Predicting Software Defect Density A Case Study on Automated Static Code Analysis

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基于代价敏感神经网络算法的软件缺陷预测缪林松【摘要】Software defect prediction has been studied as an important research topic for 30 years in software en- gineering. Recently, with the development of machine learning techniques, traditional machine learning has been applied in software defect prediction based on static code attributes successfully. However, the traditional machine learning does not consider the cost-sensitive problem and class-imbalance problem in software defect prediction appli- cations. We study the application of cost-sensitive neural networks based on over-sampling and threshold-moving to software defect prediction. The experimental results on NASA software defect prediction benchmarking dataset dem- onstrate the algorithm＇s efficacy.%软件缺陷预测作为软件工程领域的重要研究内容已有近30年。

近年来，随着机器学习技术的发展，传统机器学习技术基于静态代码属性的软件缺陷预测领域得到广泛应用。

Package‘eSDM’October24,2023Title Ensemble Tool for Predictions from Species Distribution Models Description A tool which allows users to create and evaluate ensembles of species distribution model(SDM)predictions.Functionality is offered through R functions or a GUI(R Shiny app).This tool can assist users in identifying spatial uncertainties andmaking informed conservation and management decisions.The package isfurther described in Woodman et al(2019)<doi:10.1111/2041-210X.13283>. Version0.4.0URL https://smwoodman.github.io/eSDM/,https:///smwoodman/eSDM/BugReports https:///smwoodman/eSDM/issues/Depends R(>=4.0.0)Imports dplyr(>=0.7-0),magrittr,methods,purrr,rlang,ROCR,sf(>=0.9-0),shiny,stats,unitsSuggests colorRamps,colourpicker,dichromat,DT,knitr,leafem,leaflet,maps,raster,RColorBrewer,rmarkdown,shinybusy,shinydashboard,shinyjs,testthat(>=2.1.0),tmap(>=2.3),viridis,zipLicense GPL-3Encoding UTF-8LazyData trueRoxygenNote7.2.3VignetteBuilder knitrNeedsCompilation noAuthor Sam Woodman[aut,cre](<https:///0000-0001-6071-8186>) Maintainer Sam Woodman<********************>Repository CRANDate/Publication2023-10-2417:00:02UTC12eSDM-package R topics documented:eSDM-package (2)ensemble_create (3)ensemble_rescale (4)eSDM_GUI (5)evaluation_metrics (6)gshhg.l.L16 (7)model_abundance (7)overlay_sdm (8)preds (9)pts2poly_centroids (10)pts2poly_vertices (11)validation.data (12)Index14 eSDM-package Ensemble tool for predictions from Species Distribution ModelsDescriptioneSDM:A tool for creating and exploring ensembles of predictions from Species Distribution Mod-elsDetailseSDM provides functionality for overlaying SDM predictions onto a single base geometry and creating and evaluating ensemble predictions.This can be done manually in R,or using the eSDM GUI(an R Shiny app)opened through eSDM_GUIeSDM allows users to overlay SDM predictions onto a single base geometry,create ensembles of these predictions via weighted or unweighted averages,calculate performance metrics for each set of predictions and for resulting ensembles,and visually compare ensemble predictions with original predictions.The information provided by this tool can assist users in understanding spatial uncertainties and making informed conservation decisions.The GUI ensures that the tool is accessible to non-R users,while also providing a user-friendly environment for functionality such as loading other polygons to use and visualizing predictions.However,user choices are restricted to the workflow provided by the GUI.Author(s)Sam Woodman<********************>See Alsohttps://smwoodman.github.io/eSDM/ensemble_create3 ensemble_create Create ensemble of SDM predictionsDescriptionCreate a weighted or unweighted ensemble of SDM predictions,including associated uncertainty valuesUsageensemble_create(x,x.idx,w=NULL,x.var.idx=NULL,...)##S3method for class sfensemble_create(x,x.idx,w=NULL,x.var.idx=NULL,...)##S3method for class data.frameensemble_create(x,x.idx,w=NULL,x.var.idx=NULL,...)Argumentsx object of class sf or class data.framex.idx vector of column names or numerical indices;indicates which columns in x will be used to create the ensemblew weights for the ensemble;either a numeric vector the same length as x or a data frame(or tibble)with the same number of rows as x and ncol(w)==length(x.idx).If w is a numeric vector,its values(i.e.the weights)mustsum to1.The default value is1/length(x.idx),i.e.an unweighted ensemble x.var.idx vector of column names or column indices;indicates columns in x with vari-ance values with which to calculate uncertainty values for the ensemble.Ifx.var.idx is specified,it must be the same length as e x.var.idx=NULL(the default)if none of the predictions have associated uncertainty values;in this case the uncertainty values for the ensemble will be calculated using theamong-model uncertainty.See the’Details’section for more information ...Arguments to be passed to methods;specifically designed for passing na.rm argument to sumDetailsensemble_create is designed to be used after overlaying predictions with overlay_sdm and(if desired)rescaling the overlaid predictions with ensemble_rescale.This function implements ensemble methods provided in eSDM_GUI.Note that it does not imple-ment regional exclusion,which must be done manually if not using the GUI.Ensemble uncertainty is calculated using either the within-model uncertainty(if x.var.idx is spec-ified)or the among-model uncertainty(if x.var.idx is NULL).See the eSDM GUI manual for ap-plicable formulas.4ensemble_rescaleValueAn object of the same class as x with two columns appended to the data frame:•’Pred_ens’-The ensemble predictions•’Var_ens’-The variance of the ensemble predictions,calculated using either the within-model uncertainty(if x.var.idx is specified)or the among-model uncertainty(if x.var.idx is NULL)Note that all other columns of x will be included in the returned object.Also,if x is of class sf then1)the geometry list-column will be the last column of the returned object and2)the agr attributewill be set as’constant’for’Pred_ens’and’Var_ens’Examplesensemble_create(preds.1,c("Density","Density2"),c(0.2,0.8))ensemble_create(preds.1,1:2,c(0.2,0.8),c("Var1","Var2"))ensemble_create(data.frame(a=1:5,b=3:7),c(1,2))weights.df<-data.frame(runif(325),c(rep(NA,100),runif(225)))ensemble_create(preds.1,c("Density","Density2"),weights.df,na.rm=TRUE) ensemble_rescale Rescale SDM predictionsDescriptionRescale SDM predictions and(if applicable)associated uncertaintiesUsageensemble_rescale(x,x.idx,y,y.abund=NULL,x.var.idx=NULL)Argumentsx object of class sfx.idx vector of column names or column indices;indicates columns in x with predic-tion values that will be rescaledy rescaling method;must be either"abundance"or"sumto1".See’Details’section for descriptions of the rescaling methodsy.abund numeric value;ignored if y is not"abundance"x.var.idx vector of column names or column indices;indicates columns in x with variance values that will be rescaled.If x.var.idx is specified,it must be the same lengthas e x.var.idx=NULL(the default)if none of the predictions haveassociated uncertainty values;see the’Details’section for more informationeSDM_GUI5Detailsensemble_rescale is intended to be used after overlaying predictions with overlay_sdm and be-fore creating ensembles with ensemble_create.The provided rescaling methods are:•’abundance’-Rescale the density values so that the predicted abundance is y.abund•’sumto1’-Rescale the density values so their sum is1SDM uncertainty values must be rescaled differently than the prediction values.Columns specified in x.var.idx must contain variance values.These values will be rescaled using the formula var(c *x)=c^2*var(x),where c is the rescaling factor for the associated predictions.If x.var.idx is not NULL,then the function assumes x.var.idx[1]contains the variance values associated with the predictions in x.idx[1],x.var.idx[2]contains the variance values associated with the predictions in x.idx[2],e NA in x.var.idx to indicate a set of predictions that does not have associated uncertainty values(e.g.,x.var.idx=c(4,NA,5))ValueThe sf object x with the columns specified by x.idx and x.var.idx rescaled.The agr attributes of x will be conservedExamplesensemble_rescale(preds.1,c("Density","Density2"),"abundance",50)ensemble_rescale(preds.1,c(1,2),"sumto1")ensemble_rescale(preds.1,c("Density","Density2"),"abundance",100,c(3,4))eSDM_GUI Open the eSDM GUIDescriptionOpen the eSDM graphical user interface(GUI);an R Shiny app for creating ensemble predictions using SDM predictions.UsageeSDM_GUI(launch.browser=TRUE)Argumentslaunch.browser Logical with default of TRUE;passed to launch.browser argument of runApp6evaluation_metricsSee Alsohttps://smwoodman.github.io/eSDM/evaluation_metrics Calculate SDM evaluation metricsDescriptionCalculate AUC,TSS,and RMSE for given density predictions and validation dataUsageevaluation_metrics(x,x.idx,y,y.idx,count.flag=FALSE)Argumentsx object of class sf;SDM predictionsx.idx name or index of column in x with prediction valuesy object of class sf;validation datay.idx name or index of column in y with validation data.This validation data column must have at least two unique values,e.g.0and1count.flag logical;TRUE indicates that the data in column y.idx is count data,while FALSE indicates that the data is presence/absence.See details for differences in dataprocessing based on thisflag.DetailsIf count.flag==TRUE,then eSDM::model_abundance(x,x.idx,FALSE)will be run to calculate predicted abundance and thus calculate RMSE.Note that this assumes the data in column x.idx of x are density values.If count.flag==FALSE,then all of the values in column y.idx of y must be0or1.All rows of x with a value of NA in column x.idx and all rows of y with a value of NA in column y.idx are removed before calculating metricsValueA numeric vector with AUC,TSS