Constructive COTS Model (COCOTS) Status Chris Abts USC Center for Software Engineering Annual Research Review Annual Research Review.

Slides:

Advertisements

Similar presentations

Automated Software Cost Estimation By James Roberts EEL 6883 Spring 2007.

Advertisements

Copyright 2000, Stephan Kelley1 Estimating User Interface Effort Using A Formal Method By Stephan Kelley 16 November 2000.

COCOMO Suite Model Unification Tool Ray Madachy 23rd International Forum on COCOMO and Systems/Software Cost Modeling October 27, 2008.

COSYSMO 2.0 Workshop Summary (held Monday, March 17 th 2008) USC CSSE Annual Research Review March 18, 2008 Jared Fortune.

Cocomo II Constructive Cost Model [Boehm] Sybren Deelstra.

3/14/2006USC-CSE1 Ye Yang, Barry Boehm Center for Software Engineering University of Southern California COCOTS Risk Analyzer and Process Usage Annual.

OTS Integration Analysis using iStudio Jesal Bhuta, USC-CSE March 14, 2006.

University of Southern California Center for Software Engineering CSE USC COSYSMO: Constructive Systems Engineering Cost Model Barry Boehm, USC CSE Annual.

University of Southern California Center for Software Engineering C S E USC Using COCOMO for Software Decisions - from COCOMO II Book, Section 2.6, 6.5.

COSOSIMO* Workshop 13 March 2006 Jo Ann Lane University of Southern California Center for Software Engineering CSE Annual.

1 COSYSMO 3.0: Future Research Directions Jared Fortune University of Southern California 2009 COCOMO Forum Massachusetts Institute of Technology.

Smi COCOMO II Calibration Status COCOMO Forum October 2004.

University of Southern California Center for Software Engineering CSE USC ©USC-CSE 10/23/01 1 COSYSMO Portion The COCOMO II Suite of Software Cost Estimation.

Integrated COCOMO Suite Tool for Education Ray Madachy 24th International Forum on COCOMO and Systems/Software Cost Modeling November.

May 11, 2004CS WPI1 CS 562 Advanced SW Engineering Lecture #5 Tuesday, May 11, 2004.

10/25/2005USC-CSE1 Ye Yang, Barry Boehm USC-CSE COCOTS Risk Analyzer COCOMO II Forum, Oct. 25 th, 2005 Betsy Clark Software Metrics, Inc.

1 Results of Reuse Survey Jared Fortune, USC Ricardo Valerdi, MIT Gan Wang, BAE COSYSMO COCOMO Forum 2008 Los Angeles, CA.

University of Southern California Center for Systems and Software Engineering ©USC-CSSE1 Ray Madachy, Barry Boehm USC Center for Systems and Software Engineering.

Introduction Wilson Rosa, AFCAA CSSE Annual Research Review March 8, 2010.

1 CORADMO in 2001: A RAD Odyssey Cyrus Fakharzadeh 16th International Forum on COCOMO and Software Cost Modeling University of Southern.

System-of-Systems Cost Modeling: COSOSIMO July 2005 Workshop Results Jo Ann Lane University of Southern California Center for Software Engineering.

University of Southern California Center for Systems and Software Engineering 1 November 2010 Mauricio Peña Dr. Ricardo Valerdi CHARACTERIZING THE IMPACT.

1 Discussion on Reuse Framework Jared Fortune, USC Ricardo Valerdi, MIT COSYSMO COCOMO Forum 2008 Los Angeles, CA.

Estimating System of Systems Engineering (SoSE) Effort Jo Ann Lane, USC Symposium on Complex Systems Engineering January 11-12, 2007.

COCOMO II Database Brad Clark Center for Software Engineering Annual Research Review March 11, 2002.

