Factors Influencing System-of-Systems Architecting and Integration Costs
|
|
- Griffin French
- 5 years ago
- Views:
Transcription
1 Paper # (unknown) Factors Influencing System-of-Systems Architecting and Integration Costs Jo Ann Lane University of Southern California Center for Software Engineering 941 W. 37th Place, SAL Room 328 Los Angeles, CA jolane@usc.edu Abstract Today s need for more complex, more capable systems in a short timeframe is leading more organizations towards the integration of existing systems into networkcentric, knowledge-based system-of-systems (SoS). Software and system cost model tools to-date have focused on the software and system development activities of a single software system. As we view the new SoS architectures, we find that the effort associated with the integration of these SoSs is not handled well, if at all, in current cost models. USC s Center for Software Engineering (CSE) began work on a SoS cost model, the Constructive SoS Integration Model (COSOSIMO), in late This model has evolved using feedback obtained from USC CSE affiliates and other experts in industry and academia. This paper presents an overview of the COSOSIMO cost model, descriptions of the size drivers and cost factors currently in the model, a summary of survey feedback received from USC CSE affiliates and other interested experts from industry, and the impact of survey findings on the current COSOSIMO cost model. It concludes with future plans for the COSOSIMO model. History of COSOSIMO Why a COSOSIMO? We are seeing a growing trend in industry and DoD to quickly incorporate new technologies and expand the capabilities of legacy systems by integrating them with other legacy systems, Commercial-Off-the-Shelf (COTS) products, and new systems. With this development approach, we see new activities being performed to define the new architecture, identify sources to either supply or develop the required components, and then to integrate and test these high level components. Along with this system-of-systems (SoS) development approach, we have seen a new role in the development process evolve to perform these activities: that of the Lead System Integrator (LSI). Today, there are fairly mature tools to support the estimation of the effort and schedule associated with the lower-level SoS component systems. For software development activities, there are the COCOMO II, Cost Xpert, Costar, PRICE S, and SEER-SEM cost models. At the single system level, there is COSYSMO to estimate the system engineering effort and PRICE H and SEER-H to estimate hardware development costs. For COTS implementation and integration, there is COCOTS to estimate the effort associated with the assessment, 1
2 tailoring, and glue-code implementation of COTS software products. [Boehm 2005] However, none of these models includes LSI activities such as the definition of the SoS architecture, the solicitation and procurement process for the SoS components, and the integration of the SoS components into the SoS framework. Many LSI organizations often estimate these costs using a percentage of the lower level system component development costs. As we see more and more of this type of development, it is important to get a handle on such questions as: How much should an organization budget for SoS integration activities? Is an extra 10%, 20%, or 50% sufficient? Too much? What factors or characteristics make actual effort higher or lower? COSOSIMO is a parametric model to compute just this effort. It is designed to estimate the up-front LSI effort associated with SoS abstraction, architecting, source selection, systems acquisition, as well as the effort associated with the later activities of integration, test, and change management. With the addition of COSOSIMO to the COCOMO suite of estimation tools, there will be more complete coverage of the development activities associated with implementing a system-of-systems: COSOSIMO: Designed to estimate the effort associated with designing the SoS architecture communications mechanisms, protocols, and interfaces as well as the effort required to integrate, tune, and test the SoS architecture. COSYSMO: Designed to estimate the system engineering effort associated with system development or system modifications required to enable the SoS architecture (effort typically performed by independent organizations responsible for the component systems). PRICE H or SEER-H: Designed to estimate any hardware development costs associated with system development or system modifications required to enable the SoS architecture (effort typically performed by independent organizations responsible for the component systems). COCOMO II: Used to estimate the software development effort associated with software enhancements and modifications required to enable the SoS architecture (effort typically performed by independent organizations responsible for the component systems). COCOTS: Used to estimate the effort associated with identifying, assessing, and incorporating COTS software products into the SoS framework. What is an SoS? Key to developing a cost model such as COSOSIMO is understanding what a system-of-systems is. Early literature research [Jamshidi] shows that the term system-of-systems means many things to many different people and organizations. In the business domain, it is the enterprise-wide integration and sharing of core business information across functional and geographical areas. In the military domain, it is a dynamic communications infrastructure to support operations in a constantly changing, sometimes adversarial, environment. In either case, users and nodes in the SoS network may be either fixed or mobile. Communications may be either pointto-point or broadcast. Networks may tie together other networks and well as nodes and users. SoS component systems typically come and go over time. These component systems can operate both within the SoS framework and independent of the SoS framework. Andrew Sage best summed it up when he states that SoS typically contain a majority of the following characteristics: operational independence of individual systems, 2
