A Process for Mapping COCOMO Input Parameters to True S Input Parameters

Transcription

1 A Process for Mapping Input s to Input s

2 Agenda > Overview > Rosetta Stone II > Analysis > Summary 2

3 Overview > Initial Comparison and Assessment was Completed by USC Center for Systems & Software Engineering Initial Study was Limited to a Viewpoint Only > Analysis was Completed using the same Data the initial analysis used > Results are More Granular 3

4 Example: WBS Mapping Activities Phases II Management Environment CM Requirements Design Implementation Assessment Deployment Inception Elaboration Construction Transition System Requirements System Design Software Requirements Design Implementation Test System I&T Hardware/Software Integration System Qualification System Requirements Software Requirements Preliminary Design Detailed Design Code & Unit Test Integration & Test Software Qualification Test Hardware/S oftware Integration System I&T System Qualification Legend Core effort coverage per model Effort add-on as % of core coverage Common estimate baseline 4

5 Model Analysis Flow Start 5 Common Data base Apply to Rosetta Stone Uncalibrated Analysis Calibration analysis with Calibration and refined settings Consolidated Analysis

6 to Mapping Precedentedness Rating PREC No direct Input Accounted for in Adapted Design, Code, & test, along with software size reuse, and integration Development Flexibility FLEX No direct input Accounted for in Organization productivity, development team complexity, project complexity, and operating specification Architecture/Risk Resolution RESL No direct input Accounted for in # of Equivalent Requirements, Requirements Stability, COTS Tailoring and Evaluation inputs 6

7 to Mapping Team Cohesion TEAM Stakeholder Involvement Very None Minimal Minimal Moderate Very Complete Extra Complete 7

8 to Mapping Process Maturity PMAT Organizational Productivity Very / CMM Level 1 CMM Level 2 CMM Level 3 Very CMM Level 4 Extra CMM Level 5 8

9 to Mapping Required Software Reliability RELY Operating Specification Very Commercial Proprietary Software: Informal Development Commercial Proprietary Software: Formal Development - Reliability Commercial Production Software: Reliability Commercial Production Software: Reliability Very Military and Commercial aviation Extra Space 9

10 to Mapping Data Base Size DATA Internal & External Integration Very Very Very Very Extra None 10

11 to Mapping Product Complexity CPLX Functional Complexity Very Very Extra

12 to Mapping Developed for Reusability RUSE Design for Reuse None No design for reuse Across Project reuse/low impact reuse Across Program Reuse plans for a few selected parts of the application Across Product Line Reuse plans for much of the application with more than one project Across Multiple Product Lines Significant reuse across multiple projects 12

13 to Mapping Documentation Match to Life-Cycle Needs DOCU No Direct input Accounted for in Operating Specification, amount of new software, design for reuse, and organizational productivity Execution Time Constraints TIME Project Constraints Very, & Very 0.70 Extra 0.85 Main Storage Constraint STORE Project Constraints This input was consolidated with Time 13

14 to Mapping Platform Volatility PVOL Hardware Platform Stability & Hardware Platform availability Very N/A N/A Very Stable - Hardware Exists and is Functional/Available more than 95% of the time Moderately Stable - New hardware, Well Tested/ Available 50% to 75% of the time Very Unstable - Hardware Developed in Parallel/Available less than 50% of the time Extra N/A 14

15 to Mapping Analyst Capability ACAP Development Complexity/Analyst Capability Very Novice Still Learning Capable ly Capable Very Expert Extra Expert 15

16 to Mapping Programmer Capability PCAP Development Complexity/ Programmer Capability Very Novice Still Learning Capable ly Capable Very Expert Extra Expert 16

17 to Mapping Personnel Continuity PCON Development Complexity/ Team Continuity Very > 20% Turnover 10% - 20% Turnover 5% - 10% Turnover 3% - 5% Turnover Very < 3% Turnover Extra < 3% Turnover 17

18 to Mapping Application Experience APEX Development Complexity/ Familiarity with Product Very Novice Less Than 2 Years 2 5 Years 5 10 Years Very More Than 10 Years Extra More Than 10 Years 18

19 to Mapping Platform Experience PLEX Development Complexity/ Familiarity with Development Platform Very Novice Less Than 2 Years 2 5 Years 5 10 Years Very More Than 10 Years Extra More Than 10 Years 19

20 to Mapping Language & Tool Experience LTEX Development Complexity/ Experience with Language Very Novice Less Than 2 Years 2 5 Years 5 10 Years Very More Than 10 Years Extra More Than 10 Years 20

21 to Mapping Use of Software Tools TOOL Design/Code/Test Tools Very Stand Alone Minimal Integration Integration Very Complete Integration Extra Complete Integration 21

22 to Mapping Multi-site Development SITE Multiple Site Development Very Multi-national project with many communication challenges Team in many locations with few communication challenges Entire team located in same place Team in several locations with no focus on communications Very / Extra Team in several locations with communications a high priority 22

23 to Mapping Requirements Development Schedule SCED No direct Input Accounted for by entering schedule dates 23

24 Performance Measures > Compare Actual and Estimated Effort for n Projects in Dataset Relative Error (RE) = (Estimated Effort Actual Effort) / Actual Effort Magnitude of Relative Error (MRE) = І Estimated Effort Actual Effort І / Actual Effort Mean Magnitude of Relative Error (MMRE) = ( (MRE) / n Root Mean Square (RMS) = (( 1/n) (Estimated Effort Actual Effort) 2) ½ Where k = the number of projects in a set of n projects whose MRE <=L 24

25 Performance Examples Magnitude of Relative Error (MRE) Mean Magnitude of Relative Error Record # Normalized Org. Prod Un-Calibrated Calibration Results Normalized Org. Prod Un-Calibrated Calibration Results Series1 Relative Error RE Root Mean Square (RMS) Normalized Org. Prod Un-Calibrated Calibration Results Record # Normalized Org. Prod Un-Calibrated Calibration Results 25

26 II Performance Examples Actual Effort (Personmonths) Dataset Name: NASA 94 Category: Avionics Mode: Embedded Number of projects = Effort Estimates vs. Actuals Estimated Effort (Person-months) Effort Prediction Summary Calibrated Uncalibrated MMRE 55% MMRE 91% RMS RMS PRED(10) 12% PRED(10) 9% PRED(20) 19% PRED(20) 9% PRED(30) 45% PRED(30) 18% PRED(40) 65% PRED(40) 36% Percent Percent Flight Avionics Embedded Uncalibrated Calibrated Flight Avionics Embedded MMRE Calibration Effect Uncalibrated Calibrated Flight Science Embedded PRED(40) Calibration Effect Flight Science Embedded Project Types Project Types Flight (All) Flight (All) Ground Embedded Ground Embedded

27 Summary > Detailed Rosetta Stone Developed for to Input s > Allows for Generating Two Software Model Estimates Two Different Software Model Methodologies using One Input Data Set 27