Knowledge-Based Systems Engineering Risk Assessment

Size: px
Start display at page:

Download "Knowledge-Based Systems Engineering Risk Assessment"

Transcription

1 Knowledge-Based Systems Engineering Risk Assessment Raymond Madachy, Ricardo Valerdi University of Southern California - Center for Systems and Software Engineering Massachusetts Institute of Technology Systems Engineering Advancement Research Initiative Abstract. A knowledge-based method for systems engineering risk assessment has been automated in an expert system tool. Expert COSYSMO performs systems engineering risk assessment in conjunction with cost estimation using the Constructive Systems Engineering Cost Model (COSYSMO). The technique is an extension of COSYSMO which supports project planning by identifying, categorizing, quantifying, and prioritizing system-level risks. Workshops and surveys with seasoned systems engineering practitioners are used to identify and quantify risks, and the expert assessment has been implemented in an Internet-based tool. The tool is being refined for sustained usage on projects by providing risk control advice, updating the rule base and being integrated into a more comprehensive risk management framework. Introduction Approaches for identifying systems engineering risks are usually separate from cost estimation. However, risk management practice can be improved by leveraging on existing knowledge and expertise during cost estimation activities through the use of cost factors to detect patterns of project risk. For this, we have implemented an automated technique that identifies system engineering risks in conjunction with cost estimation. This information helps users determine and rank associated sources of project risk for mitigation plans. Expert COSYSMO is an expert system tool for systems engineering risk assessment that uses factors in the COSYSMO cost model (Valerdi 2005). It runs on the Internet at The tool automatically identifies project risks in conjunction with cost estimation similar to Expert COCOMO (Madachy 1997). It currently covers 98 risk conditions which are being further discretized into about 600 finer-level risk conditions using the same inputs. The usage of Expert COSYSMO supports project planning by identifying, categorizing, quantifying, and prioritizing system-level risks. Risk situations are characterized by combinations of cost driver values indicating increased effort with a potential for more problems. It simultaneously calculates cost to enable tradeoffs with risk, as it analyzes patterns of cost driver ratings submitted for a COSYSMO (Valerdi 2005) cost estimate. In practice, risks must be identified as specific instances to be manageable. The method identifies individual risks that an experienced systems engineering manager might recognize but often fails to take into account. It also helps calibrate and rank collections of risks, a process which many managers wouldn t do otherwise. This information is then used to develop and execute project risk management plans. With these risks, mitigation plans can be created based on the relative risk severities and provided advice. Cost estimation and risk management are strongly connected since cost estimates are used to

2 evaluate risk and perform risk trade-offs; risk methods such as Monte Carlo simulation can be applied to cost models; and the likelihood of meeting cost estimates depends on risk management (Madachy 1997). The same cost inputs can also be used to assess risk using sensitivity analysis or Monte Carlo simulation such as the COSYSMO-R approach (Valerdi, Gaffney 2007), but the approach described here uses them to infer specific risk situations. Expert COSYSMO has been developed with collaboration between the USC Center for Systems and Software Engineering (CSSE), its industrial affiliates and MIT. It has been specifically supported with focused COSYSMO workshops conducted with systems engineering practitioners from the CSSE affiliates. These seasoned professionals serve as experts providing a collective knowledge base for the method. Method The tool analyzes patterns of cost driver ratings submitted for a COSYSMO cost estimate against pre-determined risk rules. COSYSMO predicts the systems engineering effort per the following equation, where PM stands for Person-Months (PM) of effort. 14 PM NS = A ( we, kφ e, k + wn, kφ n, k + wd, kφ d, k ) EM j k j= 1 Where: PM NS = effort in Person Months (Nominal Schedule) A = calibration constant derived from historical project data k = {REQ, IF, ALG, SCN} w x = weight for easy, nominal, or difficult size driver Φ x, k = quantity of k size driver at weight x E = represents diseconomy of scale (currently equals 1) EM = effort multiplier for the jth cost driver. The geometric product results in an overall effort adjustment factor to the nominal effort. The cost drivers represented in the effort multiplier term EM are used for expressing the risk rules. Predetermined combinations of driver ratings provide red flags of possible risks as the project progresses along its life cycle. For example, if the architecture understanding cost driver is rated Very Low and the level of service requirements is Very High then this indicates a potential risk in the project given that systems with high service requirements are more difficult to implement especially when the architecture is not well understood. These scenarios are predetermined and configured into the model as a set of rules that can automate and improve the risk management process. This method is derived from the Expert COCOMO model for heuristic risk assessment with cost factors (Madachy 1997). The knowledge representation scheme and risk quantification algorithm are similar, however a larger set of experts has been invoked for the knowledge base. Elicitation of knowledge came from systems engineering domain experts in CSSE-sponsored workshops. A survey was used to identify and quantify risks. Figure 1 shows these risk conditions identified from the iterated workshop survey as implemented in the model. The matrix shows the interactions of the cost factors, and the corresponding risk conditions classified into high, medium and low risks per the inputs of 19 systems engineering experts familiar with the cost estimation concepts in COSYSMO. E