3 managerial independence of individual systems, geographic distribution of SoS component systems, emergent SoS behaviour not contained in any one component system, and evolutionary development over time, with functionality continually being added, deleted, and modified. [Sage] COSOSIMO Development Methodology: The development of COSOSIMO is following the standard cost model development methodology used at USC CSE. It is a methodology that builds on current practices, analysis of those practices, as wells as a combination of historical data and expert judgment to develop a statistically significant model for predicting future effort and costs associated with system and software development activities. These seven steps, outlined in Figure 1, can be summarized as: 1) analyze existing literature, 2) perform behavioural analysis, 3) identify relative significance of size drivers and scale factors, 4) perform expert-judgment Delphi assessment, formulate a-priori model, 5) gather project data, 6) determine Bayesian A- Posteriori model, and 7) gather more data; refine model. [Boehm 2000] Current COSOSIMO development activities are focused on steps 3 and 4, as highlighted in Figure Literature Search 2. Behavior Analysis 3. Parameter Significance 4. Expert Judgment 5. Gather Data 6. Bayesian Model 7. More Data/Model Refinement Figure 1. Cost Model Development Methodology. Factors Affecting SoS Architecting and Integration Costs Initial Model Form. The first version of COSOSIMO [Lane 2004] was based upon expert judgment of key members of the COCOMO Working Group and a hierarchical view of SoS architectures as described in [ISO/IEC 15288] and shown in Figure 2. Level 1 S 1 Level 0 SOS S 2 S m S 11 S 12 S 1n S 21 S 22 S 2n S m1 S m2 S mn Figure 2. SoS Architecture View for Initial COSOSIMO It is a two-tiered model to calculate the number of integration person months (IPM) required for the SoS LSI effort: n i Level 1 IPM(S i ) = A 1 [Σ S ij ] Bi m Level 0 IPM(SoS) = A 0 [Σ IPM(S i )] B0 i=1 where: IPM Integration effort in personmonths S ij The size ij th subsystem within the SoS. Determined by computing the weighted average of the ij th subsystem size driver(s). A Constant derived from historical project data. n i Number of Subsystem level 2 components comprising the ith subsystem. j=1 3
4 m Number of Subsystem level 1 components comprising the SoS. B i Effort exponent for the ith subsystem based on the subsystem s exponential scale factors. The geometric product of the scale factors results in an overall exponential effort adjustment factor to the B 0 nominal effort. Effort exponent for the SoS based on the SoSs exponential scale factors. The geometric product of the scale factors results in an overall exponential effort adjustment factor to the nominal effort. The size drivers in the initial version focus on quantifiable sizes associated with the system components and the relationships between them. They were defined as some combination of: software size of interfacerelated software, number of major interfaces, number of component systems, and number of operational scenarios. The initial scale factors include: Integration Simplicity: A parameter that represents the degree of system component coupling, processing criticality, scope of key performance parameters, and system precedentedness. Integration Risk Resolution: A multiattribute parameter that represents the number of major integration risk items, the maturity of risk management and mitigation plan, compatibility of schedules and budgets, expert availability, tool support, and level of uncertainty in integration risk areas. Integration Stability: Indicates anticipated change in integration components during system of system integration activities. Component Readiness: Indicates readiness of component systems for integration. User evaluates level of verification and validation that has/will be performed prior to integration and the level of subsystem integration activities that will be performed prior to integration into the SoS integration lab. Integration Team Capability: Represents the anticipated level of integration team cooperation and cohesion, integration personnel capability and continuity, and integration personnel experience with the application, language, integration tools, and integration platform(s). Maturity of the Integration Processes: A parameter that rates the maturity level and completeness of an integration team s integration processes, plans, and the SOS integration lab. Feedback from CSE Affiliates and Industry Experts. In July and October 2004, the USC CSE conducted workshops to discuss the COSOSIMO model developed to date and to obtain feedback from experts in industry and academia. [Lam] Workshop attendees included representatives from Aerospace Corporation, BAE Systems, Boeing, CIA, Disciplined SW Consulting, FAA, Galorath, General Dynamics, Lockheed Martin, Northrop Grumman, PRICE Systems, Raytheon, SAIC, Sparta, Inc., University of North Carolina, US Army Tacom, and USC CSE. In addition, representatives from the Software Engineering Institute (SEI) provided feedback in other review sessions outside of the workshop. As a part of the workshops, the COSOSIMO development team conducted a survey with respect to the relative importance and applicability of the proposed COSOSIMO size drivers and scale factors. Several 4
5 interesting themes were present in the feedback received. First, the concept of an SoS varies considerably across the multiple organizations participating in the workshop. For some, it may be a multi-system architecture that is planned up-front by an LSI. For others, it is an architecture that evolves over time, often driven by organization needs, new technologies appearing on the horizon, and available budget and schedule. The evolutionary SoS architecture is more of a network architecture that grows with needs and available resources. Because the concept of an SoS varies so much, it was suggested that the CSE may want to better define what an SoS is with respect to the COSOSIMO model. Second, there was considerable discussion about the selected size drivers and scale factors. Many agreed that the initial drivers and scale factors were relevant. However, they may not be complete or they may be difficult to assess in the early stages of the architecture development. Key comments and recommendations provided either during discussions at the workshops or in survey responses included: Software size is probably not known in the early stages. There needs to be an early-design version that does not depend on software size. The COSOSIMO model team should consider the number of types of components and types of interfaces. Size drivers should focus on the number of interface protocols and the number of independent system component organizations to coordinate with in the development of the SoS. Compatibility of existing interfaces should also be considered. Other technical factors to consider should include the complexity of the SoS protocols, the maturity of the SoS architecture, and the level of system component changes being implemented in parallel with the SoS architecture modifications. Management decisions, such as cost and schedule compression, may also have a significant impact on costs. There should be factors to adjust effort for political influences and the level of required negotiations and collaboration between the SoS and component system organizations. Upon further investigation and follow-up discussions, it became clear that the actual work in developing an SoS architecture is in the interfaces and the types of communications supported between the system components. This leads to a more netcentric view of an SoS, illustrated in Figure 3, and a need to measure the size of interfaces and communications. Net-Centric SoS Figure 3. Net-Centric View of SoS. Impact of Feedback on COSOSIMO. As a result of this feedback, the initial version was identified as the post-architecture version and an alternative early-design version of the COSOSIMO model was developed. The early-design model, illustrated in Figure 4, is of the form: where: m SoS L PM = A[Σ C IPi + Σ C SCOj ] B i=1 j=1 n 5
