- 刷新页面：Ctrl + R 或 F5- 清除缓存并刷新页面：Ctrl + Shift + R- 在新标签页中打开链接：Ctrl + 左键- 调出控制台：Ctrl + Shift + K- 关闭当前标签页：Ctrl + W- 打开任务管理器：Shift + Esc2. 移动端测试工具2.1 Android Studio- 运行项目：Shift + F10- 调试项目：Shift + F9- 启动模拟器或连接真机调试：Shift + F9 - 停止调试：Shift + F2- 重启调试：Ctrl + F5- 调出 Logcat 日志：Alt + 6- 搜索类或方法：Ctrl + O2.2 Xcode- 运行项目：Ctrl + R- 调试项目：Ctrl + D- 启动模拟器或连接真机调试：Ctrl + D - 停止调试：Ctrl + . (点号)- 清除模拟器数据：Cmd + Shift + K - 调出控制台：Shift + ⌘ + C- 调出帮助文档：Shift + ⌘ + 03. 性能测试工具3.1 JMeter- 开始/停止测试：Ctrl + R- 新建线程组：Ctrl + T- 新建 HTTP 请求：Ctrl + H- 查找：Ctrl + F- 复制当前选中的请求：Ctrl + C- 粘贴请求到当前位置：Ctrl + V- 保存测试计划：Ctrl + S3.2 LoadRunner- 开始/停止测试：F5- 新建 Vuser 脚本：Ctrl + T- 运行当前脚本：F5- 打开监视器：Ctrl + M- 查找文本：Ctrl + F- 复制选中的内容：Ctrl + C- 粘贴内容到当前位置：Ctrl + V4. 接口测试工具4.1 Postman- 发送请求：Ctrl + Enter- 保存请求：Ctrl + S- 新建请求：Ctrl + N- 复制选中请求：Ctrl + C- 粘贴请求到当前位置：Ctrl + V- 新建文件夹：Ctrl + Shift + N- 打开控制台：Ctrl + Alt + C4.2 SoapUI- 运行测试用例：F9- 运行当前选中的测试步骤：Ctrl + F9 - 停止测试：Shift + F9- 保存测试项目：Ctrl + S- 导入 WSDL 文档：Ctrl + I- 查找：Ctrl + F- 新建测试套件：Ctrl + N以上仅为常用系统测试工具的一些快捷键，不同版本的工具可能会有一些略微的差异。
安全测试工具推荐与使用指南
安全测试工具推荐与使用指南一、引言随着互联网的快速发展，网络安全问题变得日益严峻。
为了保护个人和企业的隐私和财产安全，安全测试工具成为重要的选择。
本文将为您推荐几款常用的安全测试工具，并为您提供使用指南，帮助您保障网络安全。
二、推荐的安全测试工具1. Burp SuiteBurp Suite 是一款功能强大的网络安全测试工具，它主要用于检测Web应用的漏洞。
该工具提供了代理、扫描和攻击等多个模块，具备对常见漏洞如SQL注入和跨站脚本攻击的检测能力。
可以使用Burp Suite进行安全测试，找出应用中的安全漏洞并提供修复建议。
2. NmapNmap 是一款开源的网络发现和安全扫描工具。
通过Nmap，您可以快速侦测目标主机上的服务、开放端口以及网络拓扑结构。
它还支持针对远程主机的实时实用工具，例如操作系统类型与版本的识别、服务和版本的探测、漏洞扫描等。
3. WiresharkWireshark 是一款网络封包分析软件。
通过Wireshark，您可以捕获和分析网络数据包，了解网络流量、检测网络问题、分析网络协议等。
它支持多种操作系统，并提供强大的过滤和分析功能。
4. MetasploitMetasploit 是一款用于渗透测试和漏洞开发的平台。
它是一个完整的开发工具包，具备模块化架构，并提供了多种漏洞探测和利用的工具。
Metasploit 有助于安全专业人士评估和增强安全，确保系统和网络的安全性。
5. NessusNessus 是一款广泛使用的漏洞扫描工具。
它可以帮助您快速发现网络上存在的漏洞，并提供修复建议。
Nessus 支持全面的安全扫描，包括端口扫描、漏洞检测、恶意软件检测等。
三、安全测试工具的使用指南1. 熟悉工具界面和功能在使用安全测试工具之前，您需要花一些时间来熟悉工具的界面和功能。
了解工具的各个模块和选项可以帮助您更好地进行安全测试和分析。
2. 设置测试目标和范围在进行安全测试时，您需要明确测试的目标和范围。
Test Lab 使用教程
函数视图中的鼠标和键盘快捷操作在校准页面中勾选所有想要校准的通道。
建议在高级标签中，将“超时终止”勾选项取消。
这样你就有足够的时间在校准完一个传感器后换下一个校准。
或者把“检测时间”改长一些，改到足够换一个传感器标定的时间长度。
然后点击看“开始校准”，软件会基于信噪比自动识别有信号的通道，当一个通道校准完时，取下标定器上的传感器换下一个传感器。
在前后图显示中右键选择“预览模式”。
在预览模式下，你可以用Ctrl键选择一条或多条曲线显示在前后图上。
按一下右箭头，光标移到下一个采样点；按一下左箭头，光标移到上一个参考点。
按一下Ctrl+右箭头，光标移动到下一个最大值点；按一下Ctrl+左箭头，光标移动到上一个最大值点。
双击光标输入x轴数值可以移动光标到该位置。
改变默认光标1)在前后视图上右键选中选项。
2)更改光标的线条风格，比如说线条类型。
3)保存视图布局。
你可以新建一个视图布局，或者覆盖默认的前后视图布局。
如果你覆盖默认视图，所有新建的前后视图都会使用新的线条风格。
4)对其他的视图布局做类似的更改。
123复制视图设置到其他视图无需每次打开新视图时都重新设置视图布局。
可以复制一个视图的设置到另一个。
在需要被复制的视图下点击右键-> 视图格式-> 复制。
在需要改变的视图中点击右键-> 视图格式-> 粘贴在一组视图内，如果需要把所有的视图统一成一种格式，则点击右键-> 视图格式->匹配前后视图格式。
在视图上添加标题添加标题：1)视图背景上右键。
2)选择“标题注释”会产生一个标题框，在标题框上右键选择“注释选项”。
3)在标签“标题内容”下添加标题，在标签“标题布局”下改变标题的字体等。
123调整视图大小：按下Crtl键并拖动视图边框在LMS b中可以快捷改变视图尺寸——按下Ctrl键并且拖动视图边框。
视图的快捷复制在视图的标签上左键弹出下拉菜单，选择“复制”会生成该视图的复本。
Compuware TestPartner 5.3 辅助功能说明说明书
TestPartner 5.3: Specific Accessibility Information Compuware is committed to making its products and services easier for everyone to use. This document provides information about the features that make this product more accessible for people with disabilities.This product supports and/ or does not disrupt, with few exceptions,MS-Windows® accessibility features and MS-Windows-based Assistive Technology (AT) devices, software such as Braille devices, screen readers, magnifiers, etc.Customizing WindowsAccessibility features are built into Windows. These features are most useful for individuals who:•have difficulty typing or using a mouse,•are blind or have low vision, or•who are deaf or hard-of-hearing.These features may be installed during