3 SIZE RQMT ARCH LSVC MIGR TRSK DOCU INST RECU TEAM PCAP PEXP PROC SITE TOOL SIZE (REQ + INTF + ALG + OPSC) Requirements Understanding Architecture Understanding Level of Service Requirements (the ilities) Migration Complexity (legacy system considerations) Technology Risk (maturity of technology) Documentation match to life cycle needs Number and Diversity of Installations or Platforms Number of Recursive Levels in the Design Stakeholder Team Cohesion Personnel/team capability Personnel Experience and Continuity Process Capability 5 8 Multisite Coordination 8 Tool Support high risk small x = 0.5; big X = 1 medium risk n = 19 low risk Figure 1. Risk conditions from survey. The values contained in each cell represent the number of votes obtained by each driver combination. Since participants were not limited on the total number of votes allowed, the values in each cell are absolute indicators of perceived risk. However, their relative values are not informative since respondents were not asked to rank the driver combinations to each other. In order to subdivide the risks into meaningful categories, the cells that received more than 10 votes from the participants were labelled as high risk. Since there were 19 participants, 10 votes indicated that more than half of the respondents felt that these combinations were of significant risk. Subsequently, cells receiving between 5 and 9 votes were identified as medium risk and the rest were low risk. The breakdown of high/medium/low risks in Figure 1 is as follows: High risk items = 10/105 = 9% Medium risk items = 43/105 = 41% Low risk items = 52/105 From a risk management standpoint, this is a reasonable distribution of different risk categories. In other words, the most critical risk items are approximately 10% of the possible driver combinations. The risk taxonomy and risk quantification algorithms are shown in Figure 2. Risk impact, or risk exposure, is defined as the probability of loss multiplied by the cost of the loss. The quantitative risk weighting scheme accounts for the nonlinearity of the assigned risk levels and cost multiplier data to compute overall risks for each category and for the entire project. The risk level corresponds to the nonlinear relative probability of the risk occurring, and the effort multiplier product represents the cost consequence of the risk. The product involves those effort multipliers involved in the risk situation. Each of the risk categories includes rules that include relevant cost factors. For example, process risks would include conditions that involve

4 the factors for process capability, multi-site coordination and tool support. Project Risk Product risk Process risk Personnel risk Platform risk # categories # category risks Project Risk = risk level i, j * effort mu ltiplier p roduct i, j j = 1 i = 1 where risk level = 1 moderate 2 high 4 very high effort multiplier product= (driver #1 effort multiplier) * (driver #2 effort multiplier)... * (driver #n effort multiplier). Figure 2. Risk taxonomy and weighting. Figure 3 shows the Expert COSYSMO interface. The size and cost driver inputs are provided by the user, and the effort and risk outputs are shown underneath.

5 Figure 3. Expert COSYSMO interface. The risks as identified in Figure 1 are currently being further elaborated into more detailed conditions requiring no further inputs from the user. Figure 4 shows how the coarse risk conditions are being decomposed into finer grained conditions with more precision. The magnitudes of the coarse risks represented in Figure 1 will be used to set the more detailed ranges. This will multiply the number of risk conditions from 98 into approximately 600.