6 Size Drivers Number of SoS interface protocols Number of independent system component organizations Exponential Scale Factors SoS architecture maturity Cost/schedule compression Integration risk resolution Component system maturity and stability Component system readiness Integration team capability Integration processes COSOSIMO SoS LSI Effort Figure 4. Early-Design COSOSIMO Operational Concept. L PM LSI architecture definition and integration effort in personmonths. A Constant derived from historical data. C IPi Complexity factor associated with the i th SoS interface protocol. C SCOj Complexity factor associated with the j th SoS component system organization. m Number of interface protocols supported by the SoS architecture. n Number of organizations providing independently developed and maintained system components for the SoS. B Effort exponent for the SoS based on the SoSs exponential scale factors. The geometric product of the scale factors results in an overall exponential effort adjustment factor to the nominal effort. The early-design size drivers shown in Figure 4 focus on quantifiable sizes associated with the SoS interface protocols and the political complexities of negotiating agreements with other organizations to support the required system component changes needed to enable the SoS protocols. They are defined as: Number of SoS Interface Protocols: The number of distinct interface protocols to be provided by the SoS framework. Number of Independent System Component Organizations: The number organizations that are providing system components that will operate within the SoS framework. Each of these size inputs is weighted with respect to expected complexity. The interface protocol complexity is determined by factors such as number of protocol layers, desired security, and whether this is a new protocol 6
7 being implemented for the first time or an existing protocol. The independent system component organization complexity is determined by the expected level of cooperation between organizations and the competing needs of the SoS versus the needs and schedules of the independent component system provider or sponsor. The early-design scale factors, also shown in Figure 4, have been selected to capture the affects of key development processes and business/political decisions. They are defined as: SoS Architecture Maturity: A parameter that represents the level of maturity of the SoS architecture. It includes the level of detail of the interface protocols and the level of understanding of the performance of the protocols in the SoS framework. Cost/Schedule Compression: The extent of business or political pressures to reduce cost and schedule. Integration Risk Resolution: Same as the initial post-architecture version. Component System Maturity and Stability: A multi-attribute parameter that indicates the maturity level of the system components (number of new component systems versus number of component systems currently operational in other environments), overall compatibility of the system components with each other and the SoS interface protocols, the number of major component system changes being implemented in parallel with the SoS framework changes, and the anticipated change in the component systems during SoS integration activities. Component System Readiness: Same as the initial post-architecture version. Integration Team Capability: Same as the initial post-architecture version. Maturity of the Integration Processes: Same as the initial post-architecture version. Conclusions The current focus of the COSOSIMO model is to determine the key size drivers and scale factors associated with the LSI architecture and integration effort. Initial efforts were somewhat successful, as indicated in survey responses from representatives from industry and academia involved in SoS development. However, this same input indicated that while many of the initial drivers and factors selected influenced the SoS effort, they would be difficult to estimate in the early stages of development. Efforts should be more focused on an early-design version of the COSOSIMO model. Workshop discussions and surveys also indicated that there should be a considerable influence from political aspects of the SoS development effort, business decisions, and the complexities of people-communications across the development organizations. The early-design version of the model presented in this paper reflects the recommendations of workshop participants. It drops size drivers and scale factors that are relatively unknown or difficult to estimate in the early on. It replaces them with other relatively independent drivers and factors that are thought to have significant influence on the SoS LSI effort and are more readily discerned in the early stages of SoS inception and concept development. Future Plans Current plans are to continue to evolve both the initial post-architecture and the earlydesign versions of the COSOSIMO model. The next steps will be the review of the updated COSOSIMO model versions at another workshop with the USC CSE affiliates in March In addition, candidate sources 7