setup, or they may be addedlater using Windows installation disks. More information oninstallation and usage may be found by looking up "accessibility" inthe Windows Help Index.Some accessibility features built into Windows NT and higher may be added to earlier versions of those products and to MS-DOS using Access Pack files. These files from /enable/.Microsoft provides detailed instructions on how to use theaccessibility features, and a Step-by-Step guide is available at/enable/training/default.aspxUsing the KeyboardKeyboard access is designed to make the entire application usablewithout a mouse.Press ToDisplay or hide the Start menu.CTRL+ESC Display or hide the Start menu(same as ).CTRL+ALT+DELETE Display Windows Security screen orWindows Task Manager.+BREAK Display the System Propertiesdialog box.+D Show the desktop.+B Set focus on a notification.+M Minimize all windows.+Shift+M Restore minimized windows.+E Open My Computer by WindowsExplorer.+F Search for a file or folder.CTRL++F Search for computers.+F1 Display Windows Help.+L Lock your computer if you areconnected to a network domain, orswitch users if you are notconnected to a network domain.+R Open the Run dialog box.ALT+TAB(s) Switch between open windows. Whileholding the ALT key down, you canpress TAB several times to navigatethrough the system display of eachpreviously used window.ALT+SHIFT+TAB(s) Similar to ALT+TAB(s), switchbackward between open windows. Youcan switch between moving backwardor forward by holding or releasingSHIFT key.ALT+ESC(s) Cycle the input focus through thewindows in the order that they wereopened; compare to ALT+TAB.ALT+SHIFT+ESC(s) Similar to ALT+ESC(s), cycle focusbackward through windows. You canswitch between moving backward orforward by holding or releasing theSHIFT key.PRINTSCREEN Copy an image of the screen.ALT+PRINTSCREEN Copy an image of the currentwindow.Left ALT+SHIFT Switch input languages or keyboardlayouts (available and configurablewhen the user installed multiplekeyboard layouts through Regionaland Language Options in ControlPanel).CTRL+SHIFT Switch keyboard layouts or inputlanguages (available andconfigurable when the userinstalled multiple keyboard layoutsthrough Regional and LanguageOptions in Control Panel).CTRL or left ALT+SHIFT + ~, number (0~9), or grave accent key Hot key for input languages (available and configurable when the user installed multiple keyboard layouts through Regional and Language Options in Control Panel).+V [Speech recognition] Togglelistening status of the microphone. +C [Speech recognition] Correctrecognized text strings.+T [Speech recognition] Toggle betweenspeech dictation modes.+H [Handwriting] Open or closehandwriting pad.+ number Reserved for OEM use.F1 Display Application Help.SHIFT+F1 Display tips help (context-sensitive help) near the selectedcontrol.Display the shortcut menu for theselected item.SHIFT+F10 Display the shortcut menu for theselected item (same as ).CTRL+C Copy selected items.CTRL+X Cut selected items.CTRL+V Paste, cut or copied items.CTRL+Z Undo the last action.CTRL+Y Redo the last action.ESC Cancel the current task.DELETE Delete selected items.Third-party Utilities to Enhance AccessibilityMicrosoft provides detailed information on third-party accessibility aides and assistive technology products that run on Microsoft Windows operating systems at the following link:/library/default.asp?url=/library/en-us/vsintro7/html/vxmscthirdpartyutilitiestoenhanceaccessibility.aspEnhanced Accessibility with Screen