6 Figure 4. Assignment of detailed risk levels Current and Future Work The risk levels are being calibrated for usage, and we are making the outputs more actionable so that explicit risk management steps can be undertaken by users. Specific tasks being completed include scaling the risk summary outputs for each category and defining ranges for low, medium and high risks; adding more explanation to the summary outputs to rationalize the risk quantities; and furthering the capability for automated risk mitigation recommendations. We are creating more granular risk quantification rules. The initial risk assessment scheme is being elaborated into a finer grained risk assessment, and the survey is being continued. We are currently documenting expert risk mitigation advice for each risk condition, and providing that automated guidance to users to help develop their own mitigation actions. The researchers and workshop participants have identified opportunity trees for making associations with relevant risk mitigation actions. An example tree from is shown in Figure 5 from (Madachy 2007).

7 Figure 5. Opportunity tree for risk mitigations. The opportunity trees from (Madachy 2007) are being tailored for systems engineering usage as structures for representing the risk mitigation knowledge. The workshop participants will be involved in scoring the opportunities for specific risks. The tool will also be expanded to detect COSYSMO input anomalies. It will capture inconsistent inputs and flag those to the user. For example, if size indicates a project years

8 longer than any previously undertaken, requirements understanding is very low, and architecture understanding is rated high then there is a conflict in the inputs. Systems engineering risk data from industrial projects from over 60 projects is being analyzed to enhance and refine the technique. With this the method will be better supported with statistical validation tests. Domain experts from industry and government will continue to provide feedback and clarification. Future work will also involve exploration of alternate risk and uncertainty approaches including COSYSMO-R to integrate multiple risk management viewpoints into a more complete risk management framework. References Madachy R.J., Heuristic Risk Assessment Using Cost Factors, IEEE Software, May Madachy R.J., Software Process Dynamics, IEEE-Wiley, Hoboken, NJ, Valerdi, R., The Constructive Systems Engineering Cost Model (COSYSMO), PhD Dissertation, University of Southern California, Los Angeles, CA, May Valerdi, R., Gaffney, J., Reducing Risk and Uncertainty in COSYSMO Size and Cost Drivers: Some Techniques for Enhancing Accuracy, 5th Conference on Systems Engineering Research, Hoboken, NJ, March Biographies Raymond Madachy is a Research Assistant Professor in the USC Industrial and Systems Engineering Department and a Principal of the USC Center for Systems and Software Engineering. He is currently serving as Interim Director of the Systems Architecting and Engineering Program. He has 25 years of management and technical experience in industry including Chief Science Officer at the Cost Xpert Group, Chief Scientist at C-bridge Institute, and Manager of the Software Engineering Process Group at Litton Guidance and Control Systems. His research interests include modeling and simulation of processes for architecting and engineering of complex software-intensive systems; economic analysis and value-based engineering of software-intensive systems; systems and software measurement, process improvement, and quality; quantitative methods for systems risk management; integrating systems engineering and software engineering disciplines; and integrating empirical-based research with process simulation. Ricardo Valerdi is a Research Associate in the Lean Aerospace Initiative and a Lecturer in the Engineering Systems Division at MIT. He is also the co-founder of the Systems Engineering Advancement Research Initiative (SEAri) which was launched in 2007, and a Visiting Associate at the Center for Systems & Software Engineering at USC. He previously worked as a systems engineer at Motorola, and has been affiliated with the Aerospace Corporation's Economic and Market Analysis Center as a Member of the Technical Staff since His current research interests include systems engineering cost estimation, system level metrics and models, dynamics in large-scale government system acquisition, and system-of-systems ontologies.

Lessons Learned From Collecting Systems Engineering Data

Lessons Learned From Collecting Systems Engineering Data 2 nd Annual Conference on Systems Engineering Research, April 2004, Los Angeles, CA. Lessons Learned From Collecting Systems Engineering Data Ricardo Valerdi Center for Software Engineering University

More information

Dr. Barry W. Boehm USC Center for Software Engineering

Dr. Barry W. Boehm USC Center for Software Engineering 7th Annual Practical Software and Systems Measurement Users Group Conference Keystone, CO July 16, 2003 Dr. Barry W. Boehm USC 1 Workshop Agenda Day 1 (1:30 AM 5:00 PM 7/16) Next-level tutorial Review

More information

Integrated Modeling of Business Value and Software Processes

Integrated Modeling of Business Value and Software Processes Integrated Modeling of Business Value and Software Processes Raymond Madachy, USC Center for Software Engineering Department of Computer Science, SAL 8 University of Southern California Los Angeles, CA