8 for actual data have been identified and efforts to capture this data for model validation and calibration have started. References Boehm, B., Abts, C., Brown, A. W., Chulani, S., Clark, B. K., Horowitz, E., Madachy, R., Reifer, D., Steece, B., Software Cost Estimation with COCOMO II. Prentice Hall, Boehm, B., Valerdi, R., Lane, J., and Brown, W., COCOMO Suite Methodology and Evolution, CSE Working Draft, January ISO/IEC 15288, Systems Engineering System Life Cycle Processes Jamshidi, M., System-of-Systems Engineering - a Definition, IEEE SMC 2005, Hawaii, October Lam, A. and Lane, J., COSOSIMO Workshop Minutes, 19 th Forum on COCOMO and Software Cost Modeling, USC CSE, October 26, Lane, J., Constructive Cost Model for Systemof-Systems Integration, International Symposium on Empirical Software Engineering, Los Angeles, California, August Maier, M. W., Architecting Principles for Systems-of-Systems, Systems Engineering, 1:4, pp , Sage, A., and C. Cuppan. "On the Systems Engineering and Management of Systems of Systems and Federations of Systems." Information, Knowledge, and Systems Management 2 (2001): years of software engineering and development experience in software development, software project management, software process definition and implementation, and metrics collection and analysis. Ms. Lane earned her BA degree in Mathematics and her MS degree in Computer Science from San Diego State University. Biography Ms. Jo Ann Lane is currently a PhD student at USC working in the area of System Architecting and Engineering. Prior to joining the USC PhD program, Ms. Lane was a key technical member of Science Applications International Corporation s (SAIC) Software and Systems Integration Group. She has over 8
Synthesis of Existing Cost Models to Meet System of Systems Needs
Paper #128 Synthesis of Existing Cost Models to Meet System of Systems Needs Jo Ann Lane University of Southern California Center for Software Engineering 941 W. 37th Place, SAL Room 328 Los Angeles, CA
More informationSystem-of-Systems Cost Estimation: Analysis of. Lead System Integrator Engineering Activities
System-of-Systems Cost Estimation: Analysis of Lead System Integrator Engineering Activities Jo Ann Lane, University of Southern California, USA; E-mail: TUjolane@usc.eduUT Dr. Barry Boehm, University
More informationCOSOSIMO Parameter Definitions Jo Ann Lane University of Southern California Center for Software Engineering
Constructive System-of-Systems Integration Cost Model COSOSIMO Parameter Definitions Jo Ann Lane University of Southern California Center for Software Engineering jolane@usc.edu Introduction The Constructive
More informationSynthesizing SoS Concepts for Use in Cost Estimation
Synthesizing SoS Concepts for Use in Cost Estimation Jo Ann Lane Center for Software Engineering University of Southern California 941 W. 37th Place, SAL Room 328 Los Angeles, CA 90089-0781 jolane@usc.edu
More informationCOSYSMO: A Systems Engineering Cost Model
COSYSMO: A Systems Engineering Cost Model Ricardo Valerdi and Barry W. Boehm Abstract: Building on the synergy between Systems engineering and Software Engineering, we have developed a parametric model
More informationCOCOMO Suite Methodology and Evolution
Software Engineering Technology COCOMO Suite Methodology and Evolution In the late 1970s and the early 1980s as software engineering was starting to take shape, software managers found they needed a way
More informationWelcome and Overview: USC-CSE Affiliates Workshops
Welcome and Overview: -CSE Affiliates Workshops Barry Boehm, -CSE October 21, 2003 10/22/2003 -CSE 1 Outline -CSE Highlights, 2003 -CSE Affiliates and Calendar Workshop Topics and Agenda 10/22/2003 -CSE
More informationSystems of Systems Cost Estimation Solutions
Systems of Systems Cost Estimation Solutions Josh Wilson Content Background Characteristics of a System of Systems (SoS) System Engineering (SE) / System of Systems Engineering (SoSE) Cost Estimating solution
More informationCost Estimation for Secure Software & Systems Workshop Introduction
Cost Estimation for Secure Software & Systems Workshop Introduction Edward Colbert, Sr. Research Associate Dr. Barry Boehm, Director Center for System & Software Engineering {ecolbert, boehm}@csse.usc.edu
More informationCOSYSMO: A Systems Engineering Cost Model
COSYSMO: A Systems Engineering Cost Model Barry W. Boehm, Donald J. Reifer, Ricardo Valerdi University of Southern California Center for Software Engineering 941 W. 37th Place, SAL Room 328 Los Angeles,
More informationCurrent and Future Challenges for Ground System Cost Estimation
Current and Future Challenges for Ground System Cost Estimation Barry Boehm, Jim Alstad, USC-CSSE GSAW 2014 Working Group Session 11F Cost Estimation for Next-Generation Ground Systems February 26, 2014
More informationPSM. Practical Software and Systems Measurement A foundation for objective project management. COSYSMO Requirements Volatility Workshop
Practical Software and Systems Measurement A foundation for objective project management PSM COSYSMO Requirements Volatility Workshop July 27 2010 Dr. Ricardo Valerdi Mauricio Peña PSM Users Group Conference
More informationAddressing the Challenges of Systems Engineering Estimation
Addressing the Challenges of Systems Engineering Estimation Karen McRitchie/Kathy Kha, Galorath Incorporated 2016 Copyright Galorath Incorporated 1 ABSTRACT Cost is a crucial factor in evaluating the viability
More informationOverview: Focused Workshop on Software Empirical Research and COCOMO Extensions
Overview: Focused Workshop on Software Empirical Research and COCOMO Extensions Barry Boehm, USC-CSE October 24, 2000 1 Outline USC-CSE Highlights, 2000 USC-CSE Affiliates and Calendar Objectives of This
More informationSoftware Technology Conference
30 April 2003 Costing COTS Integration Software Technology Conference Salt Lake City Linda Brooks 1 Objective Provide a roadmap for doing an estimate for a Commercial Off-the-Shelf (COTS) software intensive
More informationSystem of Systems Enterprise Systems Engineering, the Enterprise Architecture Management Framework, and System of Systems Cost Estimation
System of Systems Enterprise Systems Engineering, the Enterprise Architecture Management Framework, and System of Systems Cost Estimation Dr. Paul Carlock Northrop Grumman Corporation Mission Systems paul.carlock@ngc.com
More informationSEER for Systems Engineering Webinar February 24, Copyright Galorath Incorporated 1
SEER for Systems Engineering Webinar February 24, 2016 2016 Copyright Galorath Incorporated 1 Why estimate systems engineering effort? Research has shown that adequate SE effort on the frontend leads to
More informationSOFTWARE EFFORT AND SCHEDULE ESTIMATION USING THE CONSTRUCTIVE COST MODEL: COCOMO II
SOFTWARE EFFORT AND SCHEDULE ESTIMATION USING THE CONSTRUCTIVE COST MODEL: COCOMO II Introduction Jongmoon Baik, Sunita Chulani, Ellis Horowitz University of Southern California - Center for Software Engineering
More informationCalibration Approach and Results of the COCOMO II Post- Architecture Model
Calibration Approach and Results of the COCOMO II Post- Architecture Model Sunita Chulani*, Brad Clark, Barry Boehm (USC-Center for Software Engineering) Bert Steece (USC-Marshall School of Business) ISPA
