index...

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Numbers 80-20 principle, 128. See also Pareto diagrams 99.999% availability, 360–361 A Abstract class, OO technology, 332 Acceptance testing (vendor-developed software), 302–304 Accuracy. See Reliability Active backlogs, 108 Active Design Reviews method, 180 Activity-based costs, process improvement, 462–464 Activity levels, process improvement, 462–466 Addition, interval scale and, 60 Alignment principle measuring value of process improvement, 447–448 overview of, 443–444 Alternative-form method, reliability testing, 75 Alternative hypotheses, 483 American National Standards Institute (ANSI), 86 Analysis-driven data collection, 472 Analysis phase, OO model, 29 Analysis, TQM and, 8 ANSI. See American National Standards Institute APAR. See Authorized program analysis report Applicability, reliability models, 219 Appraisal Requirements for CMMI (ARC), 439 AS/400, 141–143, 193–194 Assessment. See also Malcolm Baldrige assessment; Quality assessment; Software project assessment vs. audit, 414–415 internal assessments, 421–422 ISO standard for, 414 organizational-level, 416 process improvement and, 454 qualitative vs. quantitative, 455–456 report, 429–433 summary, products good enough to ship, 306–307 Attenuation, 76–77 Audits vs. assessments, 414–415 ISO 9000, 49 quality audits, 397 Authorized program analysis report (APAR), 139–140 Availability metrics, 359–374 99.999% availability, 360–361 availability compared with reliability, 363 defect rates and, 364–366 key factors in, 360 509 Index

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Page 1: Index [ptgmedia.pearsoncmg.com]ptgmedia.pearsoncmg.com/images/0201729156/index/kanindex.pdfCertification, ISO standard, 414 Changed source instructions (CSI), 91 Check sheets. See

Numbers80-20 principle, 128. See also Pareto diagrams99.999% availability, 360–361

AAbstract class, OO technology, 332Acceptance testing (vendor-developed

software), 302–304Accuracy. See ReliabilityActive backlogs, 108Active Design Reviews method, 180Activity-based costs, process improvement,

462–464Activity levels, process improvement, 462–466Addition, interval scale and, 60Alignment principle

measuring value of process improvement,447–448

overview of, 443–444Alternative-form method, reliability testing, 75Alternative hypotheses, 483American National Standards Institute

(ANSI), 86Analysis-driven data collection, 472Analysis phase, OO model, 29Analysis, TQM and, 8ANSI. See American National Standards

InstituteAPAR. See Authorized program analysis

report

Applicability, reliability models, 219Appraisal Requirements for CMMI

(ARC), 439AS/400, 141–143, 193–194Assessment. See also Malcolm

Baldrige assessment; Quality assessment; Software project assessment

vs. audit, 414–415internal assessments, 421–422ISO standard for, 414organizational-level, 416process improvement and, 454qualitative vs. quantitative, 455–456report, 429–433summary, products good enough to ship,

306–307Attenuation, 76–77Audits

vs. assessments, 414–415ISO 9000, 49quality audits, 397

Authorized program analysis report (APAR), 139–140

Availability metrics, 359–37499.999% availability, 360–361availability compared with reliability,

363defect rates and, 364–366key factors in, 360

509

Index

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Availability metrics (continued)outage cause analysis, 369–370outage data collection, 369, 371–372outage indicators, 366outage tracking forms, 367–368outages in-process, 372–373productivity and, 362references, 373–374reliability and, 362–364summary, 373system availability technologies, 363–364

Average method size, 334–335

BBack end of development process, 56–57

defect tracking model for, 269quality metrics and, 57reliability models and, 256

Backlog management index (BMI)control charts and, 151software maintenance and, 106–107

Backlogs, defect. See PTR arrival and backlogmodel

Baselinesprocess capability, 450process improvement and, 455–456

Basic measurespercentages, 63–65proportions, 62–63rates, 65–66ratios, 62six sigma, 66–70

Benchmarks, 455–456. See also BaselinesBeta program outages, 372–373Black box approach, software testing, 311Blitz testing, 255BMI. See Backlog management indexBOOTSTRAP method, 416Bugs, software, 4, 86Business applications, availability, 361

CCalendar-time data, 210, 215, 252Capability

management models and, 257reliability models and, 219

Capability Maturity Model (CMM), 39–44applications of, 40–41CMM-based methods, 417–418control charts and, 149defect rates and, 365

defect removal effectiveness (DRE) and,181–183

industry leadership and, 458–459maturity questionnaire, 424process improvement and, 437–438process maturity levels, 39–40six step cycle in, 417Software Engineering Institute (SEI) and, 9

Capability Maturity Model Integration(CMMI)

model based approach to processimprovement, 438

process maturity levels, 42–44, 438staged vs. continuous representations, 42,

44, 440–441Card and Glass model, 321–322CASE tools, 238, 484Causality, 79–82

correlation and, 79criteria for measuring, 80–81

Cause-and-effect diagramsdefined, 130fishbone diagram example (Grady and

Caswell), 152–153CBA IPI. See CMM-Based Appraisal for

Internal Process ImprovementCBO. See Coupling Between Object ClassesCDF. See Cumulative distribution functionCertification, ISO standard, 414Changed source instructions (CSI), 91Check sheets. See ChecklistsChecklists, 130–133

defined, 128effectiveness of, 130–131illustration of, 129inspection checklist, 238process compliance and, 449types of, 131–133

CHECKPOINT tool, SPR, 44CK (Chidamber and Kemerer) metrics suite,

337–343applying to two companies, 338–339combining with other metrics, 342list of metrics in, 337–338validation studies, 339–343

Class hierarchy, OO technology, 332–334Classes, OO technology, 332, 347Classification, scientific, 59Cleanroom methodology, 32–35

development team control and, 32–34front-end defect removal and, 180

510 Index

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Index 511

illustration of, 33overview of, 32stages of, 34–35statistical testing and, 34

Cleanroom Software Engineering, 32Cluster sampling, 379CMM. See Capability Maturity ModelCMM-Based Appraisal for Internal Process

Improvement (CBA IPI), 417–418, 438CMMI. See Capability Maturity Model

IntegrationCode development phase, 242Code inspections. See Design review/code

inspection (DR/CI)Code integration pattern, 242–245

early defect removal and, 245integration over time, 243middle of development process and, 269project-to-project comparisons, 244uses of, 244