More information

COCOMO-SCORM Interactive Courseware Project Cost Modeling

COCOMO-SCORM Interactive Courseware Project Cost Modeling COCOMO-SCORM Interactive Courseware Project Cost Modeling Roger Smith & Lacey Edwards SPARTA Inc. 13501 Ingenuity Drive, Suite 132 Orlando, FL 32826 Roger.Smith, Lacey.Edwards @Sparta.com Copyright 2006

More information

Modern Tools to Support DoD Software Intensive System of Systems Cost Estimation

Modern Tools to Support DoD Software Intensive System of Systems Cost Estimation Modern Tools to Support DoD Software Intensive System of Systems Cost Estimation Jo Ann Lane and Barry Boehm University of Southern California Center for Systems and Software Engineering Abstract Many

More information

Cost Estimation for Secure Software & Systems

Cost Estimation for Secure Software & Systems Background Cost Estimation for Secure Software & Systems Ed Colbert Dr. Barry Boehm Center for Systems & Software Engineering, University of Southern California, 941 W. 37th Pl., Sal 328, Los Angeles,

More information

Assessing Quality Processes with ODC COQUALMO

Assessing Quality Processes with ODC COQUALMO Assessing Quality Processes with ODC COQUALMO Ray Madachy, Barry Boehm USC {madachy, boehm}@usc.edu 2008 International Conference on Software Process May 10, 2008 USC-CSSE 1 Introduction Cost, schedule

More information

The ROI of Systems Engineering: Some Quantitative Results

The ROI of Systems Engineering: Some Quantitative Results The ROI of Systems Engineering: Some Quantitative Results Barry Boehm Center for Systems and Software Engineering University of Southern California boehm@usc.edu Ricardo Valerdi Lean Aerospace Initiative,

More information

Recent Results in Software Process Modeling

Recent Results in Software Process Modeling Recent Results in Software Process Modeling Ray Madachy, Ph.D. C-bridge Internet Solutions University of Southern California Center for Software Engineering rmadachy@c-bridge.com, madachy@usc.edu 1 Introduction

More information

MTAT.03.244 Software Economics. Lecture 5: Software Cost Estimation

MTAT.03.244 Software Economics. Lecture 5: Software Cost Estimation MTAT.03.244 Software Economics Lecture 5: 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 information

Simulation for Business Value and Software Process/Product Tradeoff Decisions

Simulation for Business Value and Software Process/Product Tradeoff Decisions Simulation for Business Value and Software Process/Product Tradeoff Decisions Raymond Madachy USC Center for Software Engineering Dept. of Computer Science, SAL 8 Los Angeles, CA 90089-078 740 570 madachy@usc.edu

More information

Impact and Contributions of MBASE on Software Engineering Graduate Courses

Impact and Contributions of MBASE on Software Engineering Graduate Courses Impact and Contributions of MBASE on Software Engineering Graduate Courses Ricardo Valerdi Massachusetts Institute of Technology rvalerdi@mit.edu Ray Madachy University of Southern California madachy@usc.edu

More information

Engineering Systems Doctoral Seminar ESD.83-- Fall 2011

Engineering Systems Doctoral Seminar ESD.83-- Fall 2011 Engineering Systems Doctoral Seminar ESD.83-- Fall 2011 Class 4 Faculty: Chris Magee and Joe Sussman TA: Rebecca Saari Guest:Dr. Donna Rhodes, Senior Lecturer, ESD and co-founder of SEAri 1 Class 4-- Overview

More information

CSSE 372 Software Project Management: Software Estimation With COCOMO-II

CSSE 372 Software Project Management: Software Estimation With COCOMO-II CSSE 372 Software Project Management: Software Estimation With COCOMO-II Shawn Bohner Office: Moench Room F212 Phone: (812) 877-8685 Email: bohner@rose-hulman.edu Estimation Experience and Beware of the

More information

Effect of Schedule Compression on Project Effort

Effect of Schedule Compression on Project Effort Effect of Schedule Compression on Project Effort Ye Yang, Zhihao Chen, Ricardo Valerdi, Barry Boehm Center for Software Engineering, University of Southern California (USC-CSE) Los Angeles, CA 90089-078,

More information

Measurement Information Model

Measurement Information Model mcgarry02.qxd 9/7/01 1:27 PM Page 13 2 Information Model This chapter describes one of the fundamental measurement concepts of Practical Software, the Information Model. The Information Model provides