ReaderSome of this application’s accessibility was evaluated using Freedom Scientific’s Screen Reader-JAWS for Windows© 5.0(JAWS). JAWS is thetext-to-speech solution for blind and/ or visually impaired individuals working in the computer industry. JAWS permits a blind user to access the same functionality as a sighted user by “listening and interacting” with the user interface rather than the standard primary method of “viewing and interacting” with the user interface. With few exceptions, this product is accessible for the blind and/ or visually impaired with the use of JAWS. Additional accessibility can be added withcustomization of JAWS via the use of scripting and/ or frames for this product. Additional information on JAWS is available in their documentation and on their web site at/Enhanced Accessibility with “Magnifier”“Magnifier” is a display utility that makes the screen more readable for us h oers w o have l w vision. To start MagnifierClick Start/Programs/Accessories/Accessibility/MagnifierMagnifier has some limitations with its tracking options as it canfollow the mouse pointer, the keyboard focus, and text editing. If youthe mouse cannot be used effectively in conjunction with the Magnifier, the “MouseKeys” option may be used. To activate MouseKeys:Click Start/Control Pane l. Select Accessibility Options in the Control Panel dialog, Select the Mouse Tab on the Accessibility options panel and check the “Use Mouse Key” check box inside the panel.。
imputeTestbench软件包说明说明书
Package‘imputeTestbench’October13,2022Type PackageTitle Test Bench for the Comparison of Imputation MethodsDate2019-07-05Maintainer Marcus W.Beck<*******************>Version3.0.3Description Provides a test bench for the comparison of missing data imputationmethods in uni-variate time series.Imputation methods are compared usingdifferent error metrics.Proposed imputation methods and alternative errormetrics can be used.Imports dplyr,forecast,ggplot2,imputeTS,reshape2,stats,tidyr,zooBugReports https:///neerajdhanraj/imputeTestbench/issuesLicense CC0LazyData TRUERoxygenNote6.1.1Suggests knitr,rmarkdown,magrittrNeedsCompilation noAuthor Neeraj Bokde[aut],Marcus W.Beck[cre,aut]Repository CRANDate/Publication2019-07-0518:10:14UTCR topics documented:impute_errors (2)mae (4)mape (5)plot_errors (5)plot_impute (6)print.errprof (8)rmse (8)sample_dat (9)1Index11impute_errors Function working as testbench for comparison of imputing modelsDescriptionFunction working as testbench for comparison of imputing modelsUsageimpute_errors(dataIn,smps="mcar",methods=c("na.approx","na.interp","na_interpolation","na.locf","na_mean"),methodPath=NULL,errorParameter="rmse",errorPath=NULL,blck=50,blckper=TRUE,missPercentFrom=10,missPercentTo=90,interval=10,repetition=10,addl_arg=NULL)ArgumentsdataIn input ts for testingsmps chr string indicating sampling type for generating missing data,see details methods chr string of imputation methods to use,one to many.A user-supplied function can be included if MethodPath is used,see details.methodPath chr string of location of script containing one or more functions for the proposed imputation method(s)errorParameter chr string indicating which error type to use,acceptable values are"rmse"(de-fault),"mae",or"mape".Alternatively,a user-supplied function can be passedif errorPath is used,see details.errorPath chr string of location of script containing one or more error functions for evalu-ating imputationsblck numeric indicating block sizes as a percentage of the sample