Code reuse. See ReuseCommitment

management commitment, 474team commitment, 297

Companies, customer satisfaction with,388–389

Complexity metrics. See also Structuralcomplexity metrics

CK metrics suite, 336–337compared with reliability and quality

management models, 311cyclomatic complexity, 315–318Halstead’s software science, 314–315lines of code (LOC) metric, 312–313module design examples, 322–328references, 329structural metrics, 319–322summary, 328syntactic constructs for, 318–319system model, 320–322

Component quality analysis, 135–136,369–370

Component test (CT) phase, PTR backlogs,295

Componentsavailability, 362defined, 193testing, 17

Compression factor, reliability models,224–226

Computer science vs. software science, 314

Concepts, measurement theory, 58Concrete class, OO technology, 332Confidence intervals

reliability and, 203software development and, 482

Confidence level, survey samples and, 380–381“Conformance to requirements,” 2Construct validity, 71Content validity, 71–72Continuous representation (systems

engineering), CMMI, 42, 44, 440–441Control charts, 143–152

defect removal effectiveness, 151–152defects, 146–148defined, 130effective use of, 149–151illustration of, 129inspection effectiveness, 148–149process improvements and, 149as quantitative analysis tool, 402SPC and, 143–144, 481–482types of, 145–146uses of, 145

Control flow loops, 315–316, 318–319Correlation, 77–80

causality and, 79logical guidance for use of, 81metrics and, 76, 78–79module defect level with other metrics,

324–325outliers and, 80weak relationships and, 78

Cost effectiveness, defect removaleffectiveness (DRE) and, 177–180

Cost performance index (CPI), 152Coupling Between Object Classes (CBO), 338CPI. See Cost performance indexCPU utilization, 286–289, 301Criteria for model evaluation, 207, 218–219,

257Criterion-related validity, 71Critical problems, 124, 293, 401Crosby, P. B., 2, 7, 9CSI. See Changed source instructionsCultural aspects, TQM, 8Cumulative distribution function (CDF), 189,

208, 214CUPRIMDA, 381–382CUPRIMDSO, 4, 98Curvilinear model, size to defect rate,

312–313

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Customer-Centered Six Sigma (Naumann andHoisington), 69–70

Customer problems metric, 96–99problems per user month (PUM), 96–97relationship of defects and customer

satisfaction, 97–98, 99Customer programs. See Early customer

programs (ECP)Customer satisfaction, 375–395

companies and, 388–389customer problems metric and, 97–98data analysis, 381data collection, 376–377defects and customer problems and, 99histogram analysis, 137how much is enough, 390–392market share and, 390–391metrics, 98–100quality and, 4–5references, 393–394sample size, 379–381sampling methods, 377–379small organizations and, 392–393specific areas vs. overall satisfaction,

382–388summary, 393surveys, 98, 371, 376–381UPRIMD categories, 383–388

Customerscost of recruiting vs. cost of retaining, 375feedback from, 376–377as focus of TQM programs, 8perspective on defect rates, 92–93quality evaluation by, 3, 255

Cyclomatic complexity (McCabe), 315–318correlation with LOC, 317formula for, 315program testability and maintainability,

315–316scatter diagrams, 140–141uses of, 317–318

DData

balancing analysis and collection, 472complexity, 320–321gathering and testing, 57–58quality control and, 470–472software engineering. See Software

engineering datasummarization format, 407

Data collectioncustomer satisfaction surveys and, 376–377data quality and, 470methodology (Basili and Weiss), 118questionnaire, 423

Data quality, 203, 470–472, 475Defect arrival data, 223–224. See also PTR

arrival and backlog modeldistributing overtime, 226–229exponential model and, 209OO projects and, 349–350outcome indicators and, 299reliability growth models and, 254

Defect arrival ratemachine testing and, 101–102software testing and, 279–282

Defect arrivals curve, 282Defect attributes, orthogonal defect

classification (ODC), 268Defect backlog. See PTR arrival and backlog

modelDefect data, 119–123

documentation defects, 122–123inspection defects, 119interface defects, 119logic defects, 121

Defect densitycustomer problems metric and, 97–98customer satisfaction and, 99lines of code (LOC) and, 312product quality metric and, 86–87

Defect discovery periods, orthogonal defectclassification (ODC), 267

Defect injectionactivities, 164–165by phases, 165–171

Defect origin, 159, 166, 262–263Defect origin-where found matrix, 169–171Defect prevention. See also Defect prevention

processCMM model and, 41Rayleigh model and, 236

Defect prevention process (DPP), 35–39applying to waterfall model, 38IBM’s success with, 36–37illustration of, 37key elements of, 35–36steps in, 35, 131

Defect rates. See also Six sigmaavailability metrics and, 364–366defective parts per million (DPPM), 69

512 Index

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Index 513

machine testing and, 100–101mean time to failure (MTTF) and, 364OO quality and, 347–348by phase, 265–266process improvement and, 461software and, 66

Defect removalactivities, 164–165code integration pattern and, 245model evaluation and, 257patterns, 239–240phase-based, 103, 165–171Rayleigh model and, 236

Defect removal effectiveness (DRE), 159–185control charts and, 151–152cost effectiveness and, 177–180defect injection and removal activities,

164–165defect injection and removal by phases,

165–171formulas for measuring, 160–161literature review, 160–164overview of, 103–105, 159–160phase-based defect removal model, 172–174process maturity levels and, 181–183references, 184–185summary, 183–184two-phase model, 174–177

Defect removal model (DRM)cost effectiveness of, 177–180defect removal patterns and, 239–240examples of, 173overview of, 172–174two-phase model, 174–177

Defect triggers, orthogonal defectclassification (ODC), 267–268

Defective fixes, 110Defective parts per million (DPPM), 66–67Defects

backlog over time, 283–284by category (Pareto analysis), 133–134by component (Pareto analysis), 135control charts and, 146–148defined, 86distribution of, 295–296exponential model and, 209ODC classification, 267by OO class, 347relationship to failure, 87by severity (histogram analysis), 136by testing phases, 297

tracking and reporting, 258Defects per thousand sources lines of code

(KLOC), 66Definitions, 58Delayed S model, 215–216Delinquent fixes, 138–139Department of Defense (DoD), 365, 415Deployment, 448Depth of Inheritance Tree (DIT), 338Design

high-level design (HLD), 16, 258low-level design (LLD), 16, 258

Design phase, OO model, 28Design review/code inspection (DR/CI), 238,

255Developers, data provided by, 475Development. See also Object-oriented

developmentenvironment for OO projects, 352–353iterative model. See Iterative development

processmeasurement and, 484methodologies, 413practices for OO projects and, 355Rayleigh model and, 480–481SPC and, 481–483