More information

Improving Software Development Economics Part I: Current Trends

Improving Software Development Economics Part I: Current Trends Improving Software Development Economics Part I: Current Trends by Walker Royce Vice President and General Manager Strategic Services Rational Software Over the past two decades, the software industry

More information

Topics. Project plan development. The theme. Planning documents. Sections in a typical project plan. Maciaszek, Liong - PSE Chapter 4

Topics. Project plan development. The theme. Planning documents. Sections in a typical project plan. Maciaszek, Liong - PSE Chapter 4 MACIASZEK, L.A. and LIONG, B.L. (2005): Practical Software Engineering. A Case Study Approach Addison Wesley, Harlow England, 864p. ISBN: 0 321 20465 4 Chapter 4 Software Project Planning and Tracking

More information

Software Metrics & Software Metrology. Alain Abran. Chapter 4 Quantification and Measurement are Not the Same!

Software Metrics & Software Metrology. Alain Abran. Chapter 4 Quantification and Measurement are Not the Same! Software Metrics & Software Metrology Alain Abran Chapter 4 Quantification and Measurement are Not the Same! 1 Agenda This chapter covers: The difference between a number & an analysis model. The Measurement

More information

FUNBIO PROJECT RISK MANAGEMENT GUIDELINES

FUNBIO PROJECT RISK MANAGEMENT GUIDELINES FUNBIO PROJECT RISK MANAGEMENT GUIDELINES OP-09/2013 Responsible Unit: PMO Focal Point OBJECTIVE: This Operational Procedures presents the guidelines for the risk assessment and allocation process in projects.

More information

Critical Success Factors for Knowledge-Based Software Engineering Applications

Critical Success Factors for Knowledge-Based Software Engineering Applications Critical Success Factors for Knowledge-Based Software Engineering Applications Barry Boehm and Prasanta Bose USC Center for Software Engineering Department of Computer Science {boehm,bose}@sunset.usc.edu

More information

CHAPTER 8 IMPLEMENTATION ANALYSIS OF HYBRID ESTIMATION TOOL

CHAPTER 8 IMPLEMENTATION ANALYSIS OF HYBRID ESTIMATION TOOL 81 CHAPTER 8 IMPLEMENTATION ANALYSIS OF HYBRID ESTIMATION TOOL 8.1 AN OVERVIEW One important problem with software development project is to get an early and nevertheless accurate estimation of the effort

More information

JOURNAL OF OBJECT TECHNOLOGY

JOURNAL OF OBJECT TECHNOLOGY JOURNAL OF OBJECT TECHNOLOGY Online at www.jot.fm. Published by ETH Zurich, Chair of Software Engineering JOT, 2006 Vol. 5. No. 8, November-December 2006 Requirements Engineering Tasks Donald Firesmith,

More information

CISC 322 Software Architecture

CISC 322 Software Architecture CISC 322 Software Architecture Lecture 20: Software Cost Estimation 2 Emad Shihab Slides adapted from Ian Sommerville and Ahmed E. Hassan Estimation Techniques There is no simple way to make accurate estimates

More information

PMI Risk Management Professional (PMI-RMP ) - Practice Standard and Certification Overview

PMI Risk Management Professional (PMI-RMP ) - Practice Standard and Certification Overview PMI Risk Management Professional (PMI-RMP ) - Practice Standard and Certification Overview Sante Torino PMI-RMP, IPMA Level B Head of Risk Management Major Programmes, Selex ES / Land&Naval Systems Division

More information

PROJECT RISK MANAGEMENT

PROJECT RISK MANAGEMENT PROJECT RISK MANAGEMENT DEFINITION OF A RISK OR RISK EVENT: A discrete occurrence that may affect the project for good or bad. DEFINITION OF A PROBLEM OR UNCERTAINTY: An uncommon state of nature, characterized

More information

Project Cost Risk Analysis: The Risk Driver Approach Prioritizing Project Risks and Evaluating Risk Responses

Project Cost Risk Analysis: The Risk Driver Approach Prioritizing Project Risks and Evaluating Risk Responses Project Cost Risk Analysis: The Risk Driver Approach Prioritizing Project Risks and Evaluating Risk Responses David T. Hulett, Ph.D. Keith Hornbacher, MBA Waylon T. Whitehead Hulett & Associates, LLC Los