University of Southern California Center for Software Engineering CSE USC Distributed Assessment of Risk Tool DART Jesal Bhuta

Systems Engineering Reuse: A Report on the State of the Practice Jared Fortune, USC Ricardo Valerdi, MIT Gan Wang, BAE Systems COCOMO Forum 2008 Los Angeles,

University of Southern California Center for Systems and Software Engineering © 2009, USC-CSSE 1 Reuse and Maintenance Estimation Vu Nguyen March 17, 2009.

University of Southern California Center for Software Engineering CSE USC 9/14/05 1 COCOMO II: Airborne Radar System Example Ray Madachy

1 COSYSMO 2.0: A Cost Model and Framework for Systems Engineering Reuse Jared Fortune University of Southern California Ricardo Valerdi Massachusetts Institute.

COCOTS Presentation for CSCI 577 Fall 2006 Jesal Bhuta

University of Southern California Center for Software Engineering CSE USC 110/26/2004©USC-CSE Welcome and Overview: COCOMO / SCM #19 Forum and Workshops.

University of Southern California Center for Systems and Software Engineering © 2009, USC-CSSE 1 An Analysis of Changes in Productivity and COCOMO Cost.

University of Southern California Center for Software Engineering CSE USC 10/8/00©USC-CSE1 Expediting Technology Transfer via Affiliate Programs and Focused.

CDIO Initiative Project Meeting #15 Location: University of Pretoria, Engineering Tower Building, Pretoria, South Africa February 16-18, 2005.

A Brief Introduction to COCOMO Hossein Saiedian EECS810: Software Engineering.

T. E. Potok - University of Tennessee CS 594 Software Engineering Lecture 3 Dr. Thomas E. Potok

University of Southern California Center for Software Engineering C S E USC Using COCOMO for Software Decisions - from COCOMO II Book, Section 2.6 Barry.

University of Southern California Center for Software Engineering C S E USC Using COCOMO for Software Decisions - from COCOMO II Book, Section 2.6 Barry.

10/27/20151Ian Sommerville.  Fundamentals of software measurement, costing and pricing  Software productivity assessment  The principles of the COCOMO.

Estimating Software Projects & Activity Scheduling in the Dynamic, Multi-Project Setting: Choosing Heuristics Through Deterministic Simulation.

University of Southern California Center for Systems and Software Engineering Vu Nguyen, Barry Boehm USC-CSSE ARR, May 1, 2014 COCOMO II Cost Driver Trends.

University of Southern California Center for Systems and Software Engineering COCOMO Suite Toolset Ray Madachy, NPS Winsor Brown, USC.

SFWR ENG 3KO4 Slide 1 Management of Software Engineering Chapter 8: Fundamentals of Software Engineering C. Ghezzi, M. Jazayeri, D. Mandrioli.

Software Project Estimation IMRAN ASHRAF

Investigating and Improving a COTS-based Software Development Process

University of Southern California Center for Systems and Software Engineering © 2010, USC-CSSE 1 Trends in Productivity and COCOMO Cost Drivers over the.

Empirical Estimation Models Based upon historic data Basic Structure E = A + B * (ev) C where A, B, c are empirical constants ‘ev’ is the effort in terms.

Function Points Synthetic measure of program size used to estimate size early in the project Easier (than lines of code) to calculate from requirements.

Bob Lucas University of Southern California Sept. 23, 2011 Transforming Geant4 for the Future Bob Lucas and Rob Roser USC and FNAL May 8, 2012.

COOSS: An initial COCOTS Extension Model for Estimating Cost of Integrating Open Source Software Components Lin Shi, Celia Chen, Qing Wang, Barry Boehm.

Proposed Metrics Definition Highlights Raymond Madachy Naval Postgraduate School CSSE Annual Research Review March 8, 2010.

University of Southern California Center for Systems and Software Engineering Reducing Estimation Uncertainty with Continuous Assessment: Tracking the.

The COCOMO model An empirical model based on project experience. Well-documented, ‘independent’ model which is not tied to a specific software vendor.