and RMSE values,respectively.If count.flag==FALSE,theRMSE value will be NAExamplesevaluation_metrics(preds.1,2,validation.data,"sight")evaluation_metrics(preds.1,"Density2",validation.data,"count",TRUE)gshhg.l.L167 gshhg.l.L16Low resolution GSHHG world mapDescriptionLow resolution GSHHG world map,includes hierarchical levels L1and L6.Processed using st_make_validUsagegshhg.l.L16FormatAn object of class sfcSource/pwessel/gshhg/model_abundance Calculate predicted abundanceDescriptionCalculates the predicted abundance by multiplying the density prediction values by prediction poly-gon areasUsagemodel_abundance(x,dens.idx,sum.abund=TRUE)Argumentsx object of class sf;SDM with density predictions.Must have a valid crs code dens.idx name or index of column(s)in x with density predictions.Can be a character vector(column names)or numeric vector(column indices) sum.abund logical;whether or not to sum all of the predicted abundancesDetailsMultiplies the values in the specified column(s)(i.e.the density predictions)by the area in square kilometers of their corresponding prediction polygon.The area of each prediction polygon is calcu-lated using st_area from geos_measures.x must have a valid crs code to calculate area for these abundance calculations.8overlay_sdmValueIf sum.abund==TRUE,then a vector of the same length as dens.idx representing the predicted abundance for the density values in each column.If sum.abund==FALSE and the length of dens.idx is1,then a numeric vector with the predicted abundance of each prediction polygon of x.If sum.abund==FALSE and the length of dens.idx is greater than1,then a data frame with length(dens.idx)columns of the predicted abundance of prediction polygonsExamplesmodel_abundance(preds.1,"Density")model_abundance(preds.1,c(1,1))model_abundance(preds.1,c(1,1),FALSE)overlay_sdm Overlay SDM predictions onto base geometryDescriptionOverlay specified SDM predictions that meet the percent overlap threshold requirement onto base geometryUsageoverlay_sdm(base.geom,sdm,sdm.idx,overlap.perc)Argumentsbase.geom object of class sfc;base geometrysdm object of class sf;original SDM predictionssdm.idx names or indices of column(s)with data to be overlaidoverlap.perc numeric;percent overlap threshold,i.e.percentage of each base geometry poly-gon must overlap with SDM prediction polygons for overlaid density value tobe calculated and not set as NADetailsSee the eSDM GUI manual for specifics about the overlay process.This process is equivalent to areal interpolation(Goodchild and Lam1980),where base.geom is the target,sdm is the source, and the data specified by sdm.idx are spatially intensive.Note that overlay_sdm removes rows in sdm that have NA values in thefirst column specified in sdm.idx(i.e.sdm.idx[1]),before the overlay.Thus,for valid overlay results,all columns of sdm specified in sdm.idx must either have NA values in the same rows or contain only NAs.preds9ValueObject of class sf with the geometry of base.geom and the data in the sdm.idx columns of sdm overlaid onto that geometry.Note that this means all columns of sdm not in sdm.idx will not be in the returned object.Because the data are considered spatially intensive,the agr attribute will be set as’constant’for all columns in the returned objectReferencesGoodchild,M.F.&Lam,N.S.-N.(1980)Areal interpolation:a variant of the traditional spatial problem.Geo-Processing,1,297-312.Examplespol1.geom<-sf::st_sfc(sf::st_polygon(list(rbind(c(1,1),c(3,1),c(3,3),c(1,3),c(1,1)))),crs=4326)pol2.geom<-sf::st_sfc(sf::st_polygon(list(rbind(c(0,0),c(2,0),c(2,2),c(0,2),c(0,0)))),crs=4326)pol2.sf<-sf::st_sf(data.frame(Dens=0.5),geometry=pol2.geom,crs=4326)overlay_sdm(pol1.geom,pol2.sf,1,25)#Output Dens value is NA because of higher overlap.perc valueoverlay_sdm(pol1.geom,pol2.sf,1,50)#These examples take longer to runoverlay_sdm(sf::st_geometry(preds.1),preds.2,1,50)overlay_sdm(sf::st_geometry(preds.2),preds.1,"Density",50)preds Sample SDM density predictionsDescriptionpreds.1,preds.2,and preds.3are objects of class sf that serve as sample sets of SDM density predictions for the eSDM packageUsagepreds.1preds.2preds.310pts2poly_centroids FormatObjects of class sf with a column of density predictions(name:Density)and a simple feature list column(name:geometry).preds.1also has a second column of sample density predictions (name:Density2),as well as Var1and Var2,representing the variancepreds1:An object of class sf(inherits from data.frame)with325rows and5columns.preds2:An object of class sf(inherits from data.frame)with1891rows and2columns.preds3:An object of class sf(inherits from data.frame)with1445rows and2columns.An object of class sf(inherits from data.frame)with1891rows and2columns.An object of class sf(inherits from data.frame)with1445rows and2columns.Detailspreds.1sample SDM density predictions created by importing Sample_predictions_2.csv into the eSDM GUI,exporting predictions,and then clipping them to the SoCal_bite.csv region.Also man-ually added two variance columns(numbers are randomly generated with a max of0.01)preds.2sample SDM density predictions created by importing Sample_predictions_1.csv into the eSDM GUI,exporting predictions,and then clipping them to the SoCal_bite.csv regionpreds.3is a set of sample SDM density predictions created by importing Sample_predictions_4_gdb into the eSDM GUI,exporting predictions,and then clipping them to the SoCal_bite.csv regionpts2poly_centroids Create polygons from centroid coordinatesDescriptionCreate polygon(s)from a data frame with coordinates of the polygon centroid(s)Usagepts2poly_centroids(x,y,...)Argumentsx data frame with at least two columns;thefirst two columns must contain longi-tude and latitude coordinates,respectively.See’Details’section for how addi-tional columns are handledy numeric;the perpendicular distance from the polygon centroid(center)to its edge(i.e.half the length of one side of a polygon)...passed to st_sf or to st_sfc,e.g.for passing named arguments crs and agrpts2poly_vertices11 DetailsThis function was designed for someone who reads in a.csvfile with a grid of coordinates repre-senting SDM prediction points and needs to create prediction polygons with the.csvfile coordinates as the polygon centroids.However,the function can be used to create square polygons of any size around the provided points,regardless of if those polygons touch or overlap.The created polygons are oriented so that,in a2D plane,their edges are parallel to either the x or the y axis.If x contains more than two column,then additional columns will be treated as simple feature attributes,i.e.passed along as thefirst argument to st_sfIf a crs is not specified in...,then the crs attribute of the polygon(s)will be NULL.ValueObject of class sfc(if x has exactly two columns)or class sf(if x has exactly more than two columns).The object will have a geometry type of POLYGON.If the object is of class sf,the name of the geometry list-column will be"geometry"Examples#Create an sfc object from a data frame of two columnsx<-data.frame(lon=c(5,10,15,20,5,10,15,20),lat=c(5,5,5,5,10,10,10,10))pts2poly_centroids(x,2.5,crs=4326)#Create an sf object from a data frame of more than two columnsx<-data.frame(lon=c(5,10,15,20,5,10,15,20),lat=c(5,5,5,5,10,10,10,10),sdm.pred=runif(8),sdm.pred2=runif(8))pts2poly_centroids(x,2.5,crs=4326,agr="constant")pts2poly_vertices Create polygons from vertex coordinatesDescriptionCreate polygon(s)from a data frame with the coordinates of the polygon verticesUsagepts2poly_vertices(x,...)Argumentsx data frame with at least two columns;thefirst two columns must contain longi-tude and latitude coordinates,respectively.See’Details’section for how addi-tional columns are handled...passed to st_sfc,e.g.for passing named argument crsDetailsVertices of different polygons must be demarcated by rows with values of NA in both thefirst and second columns(i.e.the longitude and latitude columns).All columns in x besides thefirst two columns are ignored.If a crs is not specified in...,then the crs attribute of the polygon(s)will be NULL.ValueObject of class sfc with the geometry type POLYGONExamplesx<-data.frame(lon=c(40,40,50,50,40),lat=c(0,10,10,0,0))pts2poly_vertices(x,crs=4326)#Create an sf objectx<-data.frame(lon=c(40,40,50,50,40,NA,20,20,30,30,20),lat=c(0,10,10,0,0,NA,0,10,10,0,0))sf::st_sf(Pred=1:2,geometry=pts2poly_vertices(x,crs=4326))validation.data Sample validation dataDescriptionSample validation data created by cropping Validation_data.csv to the SoCal_bite.csv region(.csv files from...)Usagevalidation.dataFormatAn object of class sf with8rows and3variablessight1’s and0’s indicating species presence/absencecount number of individuals observed at each pointgeometry simple feature list column representing validation data pointsIndex∗datasetsgshhg.l.L16,7preds,9validation.data,12∗packageeSDM-package,2ensemble_create,3,5ensemble_rescale,3,4eSDM(eSDM-package),2eSDM-package,2eSDM_GUI,2,3,5evaluation_metrics,6geos_measures,7gshhg.l.L16,7model_abundance,7overlay_sdm,3,5,8preds,9pts2poly_centroids,10pts2poly_vertices,11runApp,5sf,9,13sfc,7st_make_valid,7st_sf,10,11st_sfc,10,12validation.data,1214。