More informationTowards a Work Breakdown Structure for Net Centric System of Systems Engineering and Management
Towards a Work Breakdown Structure for Net Centric System of Systems Engineering and Management Dr. Gan Wang BAE Systems, Electronics & Integrated Solutions 11487 Sunset Hills Road Reston, VA 20190-4259
More informationCOSYSMO: Constructive Systems Engineering Cost Model
COSYSMO: Constructive Systems Engineering Cost Model Barry Boehm, USC CSE Annual Research Review February 6, 2001 Outline Background Scope Proposed Approach Strawman Model Size & complexity Cost & schedule
More informationA Managerial Issues-aware Cost Estimation of Enterprise Security Projects
A Managerial Issues-aware Cost Estimation of Enterprise Security Projects Boutheina A. Fessi, Yosra Miaoui, Noureddine Boudriga Communications Networks and Security Research Lab. (CN&S) University of Carthage
More informationTowards a Work Breakdown Structure for Net Centric System of Systems Engineering and Management
Towards a Work Breakdown Structure for Net Centric System of Systems Engineering and Management Dr. Gan Wang BAE Systems, Electronics & Integrated Solutions 11487 Sunset Hills Road Reston, VA 20190-4259
More informationCOSYSMO 3.0: The Expert-Based Model
COSYSMO 3.0: The Expert-Based Model Jim Alstad USC Center for Systems and Software Engineering 32nd International Forum on COCOMO and Systems/Software Cost Modeling University of Southern California October
More informationSoftware Cost Estimation Issues for Future Ground Systems
Software Cost Estimation Issues for Future Ground Systems Nancy Kern Software Engineering Department ETG/RSD The Aerospace Corporation Outline ➊ Background ➋ Software Cost Estimation Research OO Software
More informationAffordable Systems: Balancing the Capability, Schedule, Flexibility, and Technical Debt Tradespace
Affordable Systems: Balancing the Capability, Schedule, Flexibility, and Technical Debt Tradespace Jo Ann Lane, Supannika Koolmanojwong, and Barry Boehm University of Southern California 941 Bloom Walk
More informationSoftware User Manual Version 3.0. COCOMOII & COCOTS Application. User Manual. Maysinee Nakmanee. Created by Maysinee Nakmanee 2:07 PM 9/26/02 1
COCOMOII & COCOTS Application User Manual Maysinee Nakmanee Created by Maysinee Nakmanee 2:07 PM 9/26/02 1 Created by Maysinee Nakmanee 2:07 PM 9/26/02 2 Contents INTRODUCTION... 4 MODEL OVERVIEW... 5
More informationAssessing COTS Integration Risk Using Cost Estimation Inputs
Assessing COTS Integration Risk Using Cost Estimation Inputs Ye Yang University of Southern California 941 w. 37 th Place Los Angeles, CA 90089-0781 1(213) 740 6470 yangy@sunset.usc.edu Barry Boehm University
More informationCost Estimation IV for Next-Generation Ground Systems Focusing on COSYSMO 3.0: The Expert-Based Model
Cost Estimation IV for Next-Generation Ground Systems Focusing on COSYSMO 3.0: The Expert-Based Model Jim Alstad USC Center for Systems and Software Engineering GSAW 2017 Looking Beyond the Horizon Renaissance
More informationCOCOMO II Demo and ARS Example
COCOMO II Demo and ARS Example CS 566 Software Management and Economics Lecture 5 (Madachy 2005; Chapter 3, Boehm et al. 2000) Ali Afzal Malik Outline USC COCOMO II tool demo Overview of Airborne Radar
More informationCOSYSMO: COnstructive SYStems Engineering Cost MOdel. Ricardo Valerdi USC Annual Research Review March 11, 2002
COSYSMO: COnstructive SYStems Engineering Cost MOdel Ricardo Valerdi USC Annual Research Review March 11, 2002 March 2002 Outline Background on COSYSMO EIA632 Approach Delphi Survey Delphi Round 1 Results
More informationCOSYSMO-IP COnstructive SYStems Engineering Cost Model Information Processing. Headed in a new direction. Workshop Outbrief
COSYSMO-IP COnstructive SYStems Engineering Cost Model Information Processing Headed in a new direction Ricardo Valerdi Workshop Outbrief October 25, 2002 Outline Workshop Objectives Issues and Answers
More informationCOCOTS: Constructive COTS Integration Cost Model
COCOTS: Constructive COTS Integration Cost Model Center for Software Engineering University of Southern California Points of Contact at Christopher Abts (primary graduate researcher) (213) 740-6470 Ms.
More informationEconomic Impact of Reuse on Systems Engineering
Economic Impact of Reuse on Systems Engineering Dr. Ricardo Valerdi Massachusetts Institute of Technology rvalerdi@mit.edu 4 th Annual IeMRC Conference Loughborough University September 2, 2009 IeMRC 4
More informationMTAT Software Economics. Session 6: Software Cost Estimation
MTAT.03.244 Software Economics Session 6: Software Cost Estimation Marlon Dumas marlon.dumas ät ut. ee Outline Estimating Software Size Estimating Effort Estimating Duration 2 For Discussion It is hopeless
More informationOn the Use of Architectural Products for Cost Estimation
Paper #167 On the Use of Architectural Products for Cost Estimation Ricardo Valerdi Massachusetts Institute of Technology Cambridge, MA rvalerdi@mit.edu Indrajeet Dixit University of Southern California
More informationCOSYSMO-IP COnstructive SYStems Engineering Cost Model Information Processing. Headed in a new direction
COSYSMO-IP COnstructive SYStems Engineering Cost Model Information Processing Headed in a new direction Dr. Barry Boehm Ricardo Valerdi Gary Thomas Don Reifer October 24, 2002 Outline Workshop Objectives
More informationBreakout Session 1: Business Cases and Acquisition Strategies Outbrief Marilee J. Wheaton TRW S&ITG Session Chair.
Breakout Session 1: Business Cases and Acquisition Strategies Outbrief Marilee J. Wheaton TRW S&ITG Session Chair 23 February 2001 Chris Abts, USC Center for Software Engineering COCOTS Estimation Model:
More informationCOCOMO II Bayesian Analysis
COCOMO II Bayesian Analysis Sunita Chulani (sdevnani@sunset.usc.edu) Center for Software Engineering University of Southern California Annual Research Review March 9, 1998 Outline Motivation Research Approach
More informationSoftware Architecture Challenges for Complex Systems of Systems
Software Architecture Challenges for Complex Systems of Systems Barry Boehm, USC-CSE CSE Annual Research Review March 6, 2003 (boehm@sunset.usc.edu) (http://sunset.usc.edu) 3/18/03 USC-CSE 1 Complex Systems
More informationPosition Paper for the International Workshop on Reuse Economics, Austin, Texas
Position Paper for the International Workshop on Reuse Economics, Austin, Texas 4.16.2002 COTS-based Systems and Make vs. Buy Decisions: the Emerging Picture Chris Abts Information & Operations Management
More informationWhen Does Requirements Volatility Stop All Forward Progress?
