COCOMO I1 Status and Plans

Transcription

1 - A University of Southern California c I S IE I Center for Software Engineering COCOMO I1 Status and Plans Brad Clark, Barry Boehm USC-CSE Annual Research Review March 10, 1997

2 University of Southern California c S (E( Center for Software Engineering 81, Outline COCOMO I1 Status: Brad Clark - Model calibration - Tool Status - Data Status COCOMO I1 Plans: Barry Boehm - Tool Plans - Model Plans - Training and data collection plans I

3 Presentation Outline: *Model Calibration Calibration Procedures COCOMO Model paramiters Accuracy Results USC COCOMO Software Status COSTAR Software Status Calibration Data Status I BKC 2./23/97

4 UNrVERSITY OF Model Calibration Status: Three models comprise COCOMO 11: + Applications Composition + Early Design + Post-Architecture Post-Architecture model calibrated Early Design model will be derived from Post-Architecture Applications Composition: need data I I BKC 2/23/97

5 Calibration Process: Begin with expert-determined apriori model parameters Collect Data Identify and consolidated highly correlated model parameters Statistically determine estimates of consolidated model parameters from data Use data determined coefficients to adjust apriori model parameters Experiment with weighting I factors BKC 2/23/97

6 Post-Architecture Model: Non-linear model: 11 PM = size)^ O ~ E M, estimated A: Multiplicative calibration variable B : Captures relative diseconomies of scale. Consists of 5 scale factors: B=1.01+ SF. EM: Effort Multipliers to reflect characteristics of particular software under development. 0 Size: Derived from either Source Lines of Code or Function Points. Includes reuse and breakage effects. BKC 2/23/97

7 Apriori Model Parameters: I Driver / Symbol I VL I L I N I H I FLEX SF I RESL SF I TEAM I SF I PMAT I SF / 0.03 )0.02 ( 1 RELY I EM, I DATA 1 EM2 I I CPLX I EM, / IRUSEI EM, I ~~0.89~1.00~1.16~ I DOCU I EM, ( / 1.OO I TIME I EM, I I ( PVOL I EM, I BKC 2/23/97

8 SOUTHERN CALIFORNU I Driver I Symbol I VL I L N H IVHIXHI ACAP 1 EM OO / I PCAP I EMlo ~1.37~1.16~1.00~0.87/0.74~ I I PCON I EM,, I AEXP PEXP LTEX I SITE I EM,, / SCED EMlP EM,, EMl4 EM,, BKC

9 Data Collection: Define the data needed (to completely describe the Post Architecture Model). Collect data with a paper form or a computer software tool Affiliate Organizations providing majority of data. + Historical - whole project Site visits or phone interviews to record data Enter in data into the re~ositorv Data is labeled with generic id Stored in locked room Limited access by researchers Data Consistency checking and conditioning BKC 2/23/97

10 Consolidated Highly Correlated Parameters : 1 TlME 1 STOR IACAP I PCAP TIME ACAP I I PCAP Combined (for calibration purposes only): + TIME & STOR into RCON (Resource Constraints) + ACAP & PCAP into PERS (Personnel Factors) Thus, calibrated 15 effort multipliers instead of 17 BKC 2/23/97

11 Expanded Post-Architecture Model: Distribute the Scale Factors 2 1 predictor variables: 15 Effort Multiplier Coefficients + 5 Scale Factor Coefficients + overall A constant: 1.01,St = A (Size) EM,- EM 15 Log Transformed Model: Regression analysis will derive the coefficients, A and bi, for 1 each factor In(PM est )-ln(si~e)~l.ol= A+b,SF,ln(Size)+~~~+b,,ln(EMl,) BKC U23197

12 Example of Applying Coefficients to Model Apriori Parameters: I Driver I Symbol PREC I SF, FLEX I SF, RESL 1 SF, TEAM I SF, PMATI SF, RELY I EM, DATA I EM, CPLX I EM, DOCUI EM, PVOL I PERS I PCON 1 AEXP I EM, EM, EM,, EM,, BKC 2/23/97

13 SOUTHERNCALIFORNIA RUSE Effort Multiplier: Example of the effect of a negative coefficient + 10% Regression -a- Regression Results BKC 2/23/97