size for the missing data,applies only if smps= marblckper logical indicating if the value passed to blck is a percentage of the sample size for missing data,otherwise blck indicates number of observations missPercentFromnumeric from which percent of missing values to be considered missPercentTo numeric for up to what percent missing values are to be consideredinterval numeric for interval between consecutive missPercent valuesrepetition numeric for repetitions to be done for each missPercent valueaddl_arg arguments passed to other imputation methods as a list of lists,see details.DetailsThe default methods for impute_errors are na.approx,na.interp,na_interpolation,na.locf, and na_mean.See the helpfile for each for additional documentation.Additional arguments for the imputation functions are passed as a list of lists to the addl_arg argument,where the list contains one to many elements that are named by the methods.The elements of the master list are lists with arguments for the relevant methods.See the examples.A user-supplied function can also be passed to methods as an additional imputation method.Acharacter string indicating the path of the function must also be supplied to methodPath.The path must point to a function where thefirst argument is the time series to impute.An alternative error function can also be passed to errorParameter if errorPath is not NULL.The function specified in errorPath must have two arguments where thefirst is a vector for the observed time series and the second is a vector for the predicted time series.The smps argument indicates the type of sampling for generating missing data.Options are smps = mcar for missing completely at random and smps= mar for missing at random.Additional information about the sampling method is described in sample_dat.The relevant arguments for smps= mar are blck and blckper which greatly affect the sampling method.Infinite comparisons are removed with a warning if errorParameter= mape .This occurs if any of the observed values in the original time series are zero.Error estimates for such datasets are evaluated only for non-zero observations.ValueReturns an error comparison for imputation methods as an errprof object.This object is structured as a list where thefirst two elements are named Parameter and MissingPercent that describe the error metric used to assess the imputation methods and the intervals of missing observations as percentages,respectively.The remaining elements are named as the chr strings in methods of the original function call.Each remaining element contains a numeric vector of the average error at each missing percent of observations.The errprof object also includes an attribute named errall as an additional list that contains all of the error estimates for every imputation method and repetition.See Alsosample_datExamples##Not run:#default optionsaa<-impute_errors(dataIn=nottem)aaplot_errors(aa)#change the simulation for missing obsaa<-impute_errors(dataIn=nottem,smps= mar )aaplot_errors(aa)#use one interpolation method,increase repetitions4mae aa<-impute_errors(dataIn=nottem,methods= na.interp ,repetition=100)aaplot_errors(aa)#change the error metricaa<-impute_errors(dataIn=nottem,errorParameter= mae )aaplot_errors(aa)#passing additional arguments to imputation methodsimpute_errors(dataIn=nottem,addl_arg=list(na_mean=list(option= mode )))##End(Not run)mae Mean Absolute Error CalculationDescriptiontakes difference between Original data and Predicted data as inputUsagemae(obs,pred)Argumentsobs