When Does Requirements Volatility Stop All Forward Progress? Practical Software and Systems Measurement User s Group Conference Golden, Colorado July 2007 Jo Ann Lane and Barry Boehm University of Southern
More informationDRAFT. Effort = A * Size B * EM. (1) Effort in person-months A - calibrated constant B - scale factor EM - effort multiplier from cost factors
1.1. Cost Estimation Models Parametric cost models used in avionics, space, ground, and shipboard platforms by the services are generally based on the common effort formula shown in Equation 1. Size of
More informationCost Model Comparison Report
Cost Model Comparison Report October 31, 2006 Update Version Prepared for: NASA Ames Prepared by: University of Southern California Center for Software Engineering 941 West 37 th Place Los Angeles, CA
More informationCOCOMO II Status and Extensions. Barry Boehm, USC COCOMO / SCM Forum #13 October 7,1998. Outline
COCOMO II Status and Extensions Barry Boehm, USC COCOMO / SCM Forum #13 October 7,1998 1 Mt98 WSCCSE 1 Outline COCOMO 11.1 998 Status and Plans Overview of Extensions COTS Integration (COCOTS) Quality:
More informationSpiral Lifecycle Increment Modeling for New Hybrid Processes
Spiral Lifecycle Increment Modeling for New Hybrid Processes Raymond Madachy, Barry Boehm, and Jo Ann Lane University of Southern California Center for Software Engineering, 941 W. 37th Place, Los Angeles,
More informationC S E USC. University of Southern California Center for Software Engineering
COCOMO II: Airborne Radar System Example Dr. Ray Madachy C-bridge Internet Solutions Center for Software Engineering 15th International Forum on COCOMO and Software Cost Modeling October 24, 2000 C S E
More informationArchitecture-Based Drivers for System-of-Systems and Family-of-Systems Cost Estimating
Architecture-Based Drivers for System-of-Systems and Family-of-Systems Cost Estimating Gan Wang BAE Systems Reston, VA gan.wang@baesystems.com Philip Wardle BAE Systems Chelmsford, UK phil.wardle@baesystems.com
More informationCOCOTS: a COTS software integration cost model - model overview and preliminary data findings
COCOTS: a COTS software integration cost model - model overview and preliminary data findings Chris Abts, Barry W. Boehm, and Elizabeth Bailey Clark Abstract As the use of commercial-of-the-shelf (COTS)
More informationYou document these in a spreadsheet, estimate them individually and compute the total effort required.
Experience-based approaches Experience-based techniques rely on judgments based on experience of past projects and the effort expended in these projects on software development activities. Typically, you
More informationA Comparative study of Traditional and Component based software engineering approach using models
A Comparative study of Traditional and Component based software engineering approach using models Anshula Verma 1, Dr. Gundeep Tanwar 2 1, 2 Department of Computer Science BRCM college of Engineering and
More informationExperience with Empirical Studies in Industry: Building Parametric Models
Experience with Empirical Studies in Industry: Building Parametric Models Barry Boehm, USC boehm@usc.edu CESI 2013 May 20, 2013 5/20/13 USC-CSSE 1 Outline Types of empirical studies with Industry Types,
More informationSoftware Cost Metrics Manual
MOTIVATION Software Cost Metrics Manual Mr. Wilson Rosa Dr. Barry Boehm Mr. Don Reifer Dr. Brad Clark Dr. Ray Madachy 21 st Systems & Software Technology Conference April 22, 2009 DOD desires more credible
More informationCOSYSMO Reuse Extension
COSYSMO Reuse Extension Gan Wang BAE Systems Reston, VA gan.wang@baesystems.com Aaron Ankrum BAE Systems Reston, VA aaron.ankrum@baesystems.com Garry J. Roedler Lockheed Martin Philadelphia, PA garry.j.roedler@lmco.com
More informationJo Ann Lane San Diego State University September 2018
Jo Ann Lane San Diego State University September 2018 Evolution of Intelligent First Responder Systems Primarily Hardware Hardware and Software Interoperating Hardware/Software Systems PSM 2018 Capability
More informationIntegrating Systems Engineering and Test & Evaluation in System of Systems Development
Integrating Systems Engineering and Test & Evaluation in System of Systems Development Dr. Judith Dahmann The MITRE Corporation McLean, VA USA jdahmann at mitre.org Kathy Smith GBL Systems Camarillo, CA
More informationEnhancing Cost Estimation Models with Task Assignment Information
Enhancing Cost Estimation Models with Task Assignment Information Joanne Hale Area of MIS Culverhouse College of Commerce and Business Administration The University of Alabama Tuscaloosa, AL 35487 jhale@cba.ua.edu
More informationGoals of course. Themes: What can you do to evaluate a new technique? How do you measure what you are doing?