软件测试常用英语词汇静态测试：Non-Execution-Based Testing或Static testing代码走查：Walkthrough代码审查：Code Inspection技术评审：Review动态测试：Execution-Based Testing白盒测试：White-Box Testing黑盒测试：Black-Box Testing灰盒测试：Gray-Box Testing软件质量保证SQA：Software Quality Assurance软件开发生命周期：Software Development Life Cycle冒烟测试：Smoke Test回归测试：Regression Test功能测试：Function Testing性能测试：Performance Testing压力测试：Stress Testing负载测试：Volume Testing易用性测试：Usability Testing安装测试：Installation Testing界面测试：UI Testing配置测试：Configuration Testing文档测试：Documentation Testing兼容性测试：Compatibility Testing安全性测试：Security Testing恢复测试：Recovery Testing单元测试：Unit Test集成测试：Integration Test系统测试：System Test验收测试：Acceptance Test测试计划应包括：测试对象：The Test Objectives测试范围： The Test Scope测试策略： The Test Strategy测试方法： The Test Approach;测试过程： The test procedures;测试环境： The Test Environment;测试完成标准：The test Completion criteria 测试用例：The Test Cases测试进度表：The Test Schedules风险：Risks接口：Interface最终用户：The End User正式的测试环境：Formal Test Environment确认需求：Verifying The Requirements有分歧的需求：Ambiguous Requirements运行和维护：Operation and Maintenance.可复用性：Reusability可靠性： Reliability/Availability电机电子工程师协会IEEE：The Institute of Electrical and Electronics Engineers正确性：Correctness实用性：Utility健壮性：Robustness可靠性：Reliability软件需求规格说明书：SRS software requirement specification概要设计：HLD high level design详细设计：LLD low level design统一开发流程：RUP rational unified process集成产品开发：IPD integrated product development能力成熟模型：CMM capability maturity model能力成熟模型集成：CMMI capability maturity model integration戴明环：PDCA plan do check act软件工程过程组：SEPG software engineering process group集成测试：IT integration testing系统测试：ST system testing关键过程域：KPA key process area同行评审：PR peer review用户验收测试：UAT user acceptance testing验证和确认：V&V verification & validation控制变更委员会：CCB change control board图形用户界面：GUI graphic user interface配置管理员：CMO configuration management officer 平均失效间隔时间：MTBF mean time between failures 平均修复时间：MTTR mean time to restoration平均失效时间：MTTF mean time to failure工作任务书：SOW statement of workα测试：alpha testingβ测试：beta testing适应性：Adaptability可用性：Availability功能规格说明书：Functional Specification软件开发中常见英文缩写和各类软件开发文档的英文缩写：英文简写文档名称MRD market requirement document 市场需求文档PRD product requirement document 产品需求文档SOW 工作任务说明书PHB Process Handbook 项目过程手册EST Estimation Sheet 估计记录PPL Project Plan 项目计划CMP Software Management Plan 配置管理计划QAP Software Quality Assurance Plan 软件质量保证计划RMP Software Risk Management Plan 软件风险管理计划TST Test Strategy测试策略WBS Work Breakdown Structure 工作分解结构BRS Business Requirement Specification业务需求说明书SRS Software Requirement Specification软件需求说明书STP System Testing plan 系统测试计划STC System Testing Cases 系统测试用例HLD High Level Design 概要设计说明书ITP Integration Testing plan 集成测试计划ITC Integration Testing Cases 集成测试用例LLD Low Level Design 详细设计说明书UTP Unit Testing Plan 单元测试计划UTC Unit Testing Cases 单元测试用例UTR Unit Testing Report 单元测试报告ITR Integration Testing Report 集成测试报告STR System Testing Report 系统测试报告RTM Requirements Traceability Matrix 需求跟踪矩阵CSA Configuration Status Accounting 配置状态发布CRF Change Request Form 变更申请表WSR Weekly Status Report 项目周报QSR Quality Weekly Status Report 质量工作周报QAR Quality Audit Report质量检查报告QCL Quality Check List质量检查表PAR Phase Assessment Report 阶段评估报告CLR Closure Report 项目总结报告RFF Review Finding Form 评审发现表MOM Minutes of Meeting 会议纪要MTX Metrics Sheet 度量表CCF ConsistanceCheckForm一致性检查表BAF Baseline Audit Form基线审计表PTF Program Trace Form问题跟踪表领测国际科技北京有限公司软件测试中英文对照术语表AAbstract test case High level test case ：概要测试用例 Acceptance：验收Acceptance criteria：验收标准Acceptance testing：验收测试Accessibility testing：易用性测试Accuracy：精确性Actual outcome actual result ：实际输出/实际结果 Ad hoc review informal review ：非正式评审Ad hoc testing：随机测试Adaptability：自适应性Agile testing：敏捷测试Algorithm test branch testing ：分支测试Alpha testing：alpha 测试Analyzability：易分析性Analyzer：分析员Anomaly：异常Arc testing：分支测试Attractiveness：吸引力Audit：审计Audit trail：审计跟踪Automated testware：自动测试组件Availability：可用性BBack-to-back testing：对比测试Baseline：基线Basic block：基本块Basis test set：基本测试集Bebugging：错误撒播Behavior：行为Benchmark test：基准测试Bespoke software：定制的软件Best practice：最佳实践Beta testing：Beta 测试领测国际科技北京有限公司Big-bang testing：集成测试Black-box technique：黑盒技术Black-box testing：黑盒测试Black-box test design technique：黑盒测试设计技术Blocked test case：被阻塞的测试用例Bottom-up testing：自底向上测试Boundary value：边界值Boundary value analysis：边界值分析Boundary value coverage：边界值覆盖率Boundary value testing：边界值测试Branch：分支Branch condition：分支条件Branch condition combination coverage：分支条件组合覆盖率 Branch condition combination testing：分支条件组合测试Branch condition coverage：分支条件覆盖率Branch coverage：分支覆盖率Branch testing：分支测试Bug：缺陷Business process-based testing：基于商业流程的测试CCapability Maturity Model CMM ：能力成熟度模型Capability Maturity Model Integration CMMI ：集成能力成熟度模型Capture/playback tool：捕获/回放工具Capture/replay tool：捕获/重放工具CASE Computer Aided Software Engineering ：电脑辅助软件工程 CAST Computer Aided Software Testing ：电脑辅助软件测试Cause-effect graph：因果图Cause-effect graphing：因果图技术Cause-effect analysis：因果分析Cause-effect decision table：因果判定表Certification：认证Changeability：可变性Change control：变更控制Change control board：变更控制委员会Checker：检查人员Chow's coverage metrics N-switch coverage ：N 切换覆盖率 Classification tree method：分类树方法Code analyzer：代码分析器Code coverage：代码覆盖率领测国际科技北京有限公司Code-based testing：基于代码的测试Co-existence：共存性Commercial off-the-shelf software：商用离岸软件Comparator：比较器Compatibility testing：兼容性测试Compiler：编译器Complete testing：完全测试/穷尽测试Completion criteria：完成标准Complexity：复杂性Compliance：一致性Compliance testing：一致性测试Component：组件Component integration testing：组件集成测试Component specification：组件规格说明Component testing：组件测试Compound condition：组合条件Concrete test case low level test case ：详细测试用例Concurrency testing：并发测试Condition：条件表达式Condition combination coverage：条件组合覆盖率Condition coverage：条件覆盖率Condition determination coverage：条件判定覆盖率 Condition determination testing：条件判定测试Condition testing：条件测试Condition outcome：条件结果Confidence test smoke test ：信心测试冒烟测试Configuration：配置Configuration auditing：配置审核Configuration control：配置控制Configuration control board CCB ：配置控制委员会 Configuration identification：配置标识Configuration item：配置项Configuration management：配置管理Configuration testing：配置测试Confirmation testing：确认测试Conformance testing：一致性测试Consistency：一致性Control flow：控制流Control flow graph：控制流图Control flow path：控制流路径Conversion testing：转换测试COTS Commercial Off-The-Shelf software ：商业离岸软件 Coverage：覆盖率Coverage analysis：覆盖率分析领测国际科技北京有限公司Coverage item：覆盖项Coverage tool：覆盖率工具Custom software：定制软件Cyclomatic complexity：圈复杂度Cyclomatic number：圈数DDaily build：每日构建Data definition：数据定义Data driven testing：数据驱动测试Data flow：数据流Data flow analysis：数据流分析Data flow coverage：数据流覆盖率Data flow test：数据流测试Data integrity testing：数据完整性测试Database integrity testing：数据库完整性测试Dead code：无效代码Debugger：调试器Debugging：调试Debugging tool：调试工具Decision：判定Decision condition coverage：判定条件覆盖率 Decision condition testing：判定条件测试Decision coverage：判定覆盖率Decision table：判定表Decision table testing：判定表测试Decision testing：判定测试技术Decision outcome：判定结果Defect：缺陷Defect density：缺陷密度Defect Detection Percentage DDP ：缺陷发现率 Defect management：缺陷管理Defect management tool：缺陷管理工具Defect masking：缺陷屏蔽Defect report：缺陷报告Defect tracking tool：缺陷跟踪工具Definition-use pair：定义-使用对Deliverable：交付物Design-based testing：基于设计的测试Desk checking：桌面检查领测国际科技北京有限公司Development testing：开发测试Deviation：偏差Deviation report：偏差报告Dirty testing：负面测试Documentation testing：文档测试Domain：域Driver：驱动程序Dynamic analysis：动态分析Dynamic analysis tool：动态分析工具Dynamic comparison：动态比较Dynamic testing：动态测试EEfficiency：效率Efficiency testing：效率测试Elementary comparison testing：基本组合测试 Emulator：仿真器、仿真程序Entry criteria：入口标准Entry point：入口点Equivalence class：等价类Equivalence partition：等价区间Equivalence partition coverage：等价区间覆盖率Equivalence partitioning：等价划分技术Error：错误Error guessing：错误猜测技术Error seeding：错误撒播Error tolerance：错误容限Evaluation：评估Exception handling：异常处理Executable statement：可执行的语句Exercised：可执行的Exhaustive testing：穷尽测试Exit criteria：出口标准Exit point：出口点Expected outcome：预期结果Expected result：预期结果Exploratory testing：探测测试领测国际科技北京有限公司FFail：失败Failure：失败Failure mode：失败模式Failure Mode and Effect Analysis FMEA ：失败模式和影响分析Failure rate：失败频率Fault：缺陷Fault density：缺陷密度Fault Detection Percentage FDP ：缺陷发现率Fault masking：缺陷屏蔽Fault tolerance：缺陷容限Fault tree analysis：缺陷树分析Feature：特征Field testing：现场测试Finite state machine：有限状态机Finite state testing：有限状态测试Formal review：正式评审Frozen test basis：测试基线Function Point Analysis FPA ：功能点分析Functional integration：功能集成Functional requirement：功能需求Functional test design technique：功能测试设计技术 Functional testing：功能测试Functionality：功能性Functionality testing：功能性测试Gglass box testing：白盒测试HHeuristic evaluation：启发式评估High level test case：概要测试用例Horizontal traceability：水平跟踪领测国际科技北京有限公司IImpact analysis：影响分析Incremental development model：增量开发模型 Incremental testing：增量测试Incident：事件Incident management：事件管理Incident management tool：事件管理工具Incident report：事件报告Independence：独立Infeasible path：不可行路径Informal review：非正式评审Input：输入Input domain：输入范围Input value：输入值Inspection：审查Inspection leader：审查组织者Inspector：审查人员Installability：可安装性Installability testing：可安装性测试Installation guide：安装指南Installation wizard：安装向导Instrumentation：插装Instrumenter：插装工具Intake test：入口测试Integration：集成Integration testing：集成测试Integration testing in the large：大范围集成测试 Integration testing in the small：小范围集成测试 Interface testing：接口测试Interoperability：互通性Interoperability testing：互通性测试Invalid testing：无效性测试Isolation testing：隔离测试Item transmittal report：版本发布报告Iterative development model：迭代开发模型KKey performance indicator：关键绩效指标领测国际科技北京有限公司Keyword driven testing：关键字驱动测试LLearnability：易学性Level test plan：等级测试计划Link testing：组件集成测试Load testing：负载测试Logic-coverage testing：逻辑覆盖测试 Logic-driven testing：逻辑驱动测试Logical test case：逻辑测试用例Low level test case：详细测试用例MMaintenance：维护Maintenance testing：维护测试Maintainability：可维护性Maintainability testing：可维护性测试 Management review：管理评审Master test plan：综合测试计划Maturity：成熟度Measure：度量Measurement：度量Measurement scale：度量粒度Memory leak：内存泄漏Metric：度量Migration testing：移植测试Milestone：里程碑Mistake：错误Moderator：仲裁员Modified condition decision coverage：改进的条件判定覆盖率Modified condition decision testing：改进的条件判定测试Modified multiple condition coverage：改进的多重条件判定覆盖率Modified multiple condition testing：改进的多重条件判定测试 Module：模块Module testing：模块测试Monitor：监视器Multiple condition：多重条件Multiple condition coverage：多重条件覆盖率领测国际科技北京有限公司Multiple condition testing：多重条件测试Mutation analysis：变化分析Mutation testing：变化测试NN-switch coverage：N 切换覆盖率N-switch testing：N 切换测试Negative testing：负面测试Non-conformity：不一致Non-functional requirement：非功能需求Non-functional testing：非功能测试Non-functional test design techniques：非功能测试设计技术OOff-the-shelf software：离岸软件Operability：可操作性Operational environment：操作环境Operational profile testing：运行剖面测试Operational testing：操作测试Oracle：标准Outcome：输出/结果Output：输出Output domain：输出范围Output value：输出值PPair