Development cycleearly indicators, 400quality assessment and, 399

Development phasesintegration phase, 56multiple models for evaluating, 257quality indicators by, 401

Discrepancy reports (DR), 161–162Distribution of defects, 295–296DIT. See Depth of Inheritance Tree“Do it right the first time” principle, 192, 236DO WHILE statements, 319Documentation

customer satisfaction and, 384defects, 122–123process improvement and, 446–447

DoD. See Department of DefenseDowntime

availability metrics and, 360MTTR and, 363

DPP. See Defect prevention process (DPP)DPPM. See Defective parts per millionDR/CI (Design review/code inspection), 238,

255DR. See Discrepancy reports

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DRE. See Defect removal effectivenessDRM. See Defect removal modelDynamic reliability models, 187–188

EEarly customer programs (ECP), 18–19,

274–275, 305Early defect removal

code integration pattern and, 245model evaluation and, 257phase-based defect removal, 103Rayleigh model and, 236

Early detection percentage, 161–162ECP. See Early customer programsEducation, OO projects, 351–352Effort/defect matrix, 242, 259, 261Effort indicators

difficulty of establishing, 301effort/outcome model and, 298S curve and, 299

Effort/outcome model, 298–302effort indicators and outcome indicators,

298example scenarios, 299–300improvement actions, 301matrix structure of, 299quality management and, 241, 402test coverage and scoring, 301–302

Engineering. See Software engineeringEntry-Task Validation-Exit (ETVX) paradigm,

waterfall model, 14Error detection efficiency. See Defect removal

effectivenessErrors

defined, 86detection rate, 208injection rate, 239–240listing, 131measurement errors, 73–74

ETVX. See Entry-Task Validation-Exitparadigm, waterfall model

European Foundation for QualityManagement, 47

European Quality Award, 47Evaluation phase, quality assessment, 402–405

evaluation criteria, 405qualitative data, 403–405quantitative data, 402–403

Evolutionary prototyping, 21Execution-time data, 210Executive leadership, 8

Expert opinions, 403, 405Exponential model, 208–211

as case of Weibull family, 208ease of implementation, 209predictive validity, 211reliability and survival studies and, 208software reliability and, 208–209testing and, 209–210

Extreme Programming (XP ), 31–32Extreme-value distributions, 189

FFace-to-face interactions, 428Face-to-face interviews, 376Fact gathering

phase 1—project assessment, 422–423phase 2—project assessment, 425–426

Fagan’s inspection model, 180Failures

defined, 86factors in project failure, 426–427instantaneous failure rate, 208relationship to defects, 87

Fan-in/fan-out metrics (Yourdon andConstantine), 319–320

Fault containment. See Defect removaleffectiveness

Fault count modelsassumptions, 218reliability growth models and, 212

Faults, 86Field defects

arrival data, 228–229compared with tested defects, 236–237

Fishbone diagrams, 130, 152–153. See alsoCause-and-effect diagrams

“Fitness for use,” 2“Five 9’s” availability, 360–361Fix backlog metric, 106–107Fix quality, 109–110Fix response time metric, 107–108Flow control loops, 316–317, 319–320FPs (Function points), 56. See also Function

point metricsFrequency bars. See Pareto diagramsFrequency of outages, 360Front end of development process, 56–57,

269Function point metrics, 93–96

background and overview of, 456compared with LOC counts, 93–94

514 Index

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Index 515

example defect rates, 96issues with, 95measuring process improvements at

activity levels, 462–466OO metrics and, 334opportunities for error (OFE) and, 93

Function points. See FPFunctional defects, 267Functional verification test (FVT), 422–423Functions, 93FURPS (Hewlett-Packard customer

satisfaction), 4, 98

GGoal/Questions/Metric paradigm (Basili and

Weiss), 110, 472Goel generalized nonhomogeneous Poisson

process model, 214Goel-Okumoto (GO) Imperfect Debugging

model, 213, 217Goel-Okumoto Nonhomogeneous Poisson

Process (NHPP) model, 213–215Goodness-of-fit test (Kolmogorov-Smirnov),

222GQM. See Goal/Questions/Metric paradigm

(Basili and Weiss)Graphic methods, modeling and, 253

HHalstead’s software science, 314–315

formulas for, 314–315impact on software measurement, 314–315primitive measures of, 314

Hangseffort/outcome model and, 301software testing and, 289–291

Hazard rate, 208Hewlett-Packard

defects by category, 134fix responsiveness, 108FURPS customer satisfaction, 98software metrics programs, 115–116TQC program, 7

High availability. See also Availability metrics

defect rates and, 365importance of, 359reliability and, 362

High-level design (HLD), 16, 258Histograms

defined, 129

examples of, 136–138illustration of, 129as quantitative analysis tools, 402

HLD. See High-level designHuman cultural aspects, TQM, 8Hypotheses

formulating and testing, 57statistical methods and, 483

II Know It When I See It (Guaspari), 3IBM

eServer iSeries, 273Market Driven Quality, 8Object Oriented Technology Council

(OOTC), 336OS/2 and IDP, 26–27programming architecture, 14Sydney Olympics (2000) project,

302–303IBM Federal Systems Division, 191, 201IBM Houston, 161IBM Owego, 25IBM Rochester

OO projects of, 336–337software metrics programs, 116–117,

273–276IBM Rochester software testing, 273–276

assigning scores to test cases, 275defect tracking, 279process overview, 273reliability metric, 291showstopper parameter (critical problems),

293system stress tests, 286–287testing phases, 274

IDP. See Iterative development processIE. See Inspection effectivenessIF-THEN-ELSE statements, 316, 318–319IFPUG. See International Function Point

Users GroupImplementation phase, OO model, 28Improvement actions. See also Process

improvementmodule design and, 326–328by project, 432–433project assessment and, 427–428software testing and, 295–297

In-process availability metrics, 372–373In-process quality assessment. See Quality

assessment

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In-process quality metrics, 100–105defect arrivals, 101–102defect density, 100–101defect rate by phase, 265–266defect removal effectiveness, 103–105effort/outcome model and, 241inspection reports, 258–263inspection scoring questionnaire, 261overview of, 100phase-based defect removal, 103Rayleigh model and, 237, 258–260unit test coverage and defect report,

