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sqa

Course: CS 4320, Fall 2009
School: CSU Mont. Bay
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Word Count: 233

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SOFTWARE QUALITY ASSURANCE (SQA) ================================ Reference: Software Engineering: A Practitioner's Approach by Pressman. SQA Process Goal: high-quality software. Software Quality: Conformance to 1. explicit functional and performance requirements. 2. explicit development standards. 3. implicit characteristics of professionally developed...

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SOFTWARE QUALITY ASSURANCE (SQA) ================================ Reference: Software Engineering: A Practitioner's Approach by Pressman. SQA Process Goal: high-quality software. Software Quality: Conformance to 1. explicit functional and performance requirements. 2. explicit development standards. 3. implicit characteristics of professionally developed software. example: easy to maintain SQA Process Activities: 1. quality management 2. effective software engineering technology (methods and tools) 3. formal technical reviews that are applied throughout the software process 4. multi-tiered testing strategies 5. control of software documentation and updates 6. procedures to assure compliance with standards 7. measurement and reporting mechanisms Quality Control (QC): Series of inspections, reviews, and tests used throughout the development cycle to insure that each work product meets the requirements. Includes a feedback loop to the process that created the work product. SQA Plan: Purpose Management (organization, responsibilities) tasks, Documentation Standards (conventions) Reviews /Milestones Requirements Review Design Reviews (preliminary, detail) Verification Review (does the product work correctly?) Validation Review (is it the right product?) Management Reviews Software Configuration Management Problem Reporting and Corrective Action Tools, Techniques, Methodologies Records Factors: Product Operations: Correctness, Reliability, Efficiency, Integrity, Usability Product Revisions: Maintainability, Flexibility, Testability Product Transition: Portability, Reusability, Interoperatibility Sofware Reviews: Goals: ...

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CSU Mont. Bay - CS - 4320
DestClassSeatMexicoBusWindowCanadaCoachAisleUSAFirst
CSU Mont. Bay - CS - 4320
SOFTWARE METRICS =Reference: Software Metrics: A Rigorous & Practical Approach by Fenton, Pfleeger Software Engineering: A Practitioner's Approach by Pressman. The Mythical Man-Month by B
CSU Mont. Bay - CS - 4320
import junit.framework.*;public class AllTests { public static Test suite() { TestSuite suite = new TestSuite(); suite.addTest(new Driver("testApplication"); return suite; } public static void main(String args[]) { Test allTes
CSU Mont. Bay - CS - 4311
Abstract Factory: Selected Code=java GUI 1 | +->int code = Integer.parseInt(args[0]); / 1: | +->AbstractFactory f = AbstractFactory.getFactory(code); / 2: | | | +->case 1: return sun; / 2.1: | +->Menu m = f.createMenu(); /
CSU Mont. Bay - CS - 4311
Connection Pool: Selected Code=java Client | +->ConnectionPool pool = ConnectionPool.getInstance(); / 1: | | | +->return instance; / 1.1: | +->Connection conn = pool.acquire("SuppDB"); / 2: | | | +->Connection conn
CSU Mont. Bay - CS - 3240
/* * This sample applet list all entries from the table dogs in the database * ALSO asks user for privilege to connect via sockets to non-web-server machine *// Import the JDBC classesimport java.sql.*;/ Import the java classes used in applet
CSU Mont. Bay - CS - 3240
/* * This sample shows how to list all entries from the table dogs in the database * * It uses the JDBC OCI8 driver. *// You need to import the java.sql package to use JDBCimport java.sql.*;/You need to import the math packageimport java.
CSU Mont. Bay - MCS - 4170
S 4170/6170 Due 1/26Read 2.1Remember quiz 2/2 on chapter 1 (plus strings and languages from chapter 0)1. Write NDFAs for these regular expressions (follow Lemma 1.55 or not, as you choose) ( 00 U 11)* U 10 (00)*(11)*(10)2.