14 Distribution of RUSE: Frequency O 1.I 1.2 I.3 I RUSE BKC 2/23/97

15 Evolving Model Values: 100% Data Driven 100% Expert Driven Number of projects used in calibration BKC 2/23/97

16 UNNERSITY OF Aposteriori Model Parameters Using 10% of data-determined and 90% of apriori Effort constant, A: 2.45 FLEX I SF, IRESL 1 SF, TEAM 1 SF, ( IPMAT I SF, RELY I EM, ~CPLX I EM, BKC 2/23/97 ~DOCU I EM, ITlME I EM, I I ISTOR I EM, I I

17 Calibrated schedule constant, A: 2.66 (apriori value was 3.0) BKC 2/23/97

18 Presentation Outline: Model Calibration Calibration Procedures I COCOMO I Model Parameters Accuracy Results USC COCOMO Software Status COSTAR Software Status Calibration Data Status BKC 2/23/97

19 Accuracy Results: Forecast accuracy measured with Proportional Error (PE) : [pn/r,y, + p ~~ct] - 1, (PMe,yt- PMact) 2 0 -[PM,, + PM,, ] + 1, ( PMest - PMact ) < 0 I Effort I Before Stratification ( After Stratification I Schedule ( Before Stratification I After Stratification BKC 2/23/97

20 Effort Proportional Error before Stratification BKC 2/23/97

21 Effort Proportional Error after Stratification Organization Number BKC 2J23197

22 USC COCOMO Software Status: There is a initial version available for MS Windows, Sun OS, and Java + Has new calibrated values + Confidence ranges (optimistic, most likely, pessimistic) + User definable Cost Drivers: USR1, USR2 + Schedule input is now project wide + New reference manual + New values can be manually input for all cost drivers + Version changed to COCOMO II.199Y.X (where Y is the year number and X is the version within that BKC 2/23/97

23 USC COCOMO Future Work: Entry of actuals for periodic tracking of project and data submission Calibration of constant and exponent Incremental ratings between Very Low, Low, Nominal, High, Very High, Extra High Text entry for SU, AA, UNFM New Help file BKC 2l23197

24 COSTAR Software Status Commercialized version of COCOMO Beta version of COCOMO I1 model available New values will be put in the model soon BKC 2/23/97

25 Calibration Data Status More project data is required to facilitate better calibration of the general COCOMO I1 Post-Architecture model We hope the use of USC COCOMO software will facilitate collection and submission of data If you calibrate the model to your local organization (constant and exponent) - we would like to have your observations in our repository to be used for full model calibration We plan to make annual updates to the cost driver values and release them on a regular cycle. BKC 2/23/97 CENTER FOR SOF.TWARE ENGINEERING

26 Information Sources : Phone: cocomo-info@sunset.usc.edu Web site: + Affiliate Prospectus + Model Definition Manual (ver. 1.4) + Data Collection Form (ver. 1.6) + USC COCOMO Software and User's Manual + I Java COCOMO + Little Expert COCOMO Calculator BKC a23197

27 I C I S IE I University of Southern California Center for Software Engineering L Outline COCOMO I1 Status: Brad Clark - Model calibration - Tool Status - Data Status -F COCOMO I1 Plans: Barry Boehm - Tool Plans - Model Plans - Training and data collection plans

28 - Universi~y of Southern California ( c ( S IE ( Center for Software Engineering Tool Plans: USC COCOMO Calibration to an organization's data - Effort andlor schedule - Coefficient or also exponent Intermediate rating levels Updated Madachy risk assessment model 1 Added reuse parameters: SU, AA, UNFM

29 Calibration. - Provide a way of capturing and retaining a set of projects. - Capability of changing C (constant) and E (exponent) from cocomo equation. - Provide users with 2 ways of using C and E. - Standard cocomo values - Calibrated values I

30 - University of Southern California C I S BE Center for Software Engineering Model Plans: Affiliate Priorities 14 Activity distribution 13 COTS integration costs l2 Sizing improvements 5 C~st/schedule/quality tradeoffs 5 Life cycle tradeoff models