Smi COCOMO II Calibration Status USC-CSE Annual Research Review March 2004.

University of Southern California Center for Systems and Software Engineering COTS Selection Sergio Romulo Salazar April 28, 2010.

Center for Systems and Software Engineering DoD Software Resource Data Reports (SRDRs) and Cost Data Analysis Workshop Summary Brad Clark University of.

University of Southern California Center for Systems and Software Engineering 1 © USC-CSSE Integrating Case-Based, Analogy-Based, and Parameter-Based Estimation.

University of Southern California Center for Systems and Software Engineering Reducing Estimation Uncertainty with Continuous Assessment Framework Pongtip.

بشرا رجائی برآورد هزینه نرم افزار.

COCOMO Software Cost Estimating Model Lab 4 Demonstrator : Bandar Al Khalil.

1 Agile COCOMO II: A Tool for Software Cost Estimating by Analogy Cyrus Fakharzadeh Barry Boehm Gunjan Sharman SCEA 2002 Presentation University of Southern.

Product Support BCA Exercise – JRATS/JTAMS

Mathematical Formulation and Validation of the Impact of Requirements Volatility on Systems Engineering Effort March 6, 2012 Mauricio E. Peña.

COCOTS Life Cycle Estimation: Some Preliminary Observations

Sustainment Reporting CADE Sustainment Reporting

COCOMO II Security Extension Workshop Report

SLOC and Size Reporting

Software Systems Cost Estimation

Introduction to Constructive COTS (COCOTS) Model and Tool

Presentation transcript:

Constructive COTS Model (COCOTS) Status Chris Abts USC Center for Software Engineering Annual Research Review Annual Research Review Feb. 6, 2001 Feb. 6, 2001 Copyright 2001 University of Southern California

2CA 2/06/01 Briefing Outline COCOTS Overview Research Highlights Since ARR 2000 Data Highlights New Glue Code Submodel Results Next Steps Benefits

3CA 2/06/01 COCOTS Overview

4CA 2/06/01 Research Highlights Since ARR 2000 Data collected for four sources of effort Reasonably robust calibration of Glue Code submodel Spreadsheet Tool

5CA 2/06/01 Data Highlights Mean % of Total COTS Effort by Activity (+/- 1 SD) 49.07% 50.99% 61.25% 20.27% 20.75% 21.76% 31.06% 11.31% -7.57% -7.48% 0.88% 2.35% % % 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% assessmenttailoring glue code system volatility % Person-months

6CA 2/06/01 Data Highlights

7CA 2/06/01 Data Highlights

8CA 2/06/01 Data Highlights

9CA 2/06/01 New Glue Code Submodel Results Current calibration looking reasonably good – Excluding projects with very large, very small amounts of glue code (Effort Pred): [ KLOC]: Pred (.30) = 9/17 = 53% [ KLOC]: Pred (.30) = 8/13 = 62% – For comparison, calibration results shown at ARR 2000: [ KLOC]: Pred (.30) = 4/13 = 31% Propose to revisit large, small, anomalous projects – A few follow-up questions on categories of code & effort Glue code vs. application code Glue code effort vs. other sources

10CA 2/06/01 New Glue Code Submodel Results (Detailed)

11CA 2/06/01 Next Steps Data conditioning – Follow-up questions on categories of code & effort Further data collection – Accelerating collection of 10 additional data points over next 8 weeks (including a first pass at COTS maintenance data) – As always, can you help? COCOTS total lifecycle model – Two FAA-sponsored workshops, Int’l COCOMO forum breakout session held in 2000; USC-SEI-CeBASE workshop on COTS-based systems this week – COCOTS data survey being expanded to address total lifecycle issues based on insights gained from above meetings

12CA 2/06/01 Benefits Existing – Independent source of estimates – Checklist for effort sources – (Fairly) easy-to-use development phase tool On the Horizon – Empirically supported, tightly calibrated, total lifecycle COTS estimation tool