numeric vector of original datapred numeric vector of predicted dataValuemaeVal as Mean Absolute ErrorExamples##Generate100random numbers within some limitsx<-sample(1:7,100,replace=TRUE)y<-sample(1:4,100,replace=TRUE)z<-mae(x,y)zmape5 mape Mean Absolute Percent Error CalculationDescriptiontakes difference between Original data and Predicted data as inputUsagemape(obs,pred)Argumentsobs numeric vector of original datapred numeric vector of predicted dataValuemapeVal as Mean Absolute ErrorExamples##Generate100random numbers within some limitsx<-sample(1:7,100,replace=TRUE)y<-sample(1:4,100,replace=TRUE)z<-mape(x,y)zplot_errors Function to plot the Error ComparisonDescriptionFunction to plot the Error ComparisonUsageplot_errors(dataIn,plotType=c("boxplot"))##S3method for class errprofplot_errors(dataIn,plotType=c("boxplot"))ArgumentsdataIn an errprof object returned from impute_errorsplotType chr string indicating plot type,accepted values are"boxplot","bar",or"line"ValueA ggplot object that can be further modified.The entire range of errors are shown if plotType="boxplot",otherwise the averages are shown if plotType="bar"or"line".Examplesaa<-impute_errors(dataIn=nottem)#default plotplot_errors(aa)##Not run:#bar plot of averages at each repetitionplot_errors(aa,plotType= bar )#line plot of averages at each repetitionplot_errors(aa,plotType= line )#change the plot aestheticslibrary(ggplot2)p<-plot_errors(aa)p+scale_fill_brewer(palette= Paired ,guide_legend(title= Default ))p+theme(legend.position= top )p+theme_minimal()p+ggtitle( Distribution of error for imputed values )p+scale_y_continuous( RMSE )##End(Not run)plot_impute Plot imputationsDescriptionPlot imputations for data from multiple methodsUsageplot_impute(dataIn,smps="mcar",methods=c("na.approx","na.interp","na_interpolation","na.locf","na_mean"),methodPath=NULL,blck=50,blckper=TRUE,missPercent=50,showmiss=FALSE,addl_arg=NULL)ArgumentsdataIn input ts for testingsmps chr string indicating sampling type for generating missing data,see detailsmethods chr string of imputation methods to use,one to many.A user-supplied function can be included if MethodPath is used.methodPath chr string of location of script containing one or more functions for the proposed imputation method(s)blck numeric indicating block sizes as a percentage of the sample size for the missing data,applies only if smps= marblckper logical indicating if the value passed to blck is a percentage of the sample size for missing data,otherwise blck indicates number of observations missPercent numeric for percent of missing values to be consideredshowmiss logical if removed values missing from the complete dataset are plottedaddl_arg arguments passed to other imputation methods as a list of lists,see details. DetailsSee the documentation for impute_errors for an explanation of the arguments.ValueA ggplot object showing the imputed data for each method.Red points are labelled as’imputed’and blue points are labelled as’retained’from the original data set.Missing data that were removed can be added to the plot as open circles if showmiss=TRUE.See the examples for modifying the plot.Examples#defaultplot_impute(dataIn=nottem)#change missing percent totalplot_impute(dataIn=nottem,missPercent=10)#show missing valuesplot_impute(dataIn=nottem,showmiss=TRUE)#use mar samplingplot_impute(dataIn=nottem,smps= mar )#change the plot aesthetics##Not run:library(ggplot2)p<-plot_impute(dataIn=nottem,smps= mar )p+scale_colour_manual(values=c( black , grey ))p+theme_minimal()p+ggtitle( Imputation examples with different methods )p+scale_y_continuous( Temp at Nottingham Castle(F) )##End(Not run)8rmse print.errprof Print method for errprofDescriptionPrint method for errprof classUsage##S3method for class errprofprint(x,...)Argumentsx input errprof object...arguments passed to or from other methodsValuelist output for the errprof objectrmse Root Mean Square Error CalculationDescriptiontakes difference between Original data and Predicted data as inputUsagermse(obs,pred)Argumentsobs numeric vector of original datapred numeric vector of predicted dataValuermseVal as Root Mean Square ErrorExamples##Generate100random numbers within some limitsx<-sample(1:7,100,replace=TRUE)y<-sample(1:4,100,replace=TRUE)z<-rmse(x,y)zsample_dat Sample time series dataDescriptionSample time series using completely at random(MCAR)or at random(MAR)Usagesample_dat(datin,smps="mcar",repetition=10,b=10,blck=50,blckper=TRUE,plot=FALSE)Argumentsdatin input numeric vectorsmps chr string of sampling type to use,options are"mcar"or"mar"repetition numeric for repetitions to be done for each missPercent valueb numeric indicating the total amount of missing data as a percentage to removefrom the complete time seriesblck numeric indicating block sizes as a proportion of the sample size for the missing datablckper logical indicating if the value passed to blck is a proportion of missper,i.e., blocks are to be sized as a percentage of the total size of the missing data plot logical indicating if a plot is returned showing the sampled data,plots only the first repetitionValueInput data with NA values for the sampled observations if plot=FALSE,otherwise a plot showing the missing observations over the complete dataset.The missing data if smps= mar are based on random sampling by blocks.The start location of each block is random and overlapping blocks are not counted uniquely for the required sample size given by b.Final blocks are truncated to ensure the correct value of b is returned.Blocks arefixed at 1if the proportion is too small,in which case"mcar"should be used.Block sizes are also truncated to the required sample size if the input value is too large if blckper=FALSE.For the latter case, this is the same as setting blck=1and blckper=TRUE.For all cases,thefirst and last observation will never be removed to allow comparability of interpo-lation schemes.This is especially relevant for cases when b is large and smps= mar is used.For example,method=na.approx will have rmse=0for a dataset where the removed block includes the last n observations.This result could provide misleading information in comparing methods.Examplesa<-rnorm(1000)#default samplingsample_dat(a)#use mar samplingsample_dat(a,smps= mar )#show a plot of one repetitionsample_dat(a,plot=TRUE)#show a plot of one repetition,mar samplingsample_dat(a,smps= mar ,plot=TRUE)#change plot aestheticslibrary(ggplot2)p<-sample_dat(a,plot=TRUE)p+scale_colour_manual(values=c( black , grey )) p+theme_minimal()p+ggtitle( Example of simulating missing data )Indexggplot,7impute_errors,2,5,7mae,4mape,5na.approx,3na.interp,3na.locf,3na_interpolation,3na_mean,3plot_errors,5plot_impute,6print.errprof,8rmse,8sample_dat,3,9ts,2,611。
USB2.0一致性测试 Step by Step
Step by Step
美国力科公司
第1页
USB2.0简介
四线系统（D+,D-,VBUS,GND) USB2.0提供下列数据率选择
低速 全速 高速
数据速率 上升时间 1.5Mbps 75- 300ns 12Mbps 4-20ns 480Mbps 500ps
High speed:480.00 Mb/s±500 ppm Full speed:12.000 Mb/s ±0.25% (2,500 ppm). Low speed:1.50 Mb/s ±1.5% (15,000 ppm).