programming：结队编程Pair testing：结队测试Partition testing：分割测试Pass：通过Pass/fail criteria：通过/失败标准Path：路径Path coverage：路径覆盖Path sensitizing：路径敏感性Path testing：路径测试领测国际科技北京有限公司Peer review：同行评审Performance：性能Performance indicator：绩效指标Performance testing：性能测试Performance testing tool：性能测试工具 Phase test plan：阶段测试计划Portability：可移植性Portability testing：移植性测试Postcondition：结果条件Post-execution comparison：运行后比较 Precondition：初始条件Predicted outcome：预期结果Pretest：预测试Priority：优先级Probe effect：检测成本Problem：问题Problem management：问题管理Problem report：问题报告Process：流程Process cycle test：处理周期测试Product risk：产品风险Project：项目Project risk：项目风险Program instrumenter：编程工具Program testing：程序测试Project test plan：项目测试计划Pseudo-random：伪随机QQuality：质量Quality assurance：质量保证Quality attribute：质量属性Quality characteristic：质量特征Quality management：质量管理领测国际科技北京有限公司RRandom testing：随机测试Recorder：记录员Record/playback tool：记录/回放工具 Recoverability：可复原性Recoverability testing：可复原性测试Recovery testing：可复原性测试Regression testing：回归测试Regulation testing：一致性测试Release note：版本说明Reliability：可靠性Reliability testing：可靠性测试Replaceability：可替换性Requirement：需求Requirements-based testing：基于需求的测试 Requirements management tool：需求管理工具 Requirements phase：需求阶段Resource utilization：资源利用Resource utilization testing：资源利用测试 Result：结果Resumption criteria：继续测试标准Re-testing：再测试Review：评审Reviewer：评审人员Review tool：评审工具Risk：风险Risk analysis：风险分析Risk-based testing：基于风险的测试Risk control：风险控制Risk identification：风险识别Risk management：风险管理Risk mitigation：风险消减Robustness：健壮性Robustness testing：健壮性测试Root cause：根本原因SSafety：安全领测国际科技北京有限公司Safety testing：安全性测试Sanity test：健全测试Scalability：可测量性Scalability testing：可测量性测试Scenario testing：情景测试Scribe：记录员Scripting language：脚本语言Security：安全性Security testing：安全性测试Serviceability testing：可维护性测试 Severity：严重性Simulation：仿真Simulator：仿真程序、仿真器Site acceptance testing：定点验收测试Smoke test：冒烟测试Software：软件Software feature：软件功能Software quality：软件质量Software quality characteristic：软件质量特征Software test incident：软件测试事件Software test incident report：软件测试事件报告Software Usability Measurement Inventory SUMI ：软件可用性调查问卷Source statement：源语句Specification：规格说明Specification-based testing：基于规格说明的测试Specification-based test design technique：基于规格说明的测试设计技术Specified input：特定输入Stability：稳定性Standard software：标准软件Standards testing：标准测试State diagram：状态图State table：状态表State transition：状态迁移State transition testing：状态迁移测试Statement：语句Statement coverage：语句覆盖Statement testing：语句测试Static analysis：静态分析Static analysis tool：静态分析工具Static analyzer：静态分析工具Static code analysis：静态代码分析Static code analyzer：静态代码分析工具Static testing：静态测试Statistical testing：统计测试领测国际科技北京有限公司Status accounting：状态统计Storage：资源利用Storage testing：资源利用测试Stress testing：压力测试Structure-based techniques：基于结构的技术Structural coverage：结构覆盖Structural test design technique：结构测试设计技术 Structural testing：基于结构的测试Structured walkthrough：面向结构的走查Stub: 桩Subpath: 子路径Suitability: 符合性Suspension criteria: 暂停标准Syntax testing: 语法测试System:系统System integration testing：系统集成测试System testing：系统测试TTechnical review：技术评审Test：测试Test approach：测试方法Test automation：测试自动化Test basis：测试基础Test bed：测试环境Test case：测试用例Test case design technique：测试用例设计技术 Test case specification：测试用例规格说明Test case suite：测试用例套Test charter：测试宪章Test closure：测试结束Test comparator：测试比较工具Test comparison：测试比较Test completion criteria：测试比较标准Test condition：测试条件Test control：测试控制Test coverage：测试覆盖率Test cycle：测试周期Test data：测试数据Test data preparation tool：测试数据准备工具领测国际科技北京有限公司Test design：测试设计Test design specification：测试设计规格说明 Test design technique：测试设计技术Test design tool: 测试设计工具Test driver: 测试驱动程序Test driven development: 测试驱动开发Test environment: 测试环境Test evaluation report: 测试评估报告Test execution: 测试执行Test execution automation: 测试执行自动化 Test execution phase: 测试执行阶段Test execution schedule: 测试执行进度表Test execution technique: 测试执行技术Test execution tool: 测试执行工具Test fail: 测试失败Test generator: 测试生成工具Test leader:测试负责人Test harness：测试组件Test incident：测试事件Test incident report：测试事件报告Test infrastructure：测试基础组织Test input：测试输入Test item：测试项Test item transmittal report：测试项移交报告 Test level：测试等级Test log：测试日志Test logging：测试记录Test manager：测试经理Test management：测试管理Test management tool：测试管理工具Test Maturity Model TMM ：测试成熟度模型Test monitoring：测试跟踪Test object：测试对象Test objective：测试目的Test oracle：测试标准Test pass：测试通过Test performance indicator：测试绩效指标Test phase：测试阶段Test plan：测试计划Test planning：测试计划Test policy：测试方针Test Point Analysis TPA ：测试点分析Test procedure：测试过程领测国际科技北京有限公司Test procedure specification：测试过程规格说明 Test process：测试流程Test Process Improvement TPI ：测试流程改进 Test record：测试记录Test recording：测试记录Test reproduceability：测试可重现性Test report：测试报告Test requirement：测试需求Test run：测试运行Test run log：测试运行日志Test result：测试结果Test scenario：测试场景Test set：测试集Test situation：测试条件Test specification：测试规格说明Test specification technique：测试规格说明技术 Test stage：测试阶段Test strategy：测试策略Test suite：测试套Test summary report：测试总结报告Test target：测试目标Test tool：测试工具Test type：测试类型Testability：可测试性Testability review：可测试性评审Testable requirements：需求可测试性Tester：测试人员Testing：测试Testware：测试组件Thread testing：组件集成测试Time behavior：性能Top-down testing：自顶向下的测试Traceability：可跟踪性UUnderstandability：易懂性Unit：单元unit testing：单元测试Unreachable code：执行不到的代码领测国际科技北京有限公司Usability：易用性Usability testing：易用性测试Use case：用户用例Use case testing：用户用例测试User acceptance testing：用户验收测试 User scenario testing：用户场景测试 User test：用户测试VV -model：V 模式Validation：确认Variable：变量Verification：验证Vertical traceability：垂直可跟踪性 Version control：版本控制Volume testing：容量测试WWalkthrough：走查White-box test design technique：白盒测试设计技术 White-box testing：白盒测试Wide Band Delphi：Delphi 估计方法。

软件测试专业术语中英文对照AAcceptance testing : 验收测试Acceptance Testing：可接受性测试Accessibility test : 软体适用性测试actual outcome：实际结果Ad hoc testing : 随机测试Algorithm analysis : 算法分析algorithm：算法Alpha testing : α测试analysis：分析anomaly：异常application software：应用软件Application under test (AUT) : 所测试的应用程序Architecture : 构架Artifact : 工件ASQ：自动化软件质量（Automated Software Quality）Assertion checking : 断言检查Association : 关联Audit : 审计audit trail：审计跟踪Automated Testing：自动化测试BBackus-Naur Form：BNF范式baseline：基线Basic Block：基本块basis test set：基本测试集Behaviour : 行为Bench test : 基准测试benchmark：标杆/指标/基准Best practise : 最佳实践Beta testing : β测试Black Box Testing：黑盒测试Blocking bug : 阻碍性错误Bottom-up testing : 自底向上测试boundary value coverage：边界值覆盖boundary value testing：边界值测试Boundary values : 边界值Boundry Value Analysis：边界值分析branch condition combination coverage：分支条件组合覆盖branch condition combination testing：分支条件组合测试branch condition coverage：分支条件覆盖branch condition testing：分支条件测试branch condition：分支条件Branch coverage : 分支覆盖branch outcome：分支结果branch point：分支点branch testing：分支测试branch：分支Breadth Testing：广度测试Brute force testing: 强力测试Buddy test : 合伙测试Buffer : 缓冲Bug : 错误Bug bash : 错误大扫除bug fix : 错误修正Bug report : 错误报告Bug tracking system: 错误跟踪系统bug：缺陷Build : 工作版本（内部小版本）Build Verfication tests(BVTs): 版本验证测试Build-in : 内置CCapability Maturity Model (CMM): 能力成熟度模型Capability Maturity Model Integration (CMMI): 能力成熟度模型整合capture/playback tool：捕获/回放工具Capture/Replay Tool：捕获/回放工具CASE：计算机辅助软件工程（computer aided software engineering）CAST：计算机辅助测试cause-effect graph：因果图certification ：证明change control：变更控制Change Management :变更管理Change Request :变更请求Character Set : 字符集Check In :检入Check Out :检出Closeout : 收尾code audit ：代码审计Code coverage : 代码覆盖Code Inspection：代码检视Code page : 代码页Code rule : 编码规范Code sytle : 编码风格Code Walkthrough：代码走读code-based testing：基于代码的测试coding standards：编程规范Common sense : 常识Compatibility Testing：兼容性测试complete path testing ：完全路径测试completeness：完整性complexity ：复杂性Component testing : 组件测试Component：组件computation data use：计算数据使用computer system security：计算机系统安全性Concurrency user : 并发用户Condition coverage : 条件覆盖condition coverage：条件覆盖condition outcome：条件结果condition：条件configuration control：配置控制Configuration item : 配置项configuration management：配置管理Configuration testing : 配置测试conformance criterion：一致性标准Conformance Testing：一致性测试consistency ：一致性consistency checker：一致性检查器Control flow graph : 控制流程图control flow graph：控制流图control flow：控制流conversion testing：转换测试Core team : 核心小组corrective maintenance：故障检修correctness ：正确性coverage ：覆盖率coverage item：覆盖项crash：崩溃criticality analysis：关键性分析criticality：关键性CRM(change request management): 变更需求管理Customer-focused mindset : 客户为中心的理念体系Cyclomatic complexity : 圈复杂度Ddata corruption：数据污染data definition C-use pair：数据定义C-use使用对data definition P-use coverage：数据定义P-use覆盖data definition P-use pair：数据定义P-use使用对data definition：数据定义data definition-use coverage：数据定义使用覆盖data definition-use pair ：数据定义使用对data definition-use testing：数据定义使用测试data dictionary：数据字典Data Flow Analysis : 数据流分析data flow analysis：数据流分析data flow coverage：数据流覆盖data flow diagram：数据流图data flow testing：数据流测试data integrity：数据完整性data use：数据使用data validation：数据确认dead code：死代码Debug : 调试Debugging：调试Decision condition：判定条件Decision coverage : 判定覆盖decision coverage：判定覆盖decision outcome：判定结果decision table：判定表decision：判定Defect : 缺陷defect density : 缺陷密度Defect Tracking :缺陷跟踪Deployment : 部署Depth Testing：深度测试design for sustainability :可延续性的设计design of experiments：实验设计design-based testing：基于设计的测试Desk checking : 桌前检查desk checking：桌面检查Determine Usage Model : 确定应用模型Determine Potential Risks : 确定潜在风险diagnostic：诊断DIF(decimation in frequency) : 按频率抽取dirty testing：肮脏测试disaster recovery：灾难恢复DIT （decimation in time）: 按时间抽取documentation testing ：文档测试domain testing：域测试domain：域DTP DETAIL TEST PLAN详细确认测试计划Dynamic analysis : 动态分析dynamic analysis：动态分析Dynamic Testing：动态测试Eembedded software：嵌入式软件emulator：仿真End-to-End testing：端到端测试Enhanced Request :增强请求entity relationship diagram：实体关系图Encryption Source Code Base：加密算法源代码库Entry criteria : 准入条件entry point ：入口点Envisioning Phase : 构想阶段Equivalence class : 等价类Equivalence Class：等价类equivalence partition coverage：等价划分覆盖Equivalence partition testing : 等价划分测试equivalence partition testing:参考等价划分测试equivalence partition testing：等价划分测试Equivalence Partitioning：等价划分Error : 错误Error guessing : 错误猜测error seeding：错误播种/错误插值error：错误Event-driven : 事件驱动Exception handlers : 异常处理器exception：异常/例外executable statement：可执行语句Exhaustive Testing：穷尽测试exit