MSWE 607: Software Life Cycle methods and Techniques Instructor: Professor Marvin V. Zelkowitz Office: 4121 AV Williams Phone: 405-2690 or 403-8935 (Fraunhofer Center) Email (Best way to contact) mvz@cs.umd.edu
More informationProducing Production Quality Software Lecture 14: Group Programming Practices Data Prof. Arthur P. Goldberg Fall, 2005
Producing Production Quality Software Lecture 14: Group Programming Practices Data Prof. Arthur P. Goldberg Fall, 2005 Best Technical Practices for MIS Software From Software Assessments, Benchmarks, and
More informationISA 201 Intermediate Information Systems Acquisition
ISA 201 Intermediate Information Systems Acquisition Lesson 11 Software & Budgeting Learning Objectives Today we will learn to: OVERALL: Given a DoD IT/SW system scenario, develop the software program
More informationCHAPTER 6 AN ANALYSIS OF EXISTING SOFTWARE ESTIMATION TECHNIQUES
54 CHAPTER 6 AN ANALYSIS OF EXISTING SOFTWARE ESTIMATION TECHNIQUES This chapter describes the series of techniques that are implemented in the hybrid tool. Several programs, with Graphic User Interfaces
More informationA Data Item Description for System Feasibility Evidence
A Data Item Description for System Feasibility Evidence Barry Boehm, Jo Ann Lane, Supannika Koolmanojwong, USC Richard Turner, Stevens NDIA Systems Engineering Conference October 24, 2012 Summary Schedule-based
More informationModeling Software Defect Introduction
Modeling Software Defect Introduction Sunita Devnani-Chulani (sdevnani@sunset.usc.edu) California Software Symposium November 7, 1997 OMO IISunita Devnani-Chulani chart 1 Presentation Outline Motivation
More informationModeling Software Defect Introduction and Removal: COQUALMO (COnstructive QUALity MOdel)
Modeling Software Defect Introduction and Removal: COQUALMO (COnstructive QUALity MOdel) Sunita Chulani and Barry Boehm USC - Center for Software Engineering Los Angeles, CA 90089-0781 1-213-740-6470 {sdevnani,
More informationAn Empirical Study of the Efficacy of COCOMO II Cost Drivers in Predicting a Project s Elaboration Profile
An Empirical Study of the Efficacy of COCOMO II Cost Drivers in Predicting a Project s Elaboration Profile Ali Afzal Malik, Barry W. Boehm Center for Systems and Software Engineering University of Southern
More informationComplex Systems of Systems (CSOS) : Software Benefits,Risks,and Strategies
Complex Systems of Systems (CSOS) : Software Benefits,Risks,and Strategies Barry Boehm, USC Vic Basili, Fraunhofer Maryland SIS Acquisition Conference January 28, 2003 10/22/02 USC-CSE 1 Complex Systems
More informationTop Software Engineering Issues in the Defense Industry
Top Software Engineering Issues in the Defense Industry NDIA Systems Engineering Division and Software Committee September 26, 2006 1 Task Description Identify Top 5 Software Engineering problems or issues
More informationCOSYSMO 3.0: An Extended, Unified Cost Estimating Model For Systems Engineering
USC Center for Systems and Software Engineering COSYSMO 3.0: An Extended, Unified Cost Estimating Model For Systems Engineering Sponsor: DASD(SE) By Dr James P Alstad 6 th Annual SERC Doctoral Students
More informationThe Rosetta Stone: Making COCOMO 81 Files Work With COCOMO II
The Rosetta Stone: Making COCOMO 81 Files Work With COCOMO II Donald J. Reifer, Reifer Consultants, Inc. Barry W. Boehm, University of Southern California Sunita Chulani, University of Southern California
More informationSystems Cost Modeling
Systems Cost Modeling Affiliate Breakout Group Topic Gary Thomas, Raytheon 0 1900 USC Center for Software Engineering Sy~C~stModelingBreakoutTopicVisual-v0-1 vl.o - 10/27/00 University of Southern California
More informationTop 5 Systems Engineering Issues within DOD and Defense Industry
Top 5 Systems Engineering Issues within DOD and Defense Industry Task Report July 26-27, 27, 2006 1 Task Description Identify Top 5 Systems Engineering problems or issues prevalent within the defense industry
More informationECE750-Topic11: Component-Based Software. COTS-Based Development and Its Cost Estimation. Ladan Tahvildari
ECE750-Topic11: Component-Based Software COTS-Based Development and Its Cost Estimation Ladan Tahvildari Assistant Professor Dept. of Elect. & Comp. Eng. University of Waterloo COTS Definition Commercial
More informationOrganization Profile
Organization Profile President VP Product Development VP of Professional Services VP of Business Development Chief Financial Officer Director of Software Engineering Director of Professional Services (Quality
More informationSoftware cost estimation
Software cost estimation Joseph Bonello (based on slides by Ian Sommerville) Objectives To introduce the fundamentals of software costing and pricing To describe three metrics for software productivity
More informationImproving Productivity for Projects with High Turnover. Anandi Hira University of Southern California Software Technology Conference October 13, 2015
Improving Productivity for Projects with High Turnover Anandi Hira University of Southern California Software Technology Conference October 13, 2015 Introduction IDPD UCC Metrics Outline Hypotheses Data
More information3. PROPOSED MODEL. International Journal of Computer Applications ( ) Volume 103 No.9, October 2014
Software Effort Estimation: A Fuzzy Logic Approach Vishal Chandra AI, SGVU Jaipur, Rajasthan, India ABSTRACT There are many equation based effort estimation models like Bailey-Basil Model, Halstead Model,
More informationCORADMO and COSSEMO Driver Value Determination Worksheet
1. COCOMO Stage Schedule and Effort MODEL (COSSEMO) COSSEMO is based on the lifecycle anchoring concepts discussed by Boehm 3. The anchor points are defined as Life Cycle Objectives (LCO), Life Cycle Architecture
More informationAerospace Vehicle Systems Institute
System and Software Integration Verification Texas Engineering Experiment Station The idea for this cooperative began in 1997 when Walt Gillette (now the 747X program manager Boeing Commercial Airplanes)
More informationAchievements and Challenges in Software Resource Estimation
Achievements and Challenges in Software Resource Estimation Barry W. Boehm University of Southern California 941 W. 37 th Place Los Angeles, CA 90089 +1 (213) 740-5703 boehm@usc.edu Ricardo Valerdi Massachusetts
More informationThread Based Integrated Concurrent Engineering
Thread Based Integrated Concurrent Engineering ITEA 2012 Annual Symposium Huntington Beach, CA, September 17-20, 2012 Gina M. Parodi de Reid Name: Gina M. Parodi de Reid Key Technical Field: Systems Engineering
More informationCalibrating the COCOMO II Post-Architecture Model
Calibrating the COCOMO II Post-Architecture Model Sunita Devnani-Chulani Bradford Clark Barry Boehm Center for Software Engineering Computer Science Department University of Southern California Los Angeles,
More informationSEER-SEM to COCOMO II Factor Convertor
SEER-SEM to COCOMO II Factor Convertor Anthony L Peterson Mechanical Engineering 8 June 2011 SEER-SEM to COCOMO II Factor Convertor The Software Parametric Models COCOMO II public domain model which continues
More informationMoving Satellite Communications Program to Next Level
Moving Satellite Communications Program to Next Level Booz Allen Hamilton Parametric cost estimating tools, used in an integrated team, are playing a major role in pushing the Navy's Advanced Extremely
More informationAn overview of TEAM strategies for integrating the product realization process
13 An overview of TEAM strategies for integrating the product realization process C.K. Cobb Lockheed Martin Energy Systems P.O. Box 2009, MS-8160 Oak Ridge, TN 37831 USA Phone: (423) 576-1884 Fax: (423)
More informationUSC-CSE Annual Research Review 2000 DEMONSTRATION GUIDE
USC-CSE Annual Research Review 2000 DEMONSTRATION GUIDE USC Center for Software Engineering Department of Computer Science Annual Research Review February 8-1 1,2000 USC-CSE Annual Research Review: 1999
More informationName: DBA COCOMO. Presenter(s): Janet Chu. Objective: Database version of the COCOMOll with additional functionalities.