264–265In-process software testing metrics, 271–309

acceptance testing (vendor-developedsoftware), 302–304

CPU utilization, 286–289defect arrivals, 279–282defect backlog, 283–284effort/outcome model and, 298–302implementation recommendations, 294–295improvement actions and, 295–297mean time to unplanned IPLs, 291–293product preparedness for shipping, 304–307product size over time, 285quality management and, 297–298references, 309showstopper parameter (critical problems),

293summary, 308–309system crashes and hangs, 289–291test progress S curve, 272–279

Index of variation, 71Industry leadership factors, 458–459Inflection S models, 215–216Information flow metric, 320–321Infrastructure, process improvement stages,

457–458Inheritance, OO technology, 332Inheritance tree depth, 334Initial program loads (IPLs), 289–293Injection rate, errors, 239–240Inspection checklist, 238Inspection defects, 119, 317Inspection effectiveness (IE), 148–149, 167,

240–241Inspection effort, 240–242, 258–262, 269,

481Inspection model (Fagan), 180Inspection reports, 258–263

defect origin and type, 263

effort and defect rates, 260effort/defect matrix, 259

Inspection scoring checklist, 269Inspection scoring questionnaire, 261–262Instance variables, OO technology, 332Instantaneous failure rate, 208Instruction statements. See Lines of code

(LOC)Integrated product and process development

(IPPD), 42Integration phase, development process,

56Interface defects, 119, 262, 312Intermodule complexity, 321–322Internal assessments, 421–422Internal consistency method, 75International Function Point Users Group

(IFPUG), 95, 456, 462International Organization for Standardization

(ISO), 47, 414International Software Quality Exchange

(ISQE), 471Interval scale, 60–61Interviews, qualitative data and, 400, 403Intramodule complexity, 321–322IPL tracking tools, 290IPLs. See Initial program loadsIPPD. See Integrated product and process

developmentIshikawa’s seven basic tools. See Quality toolsISO 9000, 47–51

audits, 49changes to, 51compared with Malcolm Baldrige

Assessment, 50document control requirements of, 48elements of, 47–48software metrics requirements, 49

ISO. See International Organization forStandardization

ISQE. See International Software QualityExchange

Iterative development process (IDP), 24–27IBM OS/2 development as example of,

26–27illustration of, 25overview of, 24–25steps in, 26

Iterative enhancement (IE). See Iterativedevelopment process

Iterative model, 24–27

516 Index

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Index 517

JJelinski-Moranda (J-M) model, 212–213,

216–217Joint application design (JAD), 457Juran, J. M., 2, 5

KKey process areas (KPAs), 40KLOC (defects per thousand sources lines

of code), 66Kolmogorov-Smirnov goodness-of-fit test,

222

LLack of Cohesion on Methods (LCOM), 338Languages. See Programming languagesLeadership

project teams and, 428TQM and, 8

Lean Enterprise Management, 10Least-squares

correlation, 78PTR arrival and backlog model and, 252

Library of generalized components (LGC),30

Library of potentially reusable components(LPRC), 30

Life cycle, software, 191Life of product (LOP), 88Likert scale, 56–57Lines of code (LOC)

complexity metrics and, 312correlation with cyclomatic complexity

(McCabe), 316–318defects relative to, 88–92defined, 88example defect rates, 91–92function points and, 93–94, 456measurement theory and, 56OO metrics and, 334tracking over time, 285variations in the use of, 88–89

Literature reviewdefect removal effectiveness (DRE),

160–164project assessment, 426–427

Littlewood (LW) models, 213, 217Littlewood nonhomogeneous Poisson process

(LNHPP) model, 213LLD. See Low-level designLOC. See Lines of code

Logic defects, 121Lonenz metrics and rules of thumb, 334–336Loops, 316–319LOP. See Life of productLow-level design (LLD), 16, 258LPRC. See Library of potentially reusable

componentsLW. See Littlewood models

MM-O (Musa-Okumoto) logarithmic Poisson

execution time model, 215Machine testing, 100–101Mailed questionnaire, 376, 394Malcolm Baldrige assessment, 45–47

assessment categories, 45award criteria, 46ISO 9000 and, 49–50purpose of, 46used by US and European companies, 46–47value of feedback from, 46

Malcolm Baldrige National Quality Award(MBNQA), 7, 45

Management, projects. See Projectmanagement

Management, quality. See Qualitymanagement models; Total qualitymanagement

Management technologies, 456–457Managerial variables, CK metrics, 340–341Manpower buildup index (MBI), 200Margin of error, sample size, 379–380Mark II function point, 95, 462Market Driven Quality, IBM, 8Market share, customer satisfaction and,

390–391Mathematical operations

interval scale, 60ratio scale, 61

Maturity. See also Capability Maturity Model(CMM); Capability Maturity ModelIntegration (CMMI); Process maturity

project assessment and, 416software evaluation based on, 415

MBI. See Manpower buildup indexMBNQA. See Malcolm Baldrige National

Quality AwardMcCabe’s complexity index. See Cyclomatic

complexityMean time between failure (MTBF). See

Mean time to failure (MTTF)

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Mean time to failure (MTTF)cleanroom methodology and, 34defect rates and, 364as quality metric, 86–87as reliability metric, 291, 362

Mean time to repair (MTTR), 363, 366Mean time to unplanned IPLs (MTI),

291–293example of, 292–293formula for, 291–292used by IBM Rochester as reliability

measure, 291Means, 323Measurement. See also Metrics

data quality control and, 470–472errors, 73–74future of, 484references, 485software quality and, 472–475SPC and, 481–483state of art in, 484TQM and, 8

Measurement theory, 55–83attenuation, 76–77causality, 80–82concepts and definitions and, 58correlation, 77–80errors, 73–74example application of, 56–58interval and ratio scales, 60–61nominal scale, 59operational definitions, 58–59ordinal scale, 59percentages, 63–65proportions, 62–63rates, 65–66ratios, 62references, 83reliability, 70–73, 75–76scientific method and, 55–56six sigma and, 66–70summary, 82–83validity, 71–72

Messages, OO technology, 332Methods, OO technology, 332Metrics. See also Measurement

availability. See Availability metricsback end quality and, 57Capability Maturity Model (CMM), 41CK. See CK metrics suitecomplexity. See Complexity metrics

correlation and, 76function points. See Function point metricsmodule design. See Module design metricsOO projects and, 348in process quality. See In-process quality

metricsproduct quality. See Product quality

metricsproductivity. See Productivity metricssoftware maintenance. See Software

maintenance metricssoftware programs and, 472–475software quality. See Software quality

metricssoftware testing. See In-process software

testing metricsstructural complexity. See Structural

complexity metricsteam commitment and, 297

Metrics programsHewlett-Packard, 115–116IBM Rochester, 116–117Motorola, 110–115

Middle of development process, codeintegration pattern, 269

Models, 475–481. See also by individual type

criteria for evaluating, 479deficiencies in application of, 477–478development process and, 480empirical validity and, 479list of, 475–476neural network computing technology

and, 478–479probability assumptions and, 478professional’s use of, 476–477steps in modeling process, 220–224types of, 476

Module design metrics, 322–328. See alsoComplexity metrics

correlation between defect level and other metrics, 323–324

defect levels, 324–325defect rate, 325–326identifying improvement actions, 326–327list of metrics used, 323means and standard deviations of variables,

324measuring defect level among program

modules, 323–324Modules. See Program modules

518 Index

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Index 519

MotorolaQuality Policy for Software Development

(QPSD), 110–114Six Sigma Strategy, 7–8, 66–67Software Engineering Process Group

(SEPG), 110–114software metrics programs, 110–115

MTBF (mean time between failure). See Meantime to failure

MTI. See Mean time to unplanned IPLsMTTF. See Mean time to failureMTTR. See Mean time to repairMulti-inspector phase, 180Multiple regression model, 326–327Multivariate methods, 402Musa-Okumoto (M-O) logarithmic Poisson

execution time model, 215

NNASA, Software Assurance Technology

Center (SATC), 342NASA Software Engineering Laboratory, 118NEC Switching Systems Division, 103Net satisfaction index (NSI), 99–100Neural network computing technology,

478–479NOC. See Number of Children of a ClassNominal scale, 59Nonhomogeneous Poisson Process (NHPP)

model, 213–215Nonlinear regression, 195NSI. See Net satisfaction indexNull hypotheses, 483Number of Children of a Class (NOC), 338

OObject-oriented analysis (OOA), 331Object-oriented design (OOD), 331Object-oriented (OO) development, 27–32

Branson and Hernes methodology for, 26–29design and code reuse and, 29–30Extreme Programming (XP ) and, 31–32overview of, 27phases of, 27–29steps in, 28–29Unified Software Development Process and,

30–31Object-oriented programming (OOP), 331Object-oriented projects, 331–358

CK metrics, 337–343education and skills required, 351–352

examples of, 336–337Lonenz metrics and rules of thumb,

334–336metrics applicable to, 348OO concepts and constructs, 331–334OO metrics, 342–343performance and, 355productivity and, 343–347project management for, 353–354quality and development practices and,

355quality management, 347–351references, 357reusable classes and, 354–355summary, 356–357tools and development environment for,

352–353Object Oriented Technology Council (OOTC),

336Objects, OO technology, 332ODC. See Orthogonal defect classificationOFE. See Opportunities for errorOO development. See Object-oriented

developmentOOA. See Object-oriented analysisOOD. See Object-oriented designOOP. See Object-oriented programmingOOTC. See Object Oriented Technology

CouncilOperational definitions, 58–59Opportunities for error (OFE), 87, 93Ordinal scale, 59Organizational-level

assessment, 416management commitment at, 474

Organizations, 416Orthogonal defect classification (ODC),

266–269based on defect cause analysis, 266defect attributes, 268defect discovery periods, 267defect triggers, 267–268defect types and, 267

OS/2, 26Outage duration. See Mean time to repairOutages

analyzing causes, 369–370data collection methods, 369, 371–372duration of, 360frequency of, 360incidents and downtime, 370

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Outages (continued)indicators derived from outage data, 366tracking forms for, 367–368

Outcome indicators. See also Effort/outcomemodel

commonly available, 301defect arrival patterns and, 299effort/outcome model and, 298

Outlierscorrelation and, 80PTR arrival and backlog model, 250–251

Overall satisfaction, 382–388

Pp charts, 145Pareto diagrams, 133–136

defects by component, 136defined, 128examples of, 134–135illustration of, 129quantitative analysis tools, 402return on investment and, 133

PCE. See Phase containment effectivenessPDCE. See Phase defect containment

effectivenessPDF. See Probability density functionPDPC. See Process decision program chartPearson correlation coefficient

assumption of linearity, 78complexity metrics and, 323Rayleigh model and, 194

Peer reviewsprocess compliance and, 449project assessment, 423software development and, 414

Percent delinquent fixes, 108–109, 138–139Percentages, 63–65performance, OO projects, 355Personal Software Process (PSP), 42Phase-based defect removal model (DRM).

See Defect removal modelPhase containment effectiveness (PCE),

163–164, 169Phase defect containment effectiveness

(PDCE), 170Phase effectiveness, 104–405Plan-Do-Check-Act, 8–9Platform availability. See Availability metricsPoisson distribution, 145Polynomial model, 250, 476Precision. See Validity

Predictive validity, 203, 218Preparation phase, project assessment,

421–422, 425–426Preparation phase, quality assessment

qualitative data, 400–402quantitative data, 399–400

Probability assumption, 478Probability density function (PDF), 189–190,

208Probability sampling methods, 377–379

cluster sampling, 379simple random sampling, 377–378stratified sampling, 378–379systematic sampling, 377–378

Problem tracking reports (PTRs), 221,279–282. See also PTR arrival andbacklog model; PTR submodel

Problems per user month (PUM), 96–97Process adoption, 448–449Process assessment, 455–456Process capability

levels of, 440measuring, 440

Process compliance, 449–450Process decision program chart (PDPC), 154Process improvement, 453–467

activity levels and, 462–466alignment principle, 443–444CMMI staged vs. continuous

representations, 440–441day by day, 450–451defect rates and, 461economics of, 459–461maturity assessment models, 438–440measuring, 447–448process adoption, 448–449process capability levels, 440process compliance, 449–450process documentation, 446–447process maturity levels, 438productivity and, 461references, 452, 467schedules and, 461six stage program, 454–459summary, 451, 466–467teams, 441–443time to market goals and, 444–445TQM and, 8

Process maturity, 437–452. See alsoCapability Maturity Model (CMM)

assessment models, 438–440

520 Index

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Index 521

compared with project assessment, 415–417framework and quality standards, 39levels of, 438measuring, 438–440

Processes, process improvement stages and,457

Product levelvs. module level, 311reliability, 188

Product qualityreliability and defect rate as measure of,

364software quality and, 3–4vs. total quality management, 375

Product quality metrics, 86–100customer problems metric, 96–98customer satisfaction metric, 98–100customer’s perspective, 92–93defect density metric, 87–88defect density vs. mean time to failure,