point：出口点expected outcome：期望结果FExploratory testing : 探索性测试Failure : 失效Fault : 故障fault：故障feasible path：可达路径feature testing：特性测试Field testing : 现场测试FMEA：失效模型效果分析（Failure Modes and Effects Analysis）FMECA：失效模型效果关键性分析(Failure Modes and Effects Criticality Analysis)Framework : 框架FTA：故障树分析(Fault Tree Analysis)functional decomposition：功能分解Functional Specification ：功能规格说明书Functional testing : 功能测试Functional Testing：功能测试GG11N(Globalization) : 全球化Gap analysis : 差距分析Garbage characters : 乱码字符glass box testing：玻璃盒测试Glass-box testing : 白箱测试或白盒测试Glossary : 术语表GUI(Graphical User Interface): 图形用户界面HHard-coding : 硬编码Hotfix : 热补丁II18N(Internationalization): 国际化Identify Exploratory Tests –识别探索性测试IEEE：美国电子与电器工程师学会（Institute of Electrical and Electronic Engineers）Incident 事故Incremental testing : 渐增测试incremental testing：渐增测试infeasible path：不可达路径input domain：输入域Inspection : 审查inspection：检视installability testing：可安装性测试Installing testing : 安装测试instrumentation：插装instrumenter：插装器Integration :集成Integration testing : 集成测试interface : 接口interface analysis：接口分析interface testing：接口测试interface：接口invalid inputs：无效输入isolation testing：孤立测试Issue : 问题Iteration : 迭代Iterative development: 迭代开发Jjob control language：工作控制语言Job：工作KKey concepts : 关键概念Key Process Area : 关键过程区域Keyword driven testing : 关键字驱动测试Kick-off meeting : 动会议LL10N(Localization) : 本地化Lag time : 延迟时间LCSAJ：线性代码顺序和跳转（Linear Code Sequence And Jump）LCSAJ coverage：LCSAJ覆盖LCSAJ testing：LCSAJ测试Lead time : 前置时间Load testing : 负载测试Load Testing：负载测试Localizability testing: 本地化能力测试Localization testing : 本地化测试logic analysis：逻辑分析logic-coverage testing：逻辑覆盖测试MMaintainability : 可维护性maintainability testing：可维护性测试Maintenance : 维护Master project schedule :总体项目方案Measurement : 度量Memory leak : 内存泄漏Migration testing : 迁移测试Milestone : 里程碑Mock up : 模型，原型modified condition/decision coverage：修改条件/判定覆盖modified condition/decision testing ：修改条件/判定测试modular decomposition:参考模块分解Module testing : 模块测试Monkey testing : 跳跃式测试Monkey Testing：跳跃式测试mouse over：鼠标在对象之上mouse leave：鼠标离开对象MTBF：平均失效间隔实际（mean time between failures）MTP MAIN TEST PLAN主确认计划MTTF：平均失效时间（mean time to failure）MTTR：平均修复时间（mean time to repair）multiple condition coverage：多条件覆盖mutation analysis：变体分析NN/A(Not applicable) : 不适用的Negative Testing : 逆向测试, 反向测试, 负面测试negative testing:参考负面测试Negative Testing：逆向测试/反向测试/负面测试off by one：缓冲溢出错误non-functional requirements testing:非功能需求测试nominal load:额定负载N-switch coverage：N切换覆盖N-switch testing：N切换测试N-transitions：N转换OOff-the-shelf software : 套装软件operational testing：可操作性测试output domain：输出域Ppaper audit:书面审计Pair Programming : 成对编程partition testing：分类测试Path coverage : 路径覆盖path coverage：路径覆盖path sensitizing：路径敏感性path testing：路径测试path：路径Peer review : 同行评审Performance : 性能Performance indicator: 性能（绩效）指标Performance testing : 性能测试Pilot : 试验Pilot testing : 引导测试Portability : 可移植性portability testing：可移植性测试Positive testing : 正向测试Postcondition : 后置条件Precondition : 前提条件precondition：预置条件predicate data use：谓词数据使用predicate：谓词Priority : 优先权program instrumenter：程序插装progressive testing：递进测试Prototype : 原型Pseudo code : 伪代码pseudo-localization testing：伪本地化测试pseudo-random：伪随机QQC：质量控制（quality control）Quality assurance(QA): 质量保证Quality Control(QC) : 质量控制RRace Condition：竞争状态Rational Unified Process(以下简称RUP):瑞理统一工艺Recovery testing : 恢复测试recovery testing：恢复性测试Refactoring : 重构regression analysis and testing：回归分析和测试Regression testing : 回归测试Release : 发布Release note : 版本说明release：发布Reliability : 可靠性reliability assessment：可靠性评价reliability：可靠性Requirements management tool: 需求管理工具Requirements-based testing : 基于需求的测试Return of Investment（ROI）: 投资回报率review：评审Risk assessment : 风险评估risk：风险Robustness : 强健性Root Cause Analysis(RCA): 根本原因分析Ssafety critical：严格的安全性safety：（生命）安全性Sanity testing : 健全测试Sanity Testing：理智测试Schema Repository : 模式库Screen shot : 抓屏、截图SDP：软件开发计划（software development plan）Security testing : 安全性测试security testing：安全性测试security.：（信息）安全性serviceability testing：可服务性测试Severity : 严重性Shipment : 发布simple subpath：简单子路径Simulation : 模拟Simulator : 模拟器SLA(Service level agreement): 服务级别协议SLA：服务级别协议（service level agreement）Smoke testing : 冒烟测试Software development plan(SDP): 软件开发计划Software development process: 软件开发过程software development process：软件开发过程software diversity：软件多样性software element：软件元素software engineering environment：软件工程环境software engineering：软件工程Software life cycle : 软件生命周期source code：源代码source statement：源语句Specification : 规格说明书specified input：指定的输入spiral model ：螺旋模型SQAP SOFTWARE QUALITY ASSURENCE PLAN 软件质量保证计划SQL：结构化查询语句（structured query language）Staged Delivery：分布交付方法state diagram：状态图state transition testing ：状态转换测试state transition：状态转换state：状态Statement coverage : 语句覆盖statement testing：语句测试statement：语句Static Analysis：静态分析Static Analyzer：静态分析器Static Testing：静态测试statistical testing：统计测试Stepwise refinement : 逐步优化storage testing：存储测试Stress Testing : 压力测试structural coverage：结构化覆盖structural test case design：结构化测试用例设计structural testing：结构化测试structured basis testing：结构化的基础测试structured design：结构化设计structured programming：结构化编程structured walkthrough：结构化走读stub：桩sub-area：子域Summary：总结SVVP SOFTWARE Vevification&Validation PLAN：软件验证和确认计划symbolic evaluation：符号评价symbolic execution:参考符号执行symbolic execution：符号执行symbolic trace：符号轨迹Synchronization : 同步Syntax testing : 语法分析system analysis：系统分析System design : 系统设计system integration：系统集成System Testing : 系统测试TTC TEST CASE 测试用例TCS TEST CASE SPECIFICATION 测试用例规格说明TDS TEST DESIGN SPECIFICATION 测试设计规格说明书technical requirements testing：技术需求测试Test : 测试test automation：测试自动化Test case : 测试用例test case design technique：测试用例设计技术test case suite：测试用例套test comparator：测试比较器test completion criterion：测试完成标准test coverage:测试覆盖Test design : 测试设计Test driver : 测试驱动test environment:测试环境test execution technique:测试执行技术test execution:测试执行test generator:测试生成器test harness:测试用具Test infrastructure : 测试基础建设test log:测试日志test measurement technique:测试度量技术Test Metrics :测试度量test procedure:测试规程test records:测试记录test report:测试报告Test scenario : 测试场景Test Script:测试脚本Test Specification:测试规格Test strategy : 测试策略test suite:测试套Test target : 测试目标Test ware : 测试工具Testability : 可测试性testability:可测试性Testing bed : 测试平台Testing coverage : 测试覆盖Testing environment : 测试环境Testing item : 测试项Testing plan : 测试计划Testing procedure : 测试过程Thread testing : 线程测试time sharing:时间共享time-boxed : 固定时间TIR test incident report 测试事故报告ToolTip:控件提示或说明top-down testing:自顶向下测试TPS TEST PEOCESS SPECIFICATION 测试步骤规格说明Traceability : 可跟踪性traceability analysis:跟踪性分析traceability matrix:跟踪矩阵Trade-off : 平衡transaction:事务/处理transaction volume：交易量transform analysis:事务分析trojan horse:特洛伊木马truth table:真值表TST TEST SUMMARY REPORT 测试总结报告Tune System : 调试系统TW TEST WARE :测试件UUsability Testing:可用性测试Usage scenario : 使用场景User acceptance Test : 用户验收测试User database :用户数据库User interface(UI) : 用户界面User profile : 用户信息User scenario : 用户场景VV&V (Verification & Validation) : 验证&确认validation :确认verification :验证version :版本Virtual user : 虚拟用户volume testing:容量测试VSS（visual source safe）：VTP Verification TEST PLAN验证测试计划VTR Verification TEST REPORT验证测试报告WWalkthrough : 走读Waterfall model : 瀑布模型White box testing : 白盒测试Work breakdown structure (WBS) : 任务分解结构ZZero bug bounce (ZBB) : 零错误反弹。

常见的软件过程中的度量指标英文回答：Common Software Process Metrics.Software process metrics are quantitative measures of the characteristics of a software process. They are used to track progress, identify bottlenecks, and improve the quality of the process. Common software process metrics include:Time metrics: These metrics measure the amount of time it takes to complete a software process or activity. Examples of time metrics include:Cycle time: The time it takes from the start of a software process to the delivery of the final product.Lead time: The time it takes from the initial request for a software product to the delivery of the finalproduct.Defect detection time: The time it takes from the introduction of a defect into a software product to its detection.Cost metrics: These metrics measure the amount of money it costs to complete a software process or activity. Examples of cost metrics include:Total cost of ownership: The total cost of a software product over its entire lifetime, including development, maintenance, and support costs.Return on investment: The ratio of the benefits of a software product to the costs of developing and maintaining it.Quality metrics: These metrics measure the quality of a software product or process. Examples of quality metrics include:Defect density: The number of defects in a software product per unit of code.Mean time between failures: The average amount of time between failures of a software system.Customer satisfaction: The level of satisfaction of customers with a software product or process.Productivity metrics: These metrics measure the productivity of a software team or individual. Examples of productivity metrics include:Lines of code per day: The number of lines of code written by a developer per day.Story points completed per sprint: The number of story points completed by a team in a sprint.Process maturity metrics: These metrics measure the maturity of a software process. Examples of process maturity metrics include:Capability maturity model integration (CMMI) level: A measure of the maturity of a software process based onthe Capability Maturity Model Integration framework.ISO/IEC 27001 certification: A certification thata software process meets the requirements of the ISO/IEC 27001 information security standard.Software process metrics can be used to improve the quality and efficiency of software processes. By tracking these metrics, organizations can identify areas for improvement and make changes to their processes accordingly.中文回答：常见的软件过程度量指标。