CSU Mont. Bay - MCS - 4170
Assignment: due 2/16Quiz on context free languages: 2/261. Prove: If L is context free and L' is regular, then (L intersect L') is Context free [hint: see the proof for the union or intersection of two regular languages]2. Give a grammar w
CSU Mont. Bay - MCS - 4170
Due Tuesday 2/21, quiz over CFS's on Thursday 2/23Read: anything left in chapter 2, 3.1-3.31. In a grammar, a nonterminal is 'useless' if it never occurs in the production of a string in the language; if it does occur, it is 'productive'
CSU Mont. Bay - MCS - 4170
Due Thursday 2/9Read chapter 2.1-2.4 pp 128-1291. 2.4 a,b,c / left from last assignment2. a. Given this DFA, write an equivalent (a) right linear, and (b) left linear grammar.a bc (q0 is start, q1 and q3 are final)>q0 q
CSU Mont. Bay - MCS - 4170
Assignment Due Thurs 1/19Read Sipser 1.2-1.4Or Aho, Motwani, Ullman pp 37-81 (plus skim 1-36)1. Make the simplest nondeterministic FA (NDFA) that you can for these languages over {0,1} a. {w | w contains either substring 11 or substrin
CSU Mont. Bay - MCS - 4170
Due Tuesday 1/31, quiz Thursday 2/3Read chapter 2.1Sipser p. 90-91 1. 1.46 a,b 2. Borrowed from Kozen, Automata and Computability Which of the following sets are regular and which are not? Give justification (if yes, gi
CSU Mont. Bay - ST - 1000
Take a challenge with time; never let time idles away aimlessly.1Turning Data Into Informationtypes of data summary statistics distributions2What are Data?Any set of data contains information about some group of individuals. The informati
CSU Mont. Bay - ST - 1000
CSU Mont. Bay - ST - 3503
Minitab Notes for STAT 3503 Dept. of Statistics CSU HaywardUnit 10: A Nested Design10.1. The DataA drug company wants to investigate the uniformity of one of its products. It has 2 Sites at which the drug is manufactured. From each site three Ba
CSU Mont. Bay - ST - 3502
Normal Family of DistributionsStandardization, Z-scores Suppose two students take two different college entrance exams that test equivalent subject matter. Mary takes the ABC test and gets a score of 112. John takes the XYZ test and gets a score o
CSU Mont. Bay - ST - 6652
CSU Mont. Bay - ST - 4950
STAT 3900 Fall, 2006MIDTERMName:~ (print: firstlast)Covered topics: descriptive statistics, simple random sample, one-way anova Instructions: You may use your notes, and a calculator. Write your answers in the space provided. Total score is 2
CSU Mont. Bay - ST - 4950
Stat 3900 Fall 2006 Homework #1 Question 1 Note that the sample size on the homework sheet is 21, while that provided in lab is 25. The results are similar. 1) Answers and tables shown below. a) The percent of men in the sample is 52%.sex Cumulative
CSU Mont. Bay - ST - 1000
STAT 1000HOMEWORK # 4 Textbook: Mind on Statistics, 3rd Ed.Spring, 2008123
CSU Mont. Bay - ST - 3503
Minitab Notes for STAT 3503 Dept. of Statistics CSU HaywardUnit 8: A Mixed Two-Factor Design8.1. The DataWe use data quoted in Brownlee: Statistical Theory and Methodology in Science and Engineering, 2nd ed., page 502, on the rupture strength of
CSU Mont. Bay - ST - 3503
Minitab Notes for STAT 3503 Dept. of Statistics CSU HaywardUnit 9: Contrasts of Sample Means9.1. Definition of a ContrastIn designing an experiment with a balanced ANOVA model one often has in mind to estimate a linear combination = cT = i cii
CSU Mont. Bay - ST - 3502
STAT 3502Homework #7Fall, 20088.6123
CSU Mont. Bay - ST - 1000
STAT 1000HOMEWORK # 6 Textbook: Mind on StatisticsFall, 2008
CSU Mont. Bay - ST - 3502
STAT 3502 Statistical Inference ILecture InstructorFall 2006TTh 4:00-5:50 pm at Sc N215 Dr. Shenghua (Kelly) Fan Office: Sc N318 Tel: (510)885-3428 E-mail: kelly.fan@csueastbay.edu TTh 7:00-7:50 pm Office hours http:/www.sci.csueastbay.edu/~sfan
CSU Mont. Bay - ST - 4950
TTh 8:00 pm-9:50 pm at Sc N207 Prof. Shenghua (Kelly) Fan Office: Sc N318 Tel: (510)885-3428 E-mail: kelly.fan@csueastbay.edu TTh 7:00 pm-7:50 pm Office hours Class website www.sci.csueastbay.edu/~sfan __ Lecture/Lab InstructorPREREQUISITEFor Stat