Demonstration Guide - USC-CSE COCOMOISCM 18 Name: DBA COCOMO Presenter(s): Janet Chu Objective: Database version of the COCOMOll 2000.3 with additional functionalities. Rationale: This software is intended
More informationTOPIC DESCRIPTION SUPPLEMENT for the SYSTEMS ENGINEERING SURVEY DESCRIPTION
1 2 Objectives of Systems Engineering 3 4 5 6 7 8 DoD Policies, Regulations, & Guidance on Systems Engineering Roles of Systems Engineering in an Acquisition Program Who performs on an Acquisition Program
More informationSE Effectiveness Leading Indicators. Garry Roedler
SE Effectiveness Leading Indicators Garry Roedler 1 SE Effectiveness A few questions to think about: Do you perform Systems Engineering (SE), SoS SE, or SW SE to any extent? Are those SE activities effective?
More informationDetermining How Much Software Assurance Is Enough?
Determining How Much Software Assurance Is Enough? Tanvir Khan Concordia Institute of Information Systems Engineering Ta_k@encs.concordia.ca Abstract It has always been an interesting problem for the software
More informationLife Cycle Cost Estimating for System of Systems
Life Cycle Cost Estimating for System of Systems Kishore Gagrani Product Manager PRICE Systems Manmeet Grover Principal Systems Engineer Whole Life Engineering Directorate Raytheon Missile Defense Center
More informationPresented at the 2008 SCEA-ISPA Joint Annual Conference and Training Workshop -
DEVELOPMENT AND PRODUCTION COST EQUATIONS DERIVED FROM PRICE-H TO ENABLE RAPID AIRCRAFT (MDO) TRADE STUDIES 2008 Society Cost Estimating Analysis (SCEA) Conference W. Thomas Harwick, Engineering Specialist
More informationSoftware defects are not created equal and exhibit
Modeling Software Defect Dynamics RECENT ENHANCEMENTS TO THE CONSTRUCTIVE QUALITY MODEL (COQUALMO) HELP IN ASSESSING DEFECT DYNAMICS TO BETTER UNDERSTAND THE TRADEOFFS OF DIFFERENT PROCESSES AND TECHNOLOGIES
More informationAccording to the Software Capability Maturity Model (SW-
Data Collection Four areas generally influence software development effort: product factors, project factors, platform factors, and personnel facfocus estimation Quantifying the Effects of Process Improvement
More informationCOCOMO II Model. Brad Clark CSE Research Associate 15th COCOMO/SCM Forum October 22, 1998 C S E USC
COCOMO II Model Brad Clark CSE Research Associate 15th COCOMO/SCM Forum October 22, 1998 Brad@Software-Metrics.com COCOMO II Model Overview COCOMO II Overview Sizing the Application Estimating Effort Estimating
More informationSENG380:Software Process and Management. Software Size and Effort Estimation Part2
SENG380:Software Process and Management Software Size and Effort Estimation Part2 1 IFPUG File Type Complexity Table 1 External user type External input types External output types Low Average High 3 4
More informationRESULTS OF DELPHI FOR THE DEFECT INTRODUCTION MODEL
RESULTS OF DELPHI FOR THE DEFECT INTRODUCTION MODEL (SUB-MODEL OF THE COST/QUALITY MODEL EXTENSION TO COCOMO II) Sunita Devnani-Chulani USC-CSE Abstract In software estimation, it is important to recognize
More informationHeuristics for Systems Engineering Cost Estimation
Heuristics for Systems Engineering Cost Estimation 14 th Annual PSM Users Group Conference New Orleans, LA Dr. Ricardo Valerdi Massachusetts Institute of Technology July 27, 2010 [rvalerdi@mit.edu] Theory
More informationA Bayesian Software Estimating Model Using a Generalized g-prior Approach
A Bayesian Software Estimating Model Using a Generalized g-prior Approach Sunita Chulani Research Assistant Center for Software Engineering University of Southern California Los Angeles, CA 90089-078,
More informationThe Incremental Commitment Model (ICM), with Ground Systems Applications
The Incremental Commitment Model (ICM), with Ground Systems Applications Barry Boehm, USC-CSSE http://csse.usc.edu (tech report 2009-500) Presented at GSAW 2009, March 25, 2009 Outline Challenges for developing
More informationObject-Oriented Software Engineering Practical Software Development using UML and Java. Chapter 11: Managing the Software Process
Object-Oriented Software Engineering Practical Software Development using UML and Java Chapter 11: Managing the Software Process 11.1 What is Project Management? Project management encompasses all the
More information