86–87function points and, 93–96intrinsic product quality and customer

satisfaction, 86lines of code (LOC) metric, 88–92

Product size over time, 285Productivity metrics, 343–347

availability metrics and, 362examples, 343–345limitations of, 345person-days per class, 343–344process improvement and, 461two-dimensional vs. three- or four-

dimensional measures, 346–347units of measurement for, 343

Products, good enough to ship, 304–307indicators or metrics for, 304–305negative metrics and, 305product type and, 304quality assessment summary, 306–307

Program length formula, 82Program modules. See also Module design

metricscomplexity metrics and, 311module design examples, 322–328

Program size, 312Program temporary fix (PTF) checklist,

131–133Programming languages

formal specification languages, 20fourth generation languages, 21

UML (Unified Modeling Language), 30,457

variations in LOC, 89Programming Productivity (Jones), 88–89Project assessment. See Software project

assessmentProject charters, 421–422Project closeout plans, 422Project level, vs. module level, 311Project management

improvement actions and, 427in-process quality assessment and,

399OO projects, 353–354

Proportions, 62–63Prototyping

OO development and, 354risk-driven approach and, 23

Prototyping model, 19–21evolutionary prototyping, 21factors in quick turnaround of prototypes,

20–21overview of, 19rapid throwaway prototyping, 21steps in, 20

Pseudo-control charts. See Control chartsPSP. See Personal Software ProcessPTF (program temporary fix) checklist,

131–133PTR arrival and backlog model, 249–253

applications of, 249–250backlogs over time, 283–284component test (CT) phase and, 295contrasted with exponential model, 250defect arrivals over time, 279–282outliers and, 251–252predictability of arrivals with, 252–253predictor variables, 250

PTR submodel, 245–249deriving model curve, 247–248not applicable for projection, 248testing defect tracking, 246using in conjunction with other models, 249variables of, 246

PTRs. See Problem tracking reportsPUM. See Problems per user month

QQIP. See Quality improvement programQPSD. See Quality Policy for Software

Development

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Qualitative data, 400, 400–405, 402–403,405, 447

Quality. See also Software qualityambiguity regarding, 1of assumptions, 219audits, 397of conformance, 3customer satisfaction as validation of,

375of design, 3expense and, 2formula (conformance to customers’

requirements), 4, 5improvement strategies, 238in-process assessment. See In-process

quality assessmentmetrics and, 297–298OO projects and, 355popular view of, 1–2professional view of, 2–3projections with Rayleigh model, 237six sigma as measure of, 66–70

Quality assessment, 397–411assessment ratings over time, 408evaluation phase, 402–405overview of, 397–399parameters of, 406preparation phase, 399–402products, good enough to ship, 306–307recommendation phase, 408–410references, 411scale, 407summarization phase, 406–408summary, 410

Quality Improvement Paradigm/ExperienceFactory Organization, 9

Quality improvement program (QIP), 254–255Quality management models, 235–270

code integration pattern, 242–245compared with complexity metrics, 311evaluating, 257in-process metrics and reports, 258–266OO projects and, 347–351orthogonal defect classification, 266–269PTR arrival and backlog model, 249–253PTR submodel, 245–249Rayleigh model, 236–242references, 270reliability growth models, 254–257summary, 270

Quality metrics. See In-process quality metrics

Quality of measurementattenuation, 76–77errors, 73–74reliability, 70–73, 75–76validity, 71–72

Quality parameters, 5Quality Policy for Software Development

(QPSD), 110–114Quality tools, 127–158

cause-and-effect diagrams, 152–153checklists, 130–133control charts, 143–152histograms, 136–138new, 154overview of, 127–130Pareto analysis, 133–136references, 158relations diagrams, 154–156running charts, 138–140scatter diagrams, 140–143summary, 156–157

Quantitative analysis tools, 402Quantitative data

quality assessment evaluation phase,402–403

quality assessment preparation phase,399–400

QuestionnairesCMM maturity questionnaire, 424customer feedback and, 376–377project assessment and, 423–425, 487–508

RR charts, 145RAISE system tests, IBM Rochester, 286–288Random errors, 73, 75Random sampling, 377–378Rank-order correlation, 78Rapid throwaway prototyping, 21Rates, 65–66Ratio scale, 60–61Ratios, 62Rayleigh model, 189–206

assumptions, 192–195development process and, 480–481illustration of, 192implementation of, 195–203in-process quality metrics and, 258–260references, 206reliability and validity and, 203–205software life cycle and, 191

522 Index

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Index 523

summary, 205underestimation and, 204Weibull distribution and, 189–190

Rayleigh model, quality management, 236–242defect prevention and early removal, 236error injection rate and, 239–240inspection process and, 240–242organization size and, 269quality improvement strategies and, 238quality projections, 237relationship between testing and field defect

rates, 236–237Real-time delinquency index, 109Reboots. See Initial program loadsRecommendation phase, quality assessment

recommendations, 408–409risk mitigation and, 409–410

Recommendations, software projectassessment, 428–429

Referencesavailability metrics, 373–374complexity metrics, 330customer satisfaction, 393–394defect removal effectiveness, 184–185in-process quality assessment, 411in-process software testing, 309measurement, 485measurement theory, 83object-oriented projects, 357process improvement, 452, 467quality management, 270quality tools, 158Rayleigh model, 206reliability growth models, 231–233software development process models,

52–54software project assessment, 435software quality, 11–12

Relations diagrams, 154–156Relative cost, 118, 183Reliability, 70–76. See also Rayleigh model

assessing, 75–76availability and, 362–364defined, 70–71measurement errors and, 73–74measurement quality and, 73operational definition of, 362random errors and, 75technologies for improving, 363–364validity and, 72, 203–205

Reliability growth models, 207–233

assumptions, 216–218compression factor, 224–226defects over time, 226–229evaluating, 218–219exponential model and, 208–211Goel-Okumoto Imperfect Debugging

model, 213Goel-Okumoto Nonhomogeneous Poisson

Process (NHPP) model, 213–215Jelinski-Moranda (J-M) model, 212–213Littlewood models, 213modeling process, 220–224Musa-Okumoto logarithmic Poisson

execution time model, 215overview of, 207, 211–212references, 231–233S models, 215–216summary, 229–231

