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USC CSCI 510 - EC-6-COCOMOIIOverview_VBSE

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Slide 1AgendaSoftware Cost Estimation MethodsCOCOMO BackgroundSlide 5COCOMO Black Box ModelMajor COCOMO II FeaturesCOCOMO Uses for Software Decision MakingProductivity RangesCOCOMO SubmodelsSlide 11COCOMO Effort FormulationDiseconomy of ScaleCOCOMO Schedule FormulationCoverage of Different ProcessesCommon Process Anchor PointsMBASE Phase DistributionsWaterfall Phase DistributionsCOCOMO II Output RangesCOCOMO Tailoring & EnhancementsSlide 21Cost FactorsScale DriversCost DriversExample Cost Driver - Required Software Reliability (RELY)Example Effort Multiplier Values for RELYScale FactorsPrecedentedness (PREC) and Development Flexibility (FLEX)Architecture / Risk Resolution (RESL)Team Cohesion (TEAM)Process Maturity (PMAT)Key Process AreasSlide 33Product FactorsProduct Factors cont’dSlide 36Slide 37Slide 38Slide 39Platform FactorsPlatform Factors cont’dPersonnel FactorsPersonnel Factors cont’dSlide 44Project FactorsProject Factors cont’dCost Factor RatingCost Driver Rating Level SummaryCost Driver Rating Level Summary cont’dSlide 50Reused and Modified SoftwareNonlinear Reuse EffectsCOCOMO Reuse ModelCOCOMO Reuse Model cont’dAssessment and Assimilation Increment (AA)Software Understanding Increment (SU)Programmer Unfamiliarity (UNFM)Commercial Off-the-Shelf (COTS) SoftwareReuse Parameter GuidelinesSlide 60Lines of CodeLines of Code Counting RulesLines of Code Counting Rules cont’dCounting with Function PointsCounting with Function Points cont’dSlide 66Slide 67Slide 68Unadjusted Function Points CountingUnadjusted Function Points Counting Procedure cont’dSlide 71Slide 72USC COCOMO DemoSlide 74Cost Driver Ratings ProfileExample Cost Driver Ratings ProfileTechniques to Generate Cost Driver Ratings ProfileCOCOMO Dataset Cost MetricsRecommended Project Cost DataRecommended Project Cost Data cont’dEffort Staff-Hours DefinitionFurther InformationUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 119/1/14COCOMO II OverviewBarry BoehmCSCI 510 Fall 2011University of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 219/1/14Agenda•COCOMO introduction•Basic estimation formulas•Cost factors•Reuse model•Sizing•USC COCOMO tool demo•Data collectionUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 319/1/14Software Cost Estimation Methods•Cost estimation: prediction of both the person-effort and elapsed time of a project•Methods:–Algorithmic–Expert judgement–Estimation by analogy–Parkinsonian•Best approach is a combination of methods–compare and iterate estimates, reconcile differences•COCOMO is the most widely used, thoroughly documented and calibrated cost model–Price-to-win–Top-down–Bottom-upUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 419/1/14COCOMO Background•COCOMO - the “COnstructive COst MOdel”–COCOMO II is the update to COCOMO 1981–ongoing research with annual calibrations made available •Originally developed by Dr. Barry Boehm and published in 1981 book Software Engineering Economics•COCOMO II described in new book Software Cost Estimation with COCOMO II•COCOMO can be used as a framework for cost estimation and related activitiesUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 519/1/14•Effect of uncertaintiesover timeSoftware Estimation AccuracyFeasibility Plans/Rqts. Design Develop and TestPhases and MilestonesRelative Size RangeOperationalConceptLife Cycle ObjectivesLife Cycle ArchitectureInitial Operating Capabilityx0.5x0.25x4x2xUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 619/1/14COCOMO Black Box ModelCOCOMO IIproduct size estimateproduct, process, platform, and personnel attributesreuse, maintenance, and increment parametersorganizational project datadevelopment, maintenance cost and schedule estimatescost, schedule distribution by phase, activity, incrementrecalibration to organizational dataUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 719/1/14Major COCOMO II Features•Multi-model coverage of different development sectors•Variable-granularity cost model inputs•Flexibility in size inputs–SLOCS–function points–application points–other (use cases ...)•Range vs. point estimates per funnel chartUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 819/1/14COCOMO Uses for Software Decision Making•Making investment decisions and business-case analyses •Setting project budgets and schedules •Performing tradeoff analyses•Cost risk management•Development vs. reuse decisions•Legacy software phaseout decisions•Software reuse and product line decisions•Process improvement decisionsUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 919/1/14Productivity Ranges•COCOMO provides natural framework to identify high leverage productivity improvement factors and estimate their payoffs.4.143.372.211.851.721.671.641.601.571.491.481.291.281.271 1.5 2 2.5 3 3.5 4 4.5 Personnel Capability Personnel Experience Product Complexity Required Reliability Use of Software Tools Execution Time Cons traint Required Reuse Multisite Development Main Storage Constraint Platform Volatility Personnel Continuity Required Development Schedule Database Size DocumentationCost FactorProductivity RangeUniversity of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 1019/1/14COCOMO Submodels•Applications Composition involves rapid development or prototyping efforts to resolve potential high-risk issues such as user interfaces, software/system interaction, performance, or technology maturity. It’s sized with application points (weighted screen elements, reports and 3GL modules).•The Early Design model involves exploration of alternative software/system architectures and concepts of operation using function points and a course-grained set of 7 cost drivers.•The Post-Architecture model involves the actual development and maintenance of a software product using source instructions and / or function points for sizing, with modifiers for reuse and software breakage; a set of 17 multiplicative cost drivers; and a set of 5 factors determining the project's scaling exponent.University of Southern CaliforniaCenter for Software EngineeringC S EUSC(c) USC CSSE 1119/1/14Agenda•COCOMO


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USC CSCI 510 - EC-6-COCOMOIIOverview_VBSE

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