CSU Mont. Bay - ST - 1000
CSU Mont. Bay - ST - 1000
STAT 1000HOMEWORK # 1 Textbook: Mind on Statistics, 3rd Ed.Fall, 200812345
CSU Mont. Bay - ST - 1000
STAT 1000HOMEWORK # 4 Textbook: Mind on StatisticsFall, 2008
CSU Mont. Bay - ST - 3502
STAT 3502Homework #6Fall, 200812
CSU Mont. Bay - ST - 1000
STAT 1000HOMEWORK # 3 Textbook: Mind on Statistics, 3rd EdFall, 200812
CSU Mont. Bay - ST - 3502
Chapter 8: Inferences about More Than Two Population Central Values8.2 An Analysis of Variance8.1a. Yes, the mean for Device A is considerably (relative to the standard deviations) smaller than the mean for Device D. b. Ho : A = B = C = D versus
CSU Mont. Bay - ST - 3502
Chapter 11: Linear Regression and Correlation11.2 Estimating Model Parameters11.1 A scatterplot of the data is given here:4045353025y20 10 515 51015 x202511.2a. y = 1.8 + 2.0(3) = 7.8 b. The equation is plot
CSU Mont. Bay - ST - 1000
Everyday is a new beginning in life. Every moment is a time for self vigilance.1Simple Linear RegressionScatterplot Regression Correlationequation2Example: Computer RepairA company markets and repairs small computers. How fast (Time) an e
CSU Mont. Bay - ST - 1000
Keep Life Simple!We live and work and dream, Each has his little scheme, Sometimes we laugh; sometimes we cry, And thus the days go by.1Random VariablesTypesof random variables Expected values Binomial and Normal distributions2What Is a R
CSU Mont. Bay - ST - 1000
If we live with a deep sense of gratitude, our life will be greatly embellished.1Hypothesis Test ILogicof hypothesis test Rejection region and p-value Test for one proportion2ExampleApharmaceutical company wants to be able to claim tha
CSU Mont. Bay - ST - 1000
Be humble in our attribute, be loving and varying in our attitude, that is the way to live in heaven.1ProbabilitySamplespace/event Independence Bayes rule2This lady has lost 10 games in a row on this slot machine. Would you play this slot
CSU Mont. Bay - ST - 3502
Ever yda y i s a new begi nni ng i n l i fe. Ever y moment i s a ti me for sel f vi gi l a nce.Multiple ComparisonsErrorrate of control Pairwise comparisons Comparisons to a control Linear contrastsMultiple Comparison ProceduresOnce we reject
CSU Mont. Bay - ST - 3502
L augh, and the worl d l aughs wi th you. Weep and you weep al one. ~Shakespear e~1Chapter 3: Data DescriptionTypes of data Graphical/Numerical summaries2What are Data? Anyset of data contains information about some group of individuals
CSU Mont. Bay - ST - 1000
Introduction to Statistics1What Is Statistics?Statistics is the science of collecting, organizing, and interpreting numerical facts, which we call data Examples of data: 1. High and low temperatures for this week in SF: San Francisco Weather For
CSU Mont. Bay - ST - 6652
Survival AnalysisIntroductionAbbreviated OutlineDescriptive overview of survival analysis Terminology and notation Goals of survival analysisSurvival analysis is a collection of statistical procedures for data analysis for which the outco
CSU Mont. Bay - ST - 1000
Happiness comes not from material wealth but less desire.1Means and Proportions as Random VariablesSamplingdistribution Normal curve approximation2DefinitionsAstatistic is a numerical summary of a sample. Its value may differ for differ
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I f you wa nt pea ce, you must fi r st ha ve pea ce of mi nd. T o ha ve pea ce of mi nd, you must fi r st a ct a ccor di ng to r ea son. Wi th r ea son, you wi l l ha ve pea ce of mi nd, a nd then the whol e fa mi l y wi l l be a t pea ce.1survival
CSU Mont. Bay - ST - 3502
Learn to let go. That is the key to happiness. ~Jack Kornfield1ProbabilitySection4.1-4.5 Basic terms and rules Conditional probability and independence Bayes' rule2This lady has lost 10 games in a row on this slot machine. Would you play t
CSU Mont. Bay - ST - 6652
T he gr ea test bl essi ng i n l i fe is i n gi vi ng a nd not ta ki ng.1survival analysisSurvival AnalysisNonparametric Estimation of Basic Quantities2survival analysisAbbreviated OutlineSurvival data are summarized through estimates o