Reliability growth models, qualitymanagement, 254–257

advantages and applications of, 256defect arrival patterns, 254quality improvement program, 254–255

Reliability models. See also Rayleigh modelcompared with complexity metrics, 311deficiencies in application of, 477–478for small organizations, 230–231static and dynamic, 187–188

Removal efficiency, 96, 160–161, 402, 443,465

Remus-Zilles, 174, 177–178Reports, assessment, 429–433Reports, in-process

defect rate by phase, 265–266inspection, 258–263inspection scoring questionnaire and, 261severity distribution of test defects, 266test defect origin, 266unit test defects, 264–265unit test defects by phase, 265–266

Response for a Class (RFC), 338Response time, software maintenance, 107–108Results, software project assessment, 428–429Return on investment (ROI), 133Reuse

analyzing with scatter diagrams, 141–143design and code, 29–30list of reusable artifacts, 458OO projects and, 354–355reusable software parts, 20

RFC. See Response for a Class

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Rigorous implementation, 56Risk analysis, spiral development model and, 23Risk exposure rates, 65Risk mitigation, 409–410Rules of thumb (Lorenz), 334–336Run charts, 138–140

defined, 130illustration of, 129percent of delinquent fixes, 138–139tracking cumulative parameters, 140uses of, 138

SS curve, 140, 272–279, 299S models, 145, 215–216Sample size, customer surveys, 379–381

absolute size vs. relative size, 380–381margin of error and, 379–380

Sampling methods, 377–379cluster sampling, 379simple random sampling, 377–378stratified sampling, 378–379Systematic sampling, 377–378

SATC. See Software Assurance TechnologyCenter

Satisfaction scale, 381, 383. See also Customersatisfaction

Scales of measurementhierarchical nature of, 61interval, 60–61nominal, 59ordinal, 59

SCAMPI. See Standard CMMI AppraisalMethod for Process Improvement

Scatter diagrams, 140–143analyzing nonlinear relationships, 78defined, 129–130illustration of, 129McCabe’s complexity index and, 140–141,

324–325as quantitative analysis tools, 402relationship of reuse to defects, 141–143uses of, 140

SCE. See Software Capability EvaluationSchedule performance index (SPI), 152Scheduled uptime, 360Schedules, process improvement and, 461Scientific method, 55–56Scope creep, 285Scope of project assessment, 413, 415–416SEI. See Software Engineering Institute

SELECT statements, 319SEPG. See Software Engineering Process

GroupServer availability, 360–361Service calls, outage data and, 369SETT. See Software Error Tracking ToolSeven basic quality tools. See Quality toolsSeverity distribution of test defects report, 266Shipped source instructions (SSI), 91Showstopper parameter (critical problems), 293Significance tests, 483Simple random sampling, 377–378Simplicity, reliability models and, 219Single-inspector phase, 180Six sigma, 66–70

centered vs. shifted, 69implications for process improvement and

variation reduction, 69–70as industry standard, 66Motorola, 7–8standard deviation and, 66–67

Six stage program, process improvementoverview, 454Stage 0: Software Process Assessment and

Baseline, 455–456Stage 1: Focus on Management

Technologies, 456–457Stage 2: Focus on Software Processes and

the Methodologies, 457Stage 3: Focus on New Tools and

Approaches, 457Stage 4: Focus on Infrastructure and

Specialization, 457–458Stage 5: Focus on Reusability, 458Stage 6: Focus on Industry Leadership,

458–459Skills

metrics expertise, 474–475OO projects, 351–352skill-building incentives, 446–447

SLIM. See Software Life-cycle Model toolSmall organizations

customer satisfaction and, 392–393defect removal effectiveness and, 182–183object-oriented metrics and, 356quality management for, 269reliability modeling for, 230–231software testing recommendations, 308

Small teamscomplexity metrics and, 327–328OO projects and, 356

524 Index

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Index 525

Softwarecomplexity. See Complexity metricscontrol charts and, 149development models. See Software

development process modelsgetting started with metrics program for,

472–475life cycle, 191maintenance metrics. See Software

maintenance metricsmeasurement. See Measurementprocess improvement. See Process maturityproductivity. See Productivity metricsproject assessment. See Software project

assessmentquality management. See Quality

management modelsquality metrics. See Software quality

metrics; Software quality metricsquality models. See Modelsreliability. See Reliabilitystress tests, 286–287testing metrics. See In-process software

testing metricsSoftware Assessments, Benchmarks, and Best

Practices (Jones), 96Software Assurance Technology Center

(SATC), 342Software Capability Evaluation (SCE), 438Software development process models,

13–54cleanroom methodology, 32–35defect prevention process, 35–39ISO 9000, 47–51iterative model, 24–27Malcolm Baldrige assessment, 45–47object-oriented approach, 27–32process maturity framework and quality

standards, 39prototyping approach, 19–21references, 52–54SEI maturity model, 39–44spiral model, 21–24SPR assessment method, 44–45summary, 51–52waterfall model, 14–19

Software engineeringCMMI model, 440software development and, 470software quality and, 475–481techniques and tools, 484

Software engineering data, 117–123challenge of collecting, 117collection methodology, 118defect data and, 119–123expense of collecting, 118

Software Engineering Institute (SEI)assessment vs. capability evaluations, 415CBA IPI, 417–418CMM, 9, 39–44DPP and, 39lack of solid data on process improvements,

453rate of software flaws, 365SCAMPI assessors, 439

Software Engineering Metrics and Models(Conte), 88

Software Engineering Process Group (SEPG)alignment principle, 443–444capability baseline, 450measuring process improvement, 447–448monitoring process adoption, 448–449Motorola, 114–115not focusing only on maturity level,

442–443as process improvement team, 441reliable goals and, 445

Software Error Estimation Reporter (STEER),201–203

Software Error Tracking Tool (SETT), 222Software Life-cycle Model (SLIM) tool, 200Software maintenance metrics, 105–110

fix backlog and backlog management index,106–107

fix quality, 109–110fix response time and responsiveness,

107–108overview of, 105percent delinquent fixes, 108–109

Software Productivity Research, Inc. (SPR),44–45, 454. See also SPR assessmentmethod

Software project assessment, 413–436activities by phases of, 433–434assessment report, 429–433audits vs. assessments, 414–415characteristics of approach to, 420–421CMM-based method, 417–418facts gathering phase 1, 422–423facts gathering phase 2, 425–426improvements for, 426–428preparation phase, 421–422