31 - A University of Southern California I C I S IE I Center for Software Engineering Effort Distribution bv Activitv EffortIFP varies by language level (LL) I - But so does effortisloc! Proposed approach - SLOC, LL ==> Effort: Determine equivalent 3GL SLOC (3 SLOC) via backfiring Compute effort as F (3 SLOC) Apply LL stage multipliers to obtain activity distribution, total effort I - UFP, LL ==> Effort Determine 3 SLOC by backfiring Compute effort a F(3 SLOC) Apply LL stage multipliers to obtain activity distribution, total effort

32 University of Southern California Center for Software Engineering 4GL Cost and Schedule Effects 1 + Correspondence school information system + Estimated size: 15 KDSl ALL [4GL], 95 KDSl COBOL + Actual size: 13.9 KDSl ALL, 93.6 KDSl equiv. COBOL + Data on phase distribution of effort and schedule I

33 University of Southern California Center for Software Engineering 4GL Estimates vs. Actuals Quantity COCOMO- COBOL COCOMO- 4GL Recom. Actual Use Size Schedule Effort Plans&Rqts Prel Design DD/CUT/J&T GLA Mix: COBOL *4GL GL

34 University of Southern California Center for Software Engineering Effort Distribution Relative to 3GL Development Rapid APP* Devel. Spiral-type Ev. Dev., Spiral I LCO, LCA SAT SYS Devel Spiral-type LLICa Waterfall, Spiral-type I LCA W'fall, IncDev, EvDev, Spiral, Design-to-Cost, etc. Det. Design I 1 Code, Integ., Test SAT

35 c I S I E ~ University of Southern California Center for Software Engineering Proposed UML-Based Sizing Model Rational' s Universal Modeling Language (B ooch, Jacobson, 'Rumbaugh) approaching de-facto OOD standard A UML-based early sizing metric would address two major current short falls with Funciton Points - Automated counting I - Object-orientation Rational (Walker Royce) is interested in pursuing such a project Would need some Affiliates to provide data

36 Model Elements Class A set of objects that share a common structure and a 0 common behavior Use caselcollaboration A named behavior involving the collaboration of a society of 0 objects Stateloperation 0 The condition of an object; an activity Interface 0 The public part of an object Thread n An active class, capable of concurrent activity with other active classes Model Elements (cont) a Component A reusable part, typically having both logicalas well as physical aspects Node... A hardware device upon which software may reside andlor I3 execute Package A container of elements Note,. A comment, explanation, or annotation

37 Diagrams Class diagram State machine diagram Sequence diagram Collaboration diagram Activity diagram Use case diagram Component diagram Deployment diagram Architecture Enduser Functionality - Programmers Softwarn management Component I Logical View AnalysMesters Behavior Use Case Concurrency I I Dsployment I " i i viw I System Integrators Performance Scalability Throughput System Engineering srstem *polow Delivery, installation Communication

38 - A University of Southern California c [ S IE I Center for Software Engineering Training and Data Collection Plans USC COCOMO '7.1 or COSTARICALICO as data colleciton instruments Proposed role for Don Reifer - Meet broader need for COCOMO I1 training - Make data collection easier, *more efficient

39 ROLE FOR DON REIFER Assist team in calibrating COCOMO-I1 - Use his databases, when applicable, to help calibrate the model - Run statistical tools to generate goodness of fit and other meaninghl measures Provide ideas based on his extensive cost modeling experience Develop public training and help get data for the model's continued refinement

40 WIN-WIN SITUATION USC and Affiliates get: - Calibration data and the results of an analysis of about 500 projects - Extensive knowledge base of experience of one of the leaders in field of parametric modeling - Public courseware when they need it Reifer Consultants (RCI) gets: - In-depth knowledge of COCOMO-I1 - Ability to consult and market COCOMO-I1 courseware and make a profit

41 USCIRCI AGREEMENTS RCI will not market COCOMO packages - Their focus will be training and consulting RCI will not market competing packages - Their agreement with Resource has been terminated - They have elected to discontinue their SoftCost product line in the Calculations support for future USC will cooperate and get data and. feedback from the trained model users