Integrated simulation of the injection molding process withstereolithography moldsJongsoo Lee*, Jonghun KimSchool of Mechanical EngineeringYonsei University, Seoul 120-749 Korea(Manuscript Received December 12, 2006; Revised March 26, 2007; Accepted March 26, 2007)Abstract Functional parts are needed for design verification testing, field trials, customer evaluation, and production planning. By eliminating multiple steps, the creation of the injection mold directly by a rapid prototyping (RP) process holds the best promise of reducing the time and cost needed to mold low-volume quantities of parts. The potential of this integration of injection molding with RP has been demonstrated many times. What is missing is the fundamental understanding of how the modifications to the mold material and RP m anufacturing process impact both the mold design and the injection molding process. In addition, numerical simulation techniques have now become helpful tools of mold designers and process engineers for traditional injection molding. But all current simulation packages for conventional injection molding are no longer applicable to this new type of injection molds, mainly because the property of the mold material changes greatly. In this paper, an integrated approach to accomplish a numerical simulation of injection molding into rapid-prototyped molds is established and a corresponding simulation system is developed. Comparisons with experimental results are employed for verification, which show that the present scheme is well suited to handle RP fabricated stereolithography (SL) molds.Keywords Injection molding Numerical simulation Rapid prototyping1 IntroductionIn injection molding, the polymer melt at high temperature is injected into the mold under high pressure [1]. Thus, the mold material needs to have thermal and mechanical properties capable of withstanding the temperatures and pressures of the molding cycle. The focus of many studies has been to create theinjection mold directly by a rapid prototyping (RP) process. By eliminating multiple steps, this method of tooling holds the best promise of reducing the time and cost needed to create low-volume quantities of parts in a production material. The potential of integrating injection molding with RP technologies has been demonstrated many times. The properties of RP molds are very different from those of traditional metal molds. The key differences are the properties ofthermal conductivity and elastic modulus (rigidity). For example, the polymers used in RP-fabricated stereolithography (SL) molds have a thermal conductivity that is less than one thousandth that of an aluminum tool. In using RP technologies to create molds, the entire mold design and injection-molding process parameters need to be modified and optimized from traditional methodologies due to the completely different tool material. However, there is still not a fundamen tal understanding of how the modifications t o the mold tooling method and material impact both the mold design and the injection molding process parameters. One cannot obtain reasonable results by simply changing a few material properties in current models. Also, using traditional approaches when making actual parts may be generating sub-optimal results. So there is a dire need to study the interaction between the rapid tooling (RT) process and material and injection molding, so as to establish the mold design criteria and techniques for an RT-oriented injection molding process.In addition, computer simulation is an effective approach for predicting the quality of molded parts. Commercially available simulation packages of the traditional injection molding process have now become routine tools of the mold designer and process engineer [2]. Unfortunately, current simulation programs for conventional injection molding are no longer applicable to RP molds, because of the dramatically dissimilar tool material. For instance, in using the existing simulation software with aluminum and SL molds and comparing with experimental results, though the simulation values of part distortion are reasonable for the aluminum mold, results are unacceptable, with the error exceeding 50%. The distortion during injection molding is due to shrinkage and warpage of the plastic part, as well as the mold. For ordinarily molds, the main factor is the shrinkage and warpage of the plastic part, which is modeled accurately in current simulations. But for RP molds, the distortion of the mold has potentially more influence, which have been neglected in current models. For instance, [3] used a simple three-step simulation process to consider the mold distortion, which had too much deviation.In this paper, based on the above analysis, a new simulation system for RP molds is developed. The proposed system focuses on predicting part distortion, which is dominating defect in RP-molded parts. The developed simulation can be applied as an evaluation tool for RP mold design and process optimization. Our simula tion system is verified by an experimental example.Although many materials are available for use in RP technologies, we concentrate on using stereolithography (SL), the original RP technology, to create polymer molds. The SL process uses photopolymer and laser energy to build a part layer by layer. Using SL takes advantage of both the commercial dominance of SL in the RP industry and the subsequent expertise base thathas been developed for creating accurate, high-quality parts. Until recently, SL was primarily used to create physical models for visual inspection and form-fit studies with very limited func-tional applications. However, the newer generation stereolithographic photopolymers have improved dimensional, mechanical and thermal properties making it possible to use them for actual functional molds.2 Integrated simulation of the molding process2.1 MethodologyIn order to simulate the use of an SL mold in the injection molding process, an iterative method is proposed. Different software modules have been developed and used to accomplish this task. The main assumption is that temperature and load boundary conditions cause significant distortions in the SL mold. The simulation steps are as follows:1The part geometry is modeled as a solid model, which is translated to a file readable by the flow analysis package.2Simulate the mold-filling process of the melt into a pho topolymer mold, which will output the resulting temperature and pressure profiles.3Structural analysis is then performed on the photopolymer mold model using the thermal and load boundary conditions obtained from the previous step, which calculates the distortion that the mold undergo during the injection process.4If the distortion of the mold converges, move to the next step. Otherwise, the distorted mold cavity is then modeled (changes in the dimensions of the cavity after distortion), and returns to the second step to simulate the melt injection into the distorted mold.5The shrinkage and warpage simulation of the injection molded part is then applied, which calculates the final distor tions of the molded part.In above simulation flow, there are three basic simulation mod ules.2. 2 Filling simulation of the melt2.2.1 Mathematical modelingIn order to simulate the use of an SL mold in the injection molding process, an iterative method is proposed. Different software modules have been developed and used to accomplish this task. The main assumption is that temperature and load boundary conditions cause significant distortions in the SL mold. The simulation steps are as follows:1. The part geometry is modeled as a solid model, which is translated to a file readable by the flow analysis package.2. Simulate the mold-filling process of the melt into a photopolymer mold, which will output the resulting temperature and pressure profiles.3. Structural analysis is then performed on the photopolymer mold model using the thermal and load boundary conditions obtained from the previous step, which calculates the distortion that the mold undergo during the injection process.4. If the distortion of the mold converges, move to the next step. Otherwise, the distorted mold cavity is then modeled (changes in the dimensions of the cavity after distortion), and returns to the second step to simulate the melt injection into the distorted mold.5. The shrinkage and warpage simulation of the injection molded part is then applied, which calculates the final distortions of the molded part.In above simulation flow, there are three basic simulation modules.2.2 Filling simulation of the melt2.2.1 Mathematical modelingComputer simulation techniques have had success in predicting filling behavior in extremely complicated geometries. However, most of the current numerical implementation is based on a hybrid finite-element/finite-difference solution with the middleplane model. The application process of simulation packages based on this model is illustrated in Fig. 2-1. However, unlike the surface/solid model in mold-design CAD systems, the so-called middle-plane (as shown in Fig. 2-1b) is an imaginary arbitrary planar geometry at the middle of the cavity in the gap-wise direction, which should bring about great inconvenience in applications. For example, surface models are commonly used in current RP systems (generally STL file format), so secondary modeling is unavoidable when using simulation packages because the models in the RP and simulation systems are different. Considering these defects, the surface model of the cavity is introduced as datum planes in the simulation, instead of the middle-plane.According to the previous investigations [4–6], fillinggoverning equations for the flow and temperature field can be written as:where x, y are the planar coordinates in the middle-plane, and z is the gap-wise coordinate; u, v,w are the velocity components in the x, y, z directions; u, v are the average whole-gap thicknesses; and η, ρ,CP (T), K(T) represent viscosity, density, specific heat and thermal conductivity