CSU Mont. Bay - ST - 6652
Always be contented, be grateful, be understanding and be compassionate.1Survival AnalysisSurvival AnalysisSemiparametric Proportional Hazards Regression (Part I)2Survival AnalysisAbbreviated OutlineThe proportional hazards regression m
CSU Mont. Bay - ST - 4950
Joyful mood is a meritorious deed that cheers up people around you like the showering of cool spring breeze.1Applied Statistics Using SAS and SPSSTopic: Contrast and Non-parametric Test By Prof Kelly Fan, Cal State Univ, East Bay2ContrastCo
CSU Mont. Bay - ST - 4950
Happiness comes not from material wealth but less desire.1Applied Statistics Using SAS and SPSSTopic: Simple linear regression By Prof Kelly Fan, Cal State Univ, East Bay2Example: Computer RepairA company markets and repairs small computers
CSU Mont. Bay - ST - 4950
The future is a vain hope, the past is a distracting thought. Uphold our loving kindness at this instant, and be committed to our duties and responsibilities right now.1Applied Statistics Using SAS and SPSSTopic: Hypothesis Testing By Prof Kelly
CSU Mont. Bay - ST - 4950
If we live with a deep sense of gratitude, our life will be greatly embellished.1Applied Statistics Using SAS and SPSSTopic: Chi-square tests By Prof Kelly Fan, Cal. State Univ., East Bay2OutlineVariables must be categorical one: verify a
CSU Mont. Bay - ST - 1000
A heart fills with loving kindness is a likeable person indeed.1Estimating Proportions and MeansSampleestimates Confidence intervals Approximate confidence intervals for p and 2Review Unit: a person or object to be measured Populat
CSU Mont. Bay - ST - 3502
Always be mindful of the kindness and not the faults of others.1One-way Anova: Inferences about More than Two Population MeansModeland test for oneway anova Assumption checking Nonparamateric alternative2Analysis of Variance & One Factor D
CSU Mont. Bay - ST - 3502
When we free ourselves of desire, we will know serenity and freedom.1Inferences about Population MeansEstimation Statisticaland t test Sample size selectiontests- z test2EstimationTo estimate a numerical summary in population Point est
CSU Mont. Bay - ST - 3910
Be humble in our attribute, be loving and varying in our attitude, that is the way to live in heaven.Statistical Package UsageTopic: One Way ANOVA By Dr. Kelly Fan, Cal State Univ, East BayStatistical Tools vs. Variable TypesResponse (output) N
CSU Mont. Bay - ST - 3502
Since everything is a reflection of our minds, everything can be changed by our minds.1Inferences about Population VariancesThedistribution of s^2 Tests for equal variances2The Distribution of S^2Let Y1, Y2, ., Yn be a random sample f
CSU Mont. Bay - ST - 6652
BSTA6652Lecture InstructorSurvivalAnalysisWinter2009MW 4:00 am5:50 pm at SC N207 Prof. Shenghua Kelly Fan Office: SC N318 Tel: (510)885-3428 E-mail: kelly.fan@csueastbay.edu Office hours MW 2:30 pm4:00 pm Class website http:/www.sci.csueastbay
CSU Mont. Bay - ST - 6652
BSTA 6652 HW#1 Answer Key 01/21/09 Thanks to Paul LeEx. 2.2, 2.4, 2.6, 2.102.2 (a) Let X denote the time (days) to tumor development.The survival function of the Weibull distribution with = 2 and = 0.001 (using Table 2.2):The probability th
CSU Mont. Bay - ST - 3502
STAT3502/6304StatisticalInferenceIFall2008Lecture Instructor TTh 6:00 pm-7:50 pm at Sc S 213 Dr. Shenghua (Kelly) Fan Office: Sc N318 Tel: (510)885-3428 E-mail: kelly.fan@csueastbay.edu Office hours TTh 9:00 am-9:30 am and 5:20 pm-6:00 pm Class webs
CSU Mont. Bay - ST - 4950
ST3900/4950Covered materials: ST 3900 ST 4950HOMEWORK/LAB 5FALL, 2006Lessons 25, 30, 32, 33 Chapter 5 excluding Section E and Sections D, E of Chapter 7Due Nov 14, 2006_ Question 1 Three brands of tennis shoes are tested to see how many mont
CSU Mont. Bay - ST - 3502
1Statistical GraphicsHomework Problems (hand in 10/14 beginning of class):1. Install Minitab on your computer. Use each program to find mean, median, and standard deviation for the data in Problem 3.25 on page 77 of the text. Turn in one page of