第2页
USB-IF论坛负责制定USB2.0规范
第3页
USB2.0 应用产品分类
第4页
USB2.0 一致性测试规范
完整的 USB 2.0 一致性测试涵盖 了三种类型的设备
Devices Hubs Hosts
Speed HS
Test items FE SQ NE SQ Upstream SQ Downstream SQ
High Speed Device SQ实测连接
第27页
第12步：在USB Host上运行HS Electrical Test Tool
第28页
遵照Message 对话框中的指 令进行每一步 操作，在USB Host 的 Windows操作 系统上运行
HS Electrical Test Tool程序， 点击”OK”进 入下一步
Remote属性页面中的IP地址 就是 操作系统分配的 IP地址
如果示波器或QualiPHY软件显示的界面和以上不一致，请 登陆力科网站或联系就近力科办事处 获取最新版本的示波 器固件和QualiPHY软件（均免费）。
vulmap-用法
vulmap 用法
Vulmap是一个漏洞扫描工具，用于发现和评估系统中的漏洞。
下面是Vulmap的用法：
1. 安装Vulmap。
可以从GitHub上下载Vulmap的源代码，并按照官方文档中的说明进行安装和配置。
2. 启动Vulmap。
在终端中，进入Vulmap的安装目录，并执行以下命令启动Vulmap：
```
python3 vulmap.py
```
3. 设置目标。
在Vulmap的主界面中，使用命令`set target [目标URL]`来设置扫描目标。
例如：
```
set target http://.example
```
4. 开始扫描。
使用命令`run`开始进行漏洞扫描。
Vulmap将自动检测目标系统中的漏洞，并显示扫描结果。
5. 查看漏洞详情。
使用命令`show`可以查看漏洞列表。
使用命令`show [漏洞ID]`可以查看具体漏洞的详细信息。
6. 设置其他选项。
可以使用命令`set [选项名称] [选项值]`来设置Vulmap的其他选项，例如设置扫描的线程数量、启用代理等。
7. 导出扫描结果。
使用命令`export [文件路径]`可以将扫描结果导出为一个文本文件，方便后续分析和报告。
请注意，在使用Vulmap进行漏洞扫描时，务必获得授权，并且遵守法律和道德准则。
漏洞扫描工具的使用可能对目标系统造成意外影响，因此应谨慎操作，并遵循相关规定和指导。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
60 30 30 30 30 30 60 30 30 30 30 30 30 60 30 60 60 weekly 30 60 ongoing
zhangshuf Guan Jie Johnny Gao Que Xinyu Johnny Gao Wang Zhongfei Liu Wei27/Zhu wenbin Wang Zhongfei Wang Zhongfei Liu Chuangui Liu Chuangui Guan Jie Guan Jie Guan Jie yangyunbei caoxiaomei zhangshufen chenww1 chenww1 chenww1
2014/10/22 2014/9/9 2014/9/12 2014/9/16 2014/9/16 2014/9/30 2014/10/22 2014/9/5 2014/9/9 2014/9/5 2014/9/30 2014/9/12 2014/9/17 2014/10/22 2014/9/30 2014/10/30 2014/10/30 H Breakthrough(TEST) Team Leader：关杰、张淑芬 Project Member：郭长勇、张建新、刘全二、刘伟27、朱文斌、温高星、史修文、樊少军、高峰、朱银柳、蓝碧峰、林庆星、杨允北、杨存、李向飞 Sumary Duration Week1 Week2 Phase Status Owner No. Task Name Start Deadline 项目团队组建 1 1.1 确定团队成员 2014/8/26 2014/8/26 30 guanjie 1.2 职责分工 2014/9/5 2014/9/5 30 zhangshufen 1.3 项目计划 2014/9/9 2014/9/9 30 zhangshufen 1.4 2 2.1 2,2 2,3 2,4 3 3.1 3.2 3.3 