of polymer melt, respectively.Fig.2-1 a–d. Schematic procedure of the simulation with middle-plane model. a The 3-D surface model b The middle-plane model c The meshed middle-plane model d The display of the simulation result In addition, boundary conditions in the gap-wise direction can be defined as:where TW is the constant wall temperature (shown in Fig. 2a).Combining Eqs. 1–4 with Eqs. 5–6, it follows that the distributions of the u, v, T, P at z coordinates should be symmetrical, with the mirror axis being z = 0, and consequently the u, v averaged in half-gap thickness is equal to that averaged in wholegap thickness. Based on this characteristic, we can divide the whole cavity into two equal parts in the gap-wise direction, as described by Part I and Part II in Fig. 2b. At the same time, triangular finite elements are generated in the surface(s) of the cavity (at z = 0 in Fig. 2b), instead of the middle-plane (at z = 0 in Fig. 2a). Accordingly, finite-difference increments in the gapwise direction are employed only in the inside of the surface(s) (wall to middle/center-line), which, in Fig. 2b, means from z = 0 to z = b. This is single-sided instead of two-sided with respect to the middle-plane (i.e. from the middle-line to two walls). In addition, the coordinate system is changed from Fig. 2a to Fig. 2b to alter the finite-element/finite-difference scheme, as shown in Fig. 2b. With the above adjustment, governing equations are still Eqs. 1–4. However, the original boundary conditions inthe gapwise direction are rewritten as:Meanwhile, additional boundary conditions must be employed at z = b in order to keep the flows at the juncture of the two parts at the same section coordinate [7]:where subscripts I, II represent the parameters of Part I and Part II, respectively, and Cm-I and Cm-II indicate the moving free melt-fronts of the surfaces of the divided two parts in the filling stage.It should be noted that, unlike conditions Eqs. 7 and 8, ensuring conditions Eqs. 9 and 10 are upheld in numerical implementations becomes more difficult due to the following reasons:1. The surfaces at the same section have been meshed respectively, which leads to a distinctive pattern of finite elements at the same section. Thus, an interpolation operation should be employed for u, v, T, P during the comparison between the two parts at the juncture.2. Because the two parts have respective flow fields with respect to the nodes at point A and point C (as shown in Fig. 2b) at the same section, it is possible to have either both filled or one filled (and one empty). These two cases should be handled separately, averaging the operation for the former, whereas assigning operation for the latter.3. It follows that a small difference between the melt-fronts is permissible. That allowance can be implemented by time allowance control or preferable location allowance control of the melt-front nodes.4. The boundaries of the flow field expand by each melt-front advancement, so it is necessary to check the condition Eq. 10 after each change in the melt-front.5. In view of above-mentioned analysis, the physical parameters at the nodes of the same section should be compared and adjusted, so the information describing finite elements of the same section should be prepared before simulation, that is, the matching operation among the elements should be preformed.Fig. 2a,b. Illustrative of boundary conditions in the gap-wise direction a of the middle-plane model b of thesurface model2.2.2 Numerical implementationPressure field. In modeling viscosity η, which is a function of shear rate, temperature and pressure of melt, the shear-thinning behavior can be well represented by a cross-type model such as:where n corresponds to the power-law index, and τ∗ characterizes the shear stress level of the transition region between the Newtonian and power-law asymptotic limits. In terms of an Arrhenius-type temperature sensitivity and exponential pressure dependence, η0(T, P) can be represented with reasonable accuracy as follows:Equations 11 and 12 constitute a five-constant (n, τ∗, B, Tb, β) representation for viscosity. The shear rate for viscosity calculation is obtained by:Based on the above, we can infer the following filling pressure equation from the governing Eqs. 1–4:where S is calculated by S = b0/(b−z)2η d z. Applying the Galerkin method, the pressure finite-element equation is deduced as:where l_ traverses all elements, including node N, and where I and j represent the local node number in element l_ corresponding to the node number N and N_ in the whole, respectively. The D(l_) ij is calculated as follows:where A(l_) represents triangular finite elements, and L(l_) i is the pressure trial function in finite elements.Temperature field. To determine the temperature profile across the gap, each triangular finite element at the surface is further divided into NZ layers for the finite-difference grid.The left item of the energy equation (Eq. 4) can be expressed as:where TN, j,t represents the temperature of the j layer of node N at time t.The heat conduction item is calculated by:where l traverses all elements, including node N, and i and j represent the local node number in element l corresponding to the node number N and N_ in the whole, respectively.The heat convection item is calculated by:For viscous heat, it follows that:Substituting Eqs. 17–20 into the energy equation (Eq. 4), the temperature equation becomes:2.3 Structural analysis of the moldThe purpose of structural analysis is to predict the deformation occurring in the photopolymer mold due to the thermal and mechanical loads of the filling process. This model is based on a three-dimensional thermoelastic boundary element method (BEM). The BEM is ideally suited for this application because only the deformation of the mold surfaces is of interest. Moreover, the BEM has an advantage over other techniques in that computing effort is not wasted on calculating deformation within the mold.The stresses resulting from the process loads are well within the elastic range of the mold material. Therefore, the mold deformation model is based on a thermoelastic formulation. The thermal and mechanical properties of the mold are assumed to be isotropic and temperature independent.Although the process is cyclic, time-averaged values of temperature and heat flux are used for calculating the mold deformation. Typically, transient temperature variations within a mold have been restricted to regions local to the cavity surface and the nozzle tip [8]. The transients decay sharply with distance from the cavity surface and generally little variation is observed beyond distances as small as 2.5 mm. This suggests that the contribution from the transients to the deformation at the mold block interface is small, and therefore it is reasonable to neglect the transient effects. The steady state temperature field satisfies Laplace’s equation 2T = 0 and the time-averaged boundary conditions. The boundary conditions on the mold surfaces are described in detail by Tang et al. [9]. As for the mechanical boundary conditions, the cavity surface is subjected to the melt pressure, the surfaces of the mold connected to the worktable are fixed in space, and other external surfaces are assumed to be stress free.The derivation of the thermoelastic boundary integral formulation is well known [10]. It is given by:where uk, pk and T are the displacement, traction and temperature,α, ν represent the thermal expansion coefficient and Poisson’s ratio of the material, and r = |y−x|. clk(x) is the surfacecoefficient which depends on the local geometry at x, the orientation of the coordinate frame and Poisson’s ratio for the domain [11]. The fundamental displacement ˜ulk at a point y in the xk direction, in a three-dimensional infinite isotropic elastic domain, results from a unit load concentrated at a point x acting in the xl direction and is of the form:where δlk is the Kronecker delta function and μ is the shear modulus of the mold material.The fundamental traction ˜plk , measured at the point y on a surface with unit normal n, is:Discretizing the surface of the mold into a total of N elements transforms Eq. 22 to:where Γn refers to the n th surface element on the domain.Substituting the appropriate linear shape functions into Eq. 25, the linear boundary element formulation for the mold deformation model is obtained. The equation is applied at each node on the discretized mold surface, thus giving a system of 3N linear equations, where N is the total number of nodes. Each node has eight associated quantities: three components of displacement, three components of traction, a temperature and a heat flux. The steady state thermal model supplies temperature and flux values as known quantities for each node, and of the remaining six quantities, three must be specified. Moreover, the displacement values specified at a certain number of nodes must eliminate the possibility of a rigid-body motion or rigid-body rotation to ensure a non-singular system of equations. The resulting system of equations is assembled into a integrated matrix, which is solved with an iterative solver.2.4 Shrinkage and warpage simulation of the molded partInternal stresses in injection-molded components are the principal cause of shrinkage and warpage. These residual stresses are mainly frozen-in thermal stresses due to inhomogeneous cooling, when surface layers stiffen sooner than the core region, as in free quenching. Based onthe assumption of the linear thermo-elastic and linear thermo-viscoelastic compressible behavior of the polymeric materials, shrinkage and warpage are obtained implicitly using displacement formulations, and the governing equations can be solved numerically using a finite element