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Software project assessment (continued)project assessment vs. process maturity

assessment, 415–417questionnaire for, 423–425references, 435results and recommendations, 428–429scope of, 413SPR method, 419–420summary, 434–435summary report, 433

Software quality, 1–12customer’s role in, 3–4overview of, 4–7popular view of, 1–2professional view of, 2–3references, 11–12summary, 10TQM and, 7–10

Software Quality and Productivity Analysis(SQPA), 7

Software Quality Assurance (SQA) group,449

Software quality engineering, 475–481Software quality metrics

data collection, 117–123Hewlett-Packard program, 115–116IBM Rochester program, 116–117Motorola program, 110–114overview of, 85–86in process quality. See In-process quality

metricsproduct quality. See Product quality metricsreferences, 125–126software maintenance. See Software

maintenance metricssummary, 123–125

Space Shuttle software project, 161, 194–195,238

SPC. See Statistical process controlSpearman’s rank-order correlation, 78, 194SPI. See Schedule performance indexSpiral development model, 21–24

advantages/disadvantages of, 23–24illustration of, 22risk analysis and risk-driven basis of, 23TRW Software Productivity System, 21

Split-halves method, 75SPR assessment method, 44–45

assessment themes, 45five-point scale of, 44process maturity and, 416

questionnaire topics, 44–45steps in, 419–420

SPR. See Software Productivity Research, Inc.Spurious relationships, causality, 81SQA. See Software Quality Assurance groupSQC. See Statistical quality controlSQPA. See Software Quality and Productivity

AnalysisSSI. See Shipped source instructionsStaged representation (software engineering),

CMMI, 42, 44, 440–441Standard CMMI Appraisal Method for

Process Improvement (SCAMPI), 415,418, 438–439

Standard deviationmodule design metrics and, 324sample size and, 380–381six sigma measures and, 66–70

Standards, ISO 9000, 47–51Static measures vs. dynamic, 65Static reliability models, 187–188Statistical methods

null hypotheses and alternative hypothesesand, 483

software development and, 482Statistical process control (SPC), 143–144,

481–483Statistical quality control (SQC), 481–483STEER. See Software Error Estimation

ReporterStratified sampling, 378–379Stress testing, 286, 301Structural complexity metrics, 319–322

defined (Henry and Kafura), 319–320fan-in/fan-out metrics (Yourdon and

Constantine), 319–320interaction between modules and,

320system complexity model (Card and Glass),

320–322Subtraction, interval scale and, 60Success of projects, factors in, 426–427Summarization phase, quality assessment

overall assessment, 406–408strategy for, 406

Summary report, project assessment, 433Supplier quality, 390Surveys, customer satisfaction, 98, 371,

376–381Survival studies, exponential model and, 208SVT. See System verification test

526 Index

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Index 527

Sydney Olympics (2000), acceptance testingand, 302

Syntactic constructs, complexity metrics,318–319

System availability. See Availability metricsSystem complexity model (Card and Glass),

320–322System crashes and hangs

effort/outcome model and, 301software testing and, 289–291

System testsCPU utilization and, 286–289quality improvement program and, 255

System verification test (SVT), 422–423Systematic errors, 73, 75Systematic sampling, 377–378Systems engineering, CMMI model, 440

TTDCE. See Total defect containment

effectivenessTeam commitment, project success and,

297Team Software Process (TSP), 42Teams

complexity metrics and, 327–328process improvement and, 441–443project assessment and, 427–429quality management models for, 474

Telephone interviews, 376Test case, metrics, 303–304Test defect origin report, 266Test execution, metrics, 303–304Test plan curve, 278–279Test progress S curve

assigning scores to test cases and, 275coverage weighting, 276execution plan and, 278illustration of, 275plan curve and, 278–279purpose of, 272tracking, 276–278

Test/retest method, 75, 76Tests

assigning scores to test cases, 275component, 17defect removal and, 160defect tracking and, 246, 269determining end date for, 256effort/outcome model and, 298functional verification test (FVT), 422–423

Kolmogorov-Smirnov goodness-of-fit test,222

phases, 295for significance, 483system verification test (SVT), 422–423tested defects vs. field defects, 236–237unit tests (UT), 16–17, 243, 264–266

Time between failures modelsassumptions, 217–218Jelinski-Moranda (J-M) model, 212–213reliability growth models and, 212reliability models and, 197

Time boxing, 21Time to market goals, 444–445Tools. See also Quality tools

OO projects, 352–353process improvement stages and, 457

Total defect containment effectiveness(TDCE), 163

Total Quality Control (TQC), 7Total quality management (TQM)

background of, 7customer satisfaction and, 375Hewlett-Packard’s program, 7IBM’s program, 8key elements of, 8, 392Motorola’s program, 7–8organizational frameworks for, 8–10

TQC. See Total Quality ControlTQM. See Total quality managementTracking defects. See also problem tracking

reports (PTRs)IBM Rochester software testing, 279OO quality and, 347–348PTR submodel and, 246testing and, 269

Tracking outage data, 367–368Training materials, 447Trend charts, 402Trillum model, 416TRW Software Productivity System (TRW-

SPS), 21–22TSP. See Team Software Process

Uu charts, 145–146Unified Modeling Language (UML), 30, 457Unified Software Development Process, 30–31Unit tests (UT)

code development and, 243coverage and defect report, 264–265

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Unit tests (UT) (continued)defects by test phase report, 265–266waterfall model, 16–17

Unplanned IPLs. See Initial program loadsUPRIMD (usability, performance, reliability,

installability, maintainability, docu-mentation, and availability) categories,383–388

Use-case model, UML, 30UT. See Unit tests

VValidation of data, 118Validity, 70–73

defined, 71measurement quality and, 73predictive vs. empirical, 203–204reliability and, 72systematic errors and, 75types of, 71–72

Variation, index of, 71Vendors. See also Acceptance testing (vendor-

developed software)Video conferencing, 428Vienna Development Method (VDM), 34

WWaterfall development model, 14–19

advantages of, 14

coding stage, 16component tests, 17early customer programs (ECP),

18–19Entry-Task-Validation-Exit (ETVX),

14example implementation, 14–15high-level design, 16low-level design, 16system-level test, 17–18unit tests, 16–17

Weibull modelsexponential model, 208–211Rayleigh model, 189–190

Weighted Methods per Class (WMC),337–338

Where found, 169–171, 182–183Workload characteristics, 286–287

XX-bar charts, 145XP (Extreme Programming), 31–32

ZZ notation, 34Zero point, ratio scale and, 61

528 Index

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