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 项目启动 现状确认及测量分析 确认设备现状 确定主要改善项目 确定工位瓶颈 建立UPH,UPPH测量系统 工艺文件改善 确定工艺调整的范围 工位工艺文件调整（2个工位） 新工艺文件导入实施 2014/8/14 2014/9/5 2014/9/6 2014/9/9 2014/9/1 2014/9/22 2014/10/8 2014/8/14 2014/9/5 2014/9/6 2014/9/9 2014/9/5 2014/9/24 2014/10/8 2014/8/26 2014/8/20 2014/9/30 2014/9/30 2014/10/30 2014/9/30 2014/9/30 2014/9/30 2014/9/11 2014/9/11 2014/9/16 2014/9/18 2014/9/28 2014/10/7 2014/10/10 2014/10/13 2014/10/17 30 30 30 30 30 30 60 30 30 30 closed closed ongoing 30 30 30 30 30 30 30 30 60 60 60 60 closed zhangshufen guanjie linkaixiong linkaixiong Liu Wei27 Wang Zhongfei Wang Zhongfei zhangshufen guanjie zhuyinliu lanbifeng lanbifeng liuchuangui gaofeng gaofeng guanjie、huangyuezong zhangsfhufen linqinxin zhuyinliu quexinyi zhuyinliu lanbifen、qiugenhui linkaixiong、liuwei27 liuwei27 2014/9/11 2014/9/11 30 chenwenwen
closed ongoing delay
Week3 Week4
头脑风暴问题改善 2014/8/26 来料开单手写改成机打 2014/8/20 抽测手动抄值取消，程序自动监控 2014/9/11 •点胶炉前炉后托盘运输导入双向滚轮 2014/9/8 •炉后自动冷却，减少搬运 2014/9/16 •点胶导入皮带线，减少点胶人员取放主板、托盘动作 2014/9/8 产出扫描取消排程选择动作 2014/9/17 夹具自动扣合，取消插射频线动作 2014/9/17 写SN号工位扫描自动感应，取消手动操作 layout改善 2014/9/9 调整方案输出 2014/9/11 工作桌AMC需求表 2014/9/11 可行性评估（改造费用预算输出） 2014/9/17 制作工装需求清单 2014/9/18 物品采购 2014/9/29 工装桌改造 2014/10/8 现场电气改造施工 2014/10/10 线体布局调整 2014/10/14 设备调试完成
ongoing ongoing
Plan Actual
Week5 Week6 Week7 Week8 Week9 Week10 Week11 Week12
5.10 6 6.1 6.2 6.3 6.3 6.4 6.5 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 8 8.1 8.2 8.3 9 9.1 9.2 9.3
2014/10/18 新线导入使用 导入自动化工装设备 2014/9/5 确认自动化工装通信规范 2014/9/9 确认自动化工装硬件设计方案及图纸 2014/9/15 完成自动化工装采购 2014/9/15 完成自动工装样品交付 2014/9/17 完成自动工装样品验证 2014/10/8 完成S850一条线体自动工装安装及调试 程序优化改善 2014/9/1 完成高通WIFI抽测OA签批 2014/9/8 完成高通机型SOP修改及工艺定额更新 2014/9/1 制造内部达成一致，产出扫描取消选择排程 2014/9/8 MES完成产出扫描取消选择排程动作 2014/9/8 开发SN自动扫描程序，取消手动滑动扫描 2014/9/13 开发测试工装自动控制程序并实现自动点击开始按钮 2014/9/18 优化自动化程序 排产优化 优化排产规则 2014/9/11 排产实施 效果确认 项目总结分享 项目周报 SMT&TEST 总结30天 SMT&TEST总结60天 － 2014/10/10 2014/11/10 2014/10/7 2014/10/8