method.With the basic assumptions of injection molding [12], the components of stress and strain are given by:The deviatoric components of stress and strain, respectively, are given byUsing a similar approach developed by Lee and Rogers [13] for predicting the residual stresses in the tempering of glass, an integral form of the viscoelastic constitutive relationships is used, and the in-plane stresses can be related to the strains by the following equation:Where G1 is the relaxation shear modulus of the material. The dilatational stresses can be related to the strain as follows:Where K is the relaxation bulk modulus of the material, and the definition of α and Θ is:If α(t) = α0, applying Eq. 27 to Eq. 29 results in:Similarly, applying Eq. 31 to Eq. 28 and eliminating strain εxx(z, t) results in:Employing a Laplace transform to Eq. 32, the auxiliary modulus R(ξ) is given by:Using the above constitutive equation (Eq. 33) and simplified forms of the stresses and strains in the mold, the formulation of the residual stress of the injection molded part during the cooling stage is obtain by:Equation 34 can be solved through the application of trapezoidal quadrature. Due to the rapid initial change in the material time, a quasi-numerical procedure is employed for evaluating the integral item. The auxiliary modulus is evaluated numerically by the trapezoidal rule.For warpage analysis, nodal displacements and curvatures for shell elements are expressed as:where [k] is the element stiffness matrix, [Be] is the derivative operator matrix, {d} is the displacements, and {re} is the element load vector which can be evaluated by:The use of a full three-dimensional FEM analysis can achieve accurate warpage results, however, it is cumbersome when the shape of the part is very complicated. In this paper, a twodimensional FEM method, based on shell theory, was used because most injection-molded parts have a sheet-like geometry in which the thickness is much smaller than the other dimensions of the part. Therefore, the part can be regarded as an assembly of flat elements to predict warpage. Each three-node shell element is a combination of a constant strain triangular element (CST) and a discrete Kirchhoff triangular element (DKT), as shown in Fig. 3. Thus, the warpage can be separated into plane-stretching deformation of the CST and plate-bending deformation of the DKT, and correspondingly, the element stiffness matrix to describe warpage can also be divided into the stretching-stiffness matrix and bending-stiffness matrix.Fig. 3a–c. Deformation decomposition of shell element in the local coordinate system. a In-plane stretchingelement b Plate-bending element c Shell element3 Experimental validationTo assess the usefulness of the proposed model and developed program, verification is important. The distortions obtained from the simulation model are compared to the ones from SL injection molding experiments whose data is presented in the literature [8]. A common injection molded part with the dimensions of 36×36×6 mm is considered in the experiment, as shown in Fig. 4. The thickness dimensions of the thin walls and rib are both 1.5 mm; and polypropylene was used as the injection material. The injection machine was a production level ARGURY Hydronica 320-210-750 with the following process parameters: a melt temperature of 250 ◦C; an ambient temperature of 30 ◦C; an injection pressure of 13.79 MPa; an injection time of 3 s; and a cooling time of 48 s. The SL material used, Dupont SOMOSTM 6110 resin, has the ability to resist temperatures of up to 300 ◦C temperatures. As mentioned above, thermal conductivity of the mold is a major factor that differentiates between an SL and a traditional mold. Poor heat transfer in the mold would produce a non-uniform temperature distribution, thus causing warpage that distorts the completed parts. For an SL mold, a longer cycle time would be expected. The method of using a thin shell SL mold backed with a higher thermal conductivity metal (aluminum) was selected to increase thermal conductivity of the SL mold.Fig. 4. Experimental cavity modelFig. 5. A comparison of the distortion variation in the X direction for different thermal conductivity; where “Experimental”, “present”, “three-step”, and “conventional” mean the results of the experimental, the presented simulation, the three-step simulation process and the conventional injection molding simulation, respectively.Fig. 6. Comparison of the distortion variation in the Y direction for different thermal conductivitiesFig. 7. Comparison of the distortion variation in the Z direction for different thermal conductivitiesFig. 8. Comparison of the twist variation for different thermal conductivities For this part, distortion includes the displacements in three directions and the twist (the difference in angle between two initially parallel edges). The validation results are shown in Fig.5 to Fig. 8. These figures also include the distortion values predicted by conventional injection molding simulation and the three-step model reported in [3].4 ConclusionsIn this paper, an integrated model to accomplish the numerical simulation of injection molding into rapid-prototyped molds is established and a corresponding simulation system is developed. For verification, an experiment is also carried out with an RPfabricated SL mold.It is seen that a conventional simulation using current injection molding software breaks down for a photopolymer mold. It is assumed that this is due to the distortion in the mold caused by the temperature and load conditions of injection. The three-step approach also has much deviation. The developed model gives results closer to experimental.Improvement in thermal conductivity of the photopolymer significantly increases part quality. Since the effect of temperature seems to be more dominant than that of pressure (load), an improvement in the thermal conductivity of the photopolymer can improve the part quality significantly.Rapid Prototyping (RP) is a technology makes it possible to manufacture prototypes quickly and inexpensively, regardless of their complexity. Rapid Tooling (RT) is the next step in RP’s steady progress and much work is being done to obtain more accurate tools to define the parameters of the process. Existing simulation tools can not provide the researcher with a useful means of studying relative changes. An integrated model, such as the one presented in this paper, is necessary to obtain accurate predictions of the actual quality of final parts. In the future, we expect to see this work expanded to develop simulations program for injection into RP molds manufactured by other RT processes.References1. Wang KK (1980) System approach to injection molding process. 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一， TL9000 返还率a) 返还率计算公式：×12100%IRR IRR IRR =⨯基本装运周期内的返还数基本装运周期内的发运数IRR 基本装运周期内发运数：计算月之前6个月的发货总量(不包括计算月） IRR 基本装运周期内返还数：发货在计算月或之前6个月期间内，并在计算月内返还的总量。

×12100%YRR YRR YRR =⨯基本装运周期内的返还数基本装运周期内的发运数YRR 基本装运周期内发运数：计算月之前7~18个月的发货总量.YRR 基本装运周期内返还数：发货在计算月之前7~18个月期间内，并在计算月内返还的总量。

×12100%LTR LTR LTR =⨯基本装运周期内的返还数基本装运周期内的发运数LTR 基本装运周期内发运数：计算月之前19个月之前包括第19个月的期间内的发货总量.LTR 基本装运周期内返还数：发货在计算月之前19个月并且包括第19个月的期间内，并在计算月内返还的总量。

b) 数据源：PMS 包装出库管理信息系统、Oracle 发货系统报表、Oracle CRC 维修系统与新加坡维修数据库查询报表(GSS: Joy Chen)．c) Return Rate 计算月的定义：上月25日至本月的24日二， FPY(First Pass Yield)1. System Product FPYa) 计算公式：Products FPY=SMT FPY(%)*Unit TEST FPY(%) b) 数据源：MES 系统Manufacturing Quality Weekly Report (QD:许剑鹏)2. EMS Yielda) 计算公式：EMS Yield Rate=Yield/Inputb) 数据源：UT KPI Report (Foxconn: Huang Yuanba/东信：汪盈）三，LRR (Lot Reject Rate)a)计算公式：LRR= Rejected Lots/ Inspected Lotsb)数据源：Oracle系统IQC Weekly Report(QD:徐海平/余红华)四，CoPQ (Cost of Poor Quality )1.Def/Slow moving inventory scrapa)计算公式：Material Cost=Qty*Unit Costb)数据源: UTS Item Product Line Report(Finance: Xiao Xiao)Write off List (Logistic: Zheng Aiqin, Su Jing)2.TBU EMS Material Attritiona)计算公式：UT承担金额=盘亏总金额-EMS赔偿总金额b)数据源：EMS 工厂盘点月报（Logistics: Zhao Yufang）3.Stop Line Costa)计算公式：Stop Line Cost=Down Time(H)/6*$100b)数据源：Production Monthly Report（GSC: Qian Huan）4.Slow moving(excess) Inventory handling costa)计算公式：持库成本＝Slow moving库存金额* (年贷款利率/12 + 保险金利率) +仓库每月管理费用* Slow moving库存金额/ 库存总金额b)数据源：Inventory Detail List-System Product（GSC: Wu Xiao）HSTC aging report (Finance: Shelley Jiang)Logistic Expense report (Finance: Xiao Xiao)5.UT Rework Cost (Include related labor and material)a)计算公式：返工成本＝返工工时*每小时的制造工时费＋材料费用b)数据源：Production rework cost monthly report（GSC: Wu Zhiyong）制造工时费(Finance:Xiao Xiao)6.TBU CSD/"e 站式"/ Costa)计算公式：TBU CSD/"e 站式"/ Cost＝运费+材料费＋劳务费b)数据源：Warranty monthly report（Finance: Sharon Yang）7.CRC Material Costa)计算公式：CRC Material Cost＝CRC Repair Qty*Material Unit Costb)数据源：CRC维修器件明细单（IT: Lily Song）UTS Item Product Line Report (Finance: Xiao Xiao)8.RMA inventory Scrap Costa) 计算公式：RMA inventory Scrap Cost ＝Qty*Material Unit Cost b) 数据源：备品备件报废清单（GSS: Joy Chen ）UTS Item Product Line Report (Finance: Xiao Xiao)9. Freight for returned the parts from fielda) 计算公式：Freight ＝退货运费＋CRC 维修运费 b) 数据源：CRC 维修运费（Finance ：Sharon Yang ）退货运费 (Logistic: Li Xiuchun) CRC 维修记录 (CRC: Y u JiaYin)10. MFG Production RMA Labor Costa) 计算公式：RMA Labor Cost ＝RMA Labor （Hrs ）*工时费 b) 数据源：CRC 维修运费（FTE ：Chen Ting ）11. Int'l COPQ Costa) 计算公式：int ’l Warranty Costb) 数据源：int ’l Warranty report (Bobby Forbes)12. Cost Savinga) 计算公式：累加各条记录金额 b) 数据源：Finance13. Cost Controla) 计算公式：累加各条记录金额 b) 数据源：Oracle 系统五， 可靠性A. MTBF∑====n i i gi)iπ(λN //λ：MTBF QEQUIP 111计算公式 B.C. 数据源：MIL-HDBK-217F, BOM六， RoHS Compliance Rate （by module ）数据源：Agile计算公式：RoHS Compliance Rate ＝符合RoHS 的模板数量/总的模板数量步骤如下：1）输入条件（product line ，description ，q-state ，P/N ，lifecycle ），从Agile 中导出2424开头的Module 清单；2）整理清单，剔除重复的（00与05/06同时存在的只计05/06，不计00结尾的）； 3）根据描述、Q 状态及P/N 结尾特征，筛选出符合与不符合的器件； 4）根据产品线统计各产品的符合比例；5）根据统计结果，完成统计报告供相关部门参考。