More information

A Look at Software Engineering Risks in a Team Project Course

A Look at Software Engineering Risks in a Team Project Course A Look at Software Engineering Risks in a Team Project Course Supannika Koolmanojwong and Barry Boehm Center for Systems and Software Engineering (CSSE) University of Southern California (USC) Los Angeles,

More information

ESTIMATING SYSTEMS ENGINEERING REUSE WITH THE CONSTRUCTIVE SYSTEMS ENGINEERING COST MODEL (COSYSMO 2.0) Jared Fortune

ESTIMATING SYSTEMS ENGINEERING REUSE WITH THE CONSTRUCTIVE SYSTEMS ENGINEERING COST MODEL (COSYSMO 2.0) Jared Fortune ESTIMATING SYSTEMS ENGINEERING REUSE WITH THE CONSTRUCTIVE SYSTEMS ENGINEERING COST MODEL (COSYSMO 2.0) by Jared Fortune A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN

More information

Appendix V Risk Management Plan Template

Appendix V Risk Management Plan Template Appendix V Risk Management Plan Template Version 2 March 7, 2005 This page is intentionally left blank. Version 2 March 7, 2005 Title Page Document Control Panel Table of Contents List of Acronyms Definitions

More information

Software cost estimation. Predicting the resources required for a software development process

Software cost estimation. Predicting the resources required for a software development process Software cost estimation Predicting the resources required for a software development process Ian Sommerville 2000 Software Engineering, 6th edition. Chapter 23 Slide 1 Objectives To introduce the fundamentals

More information

Business Architecture Guild Body of Knowledge Handbook 2.0

Business Architecture Guild Body of Knowledge Handbook 2.0 Guild Body of Knowledge Handbook 2.0 ------------------------ Section 1: Introduction The Guild has made this Introduction section of its Body of Knowledge Handbook 2.0 ( Handbook ) publicly available

More information

ISO, CMMI and PMBOK Risk Management: a Comparative Analysis

ISO, CMMI and PMBOK Risk Management: a Comparative Analysis ISO, CMMI and PMBOK Risk Management: a Comparative Analysis Cristine Martins Gomes de Gusmão Federal University of Pernambuco / Informatics Center Hermano Perrelli de Moura Federal University of Pernambuco

More information

Architecture Evaluation Methods: Introduction to ATAM

Architecture Evaluation Methods: Introduction to ATAM Architecture Evaluation Methods: Introduction to ATAM Contents What is ATAM? What are the outputs of ATAM? Phases and Steps of ATAM ATAM Running Example Introduction to ATAM 2 What is ATAM? ATAM (Architecture

More information

The level of complexity needed to

The level of complexity needed to The level of complexity needed to develop spacecraft systems and other emerging technologies require programs to develop risk management and risk planning techniques that can potentially identify schedule

More information

Your Software Quality is Our Business. INDEPENDENT VERIFICATION AND VALIDATION (IV&V) WHITE PAPER Prepared by Adnet, Inc.

Your Software Quality is Our Business. INDEPENDENT VERIFICATION AND VALIDATION (IV&V) WHITE PAPER Prepared by Adnet, Inc. INDEPENDENT VERIFICATION AND VALIDATION (IV&V) WHITE PAPER Prepared by Adnet, Inc. February 2013 1 Executive Summary Adnet is pleased to provide this white paper, describing our approach to performing

More information

Estimating Size and Effort

Estimating Size and Effort Estimating Size and Effort Dr. James A. Bednar jbednar@inf.ed.ac.uk http://homepages.inf.ed.ac.uk/jbednar Dr. David Robertson dr@inf.ed.ac.uk http://www.inf.ed.ac.uk/ssp/members/dave.htm SAPM Spring 2007:

More information

Chap 1. Introduction to Software Architecture

Chap 1. Introduction to Software Architecture Chap 1. Introduction to Software Architecture 1. Introduction 2. IEEE Recommended Practice for Architecture Modeling 3. Architecture Description Language: the UML 4. The Rational Unified Process (RUP)

More information

Partnering for Project Success: Project Manager and Business Analyst Collaboration

Partnering for Project Success: Project Manager and Business Analyst Collaboration Partnering for Project Success: Project Manager and Business Analyst Collaboration By Barbara Carkenord, CBAP, Chris Cartwright, PMP, Robin Grace, CBAP, Larry Goldsmith, PMP, Elizabeth Larson, PMP, CBAP,

More information

An Integrated Quality Assurance Framework for Specifying Business Information Systems

An Integrated Quality Assurance Framework for Specifying Business Information Systems An Integrated Quality Assurance Framework for Specifying Business Information Systems Frank Salger 1, Stefan Sauer 2, Gregor Engels 1,2 1 Capgemini sd&m AG, Carl-Wery-Str. 42, D-81739 München, Germany

More information

Background: Business Value of Enterprise Architecture TOGAF Architectures and the Business Services Architecture

Background: Business Value of Enterprise Architecture TOGAF Architectures and the Business Services Architecture Business Business Services Services and Enterprise and Enterprise This Workshop Two parts Background: Business Value of Enterprise TOGAF s and the Business Services We will use the key steps, methods and

More information

Malay A. Dalal Madhav Erraguntla Perakath Benjamin. Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A.

Malay A. Dalal Madhav Erraguntla Perakath Benjamin. Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A. AN INTRODUCTION TO USING PROSIM FOR BUSINESS PROCESS SIMULATION AND ANALYSIS Malay A. Dalal Madhav Erraguntla Perakath Benjamin Knowledge Based Systems, Inc. (KBSI) College Station, TX 77840, U.S.A. ABSTRACT

More information

PASTA Abstract. Process for Attack S imulation & Threat Assessment Abstract. VerSprite, LLC Copyright 2013

PASTA Abstract. Process for Attack S imulation & Threat Assessment Abstract. VerSprite, LLC Copyright 2013 2013 PASTA Abstract Process for Attack S imulation & Threat Assessment Abstract VerSprite, LLC Copyright 2013 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

More information

Improving Software Development Processes with Multicriteria Methods

Improving Software Development Processes with Multicriteria Methods Improving Software Development Processes with Multicriteria Methods Elena Kornyshova, Rébecca Deneckère, and Camille Salinesi CRI, University Paris 1 - Panthéon Sorbonne, 90, rue de Tolbiac, 75013 Paris,

More information

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group Practical Data Mining Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor Ei Francis Group, an Informs

More information

Requirements Analysis Concepts & Principles. Instructor: Dr. Jerry Gao

Requirements Analysis Concepts & Principles. Instructor: Dr. Jerry Gao Requirements Analysis Concepts & Principles Instructor: Dr. Jerry Gao Requirements Analysis Concepts and Principles - Requirements Analysis - Communication Techniques - Initiating the Process - Facilitated

More information

SOFTWARE ARCHITECTURE QUALITY EVALUATION

SOFTWARE ARCHITECTURE QUALITY EVALUATION SOFTWARE ARCHITECTURE QUALITY EVALUATION APPROACHES IN AN INDUSTRIAL CONTEXT Frans Mårtensson Blekinge Institute of Technology Licentiate Dissertation Series No. 2006:03 School of Engineering Software

More information

Hathaichanok Suwanjang and Nakornthip Prompoon

Hathaichanok Suwanjang and Nakornthip Prompoon Framework for Developing a Software Cost Estimation Model for Software Based on a Relational Matrix of Project Profile and Software Cost Using an Analogy Estimation Method Hathaichanok Suwanjang and Nakornthip

More information

An Overview of IEEE Software Engineering Standards and Knowledge Products

An Overview of IEEE Software Engineering Standards and Knowledge Products Paul R. Croll Chair, IEEE SESC Computer Sciences Corporation pcroll@csc.com An Overview of IEEE Software Engineering Standards and Knowledge Products Objectives Provide an introduction to The IEEE Software

More information

The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into

The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material,

More information

Develop Project Charter. Develop Project Management Plan

Develop Project Charter. Develop Project Management Plan Develop Charter Develop Charter is the process of developing documentation that formally authorizes a project or a phase. The documentation includes initial requirements that satisfy stakeholder needs

More information

Software Migration Project Cost Estimation using COCOMO II and Enterprise Architecture Modeling

Software Migration Project Cost Estimation using COCOMO II and Enterprise Architecture Modeling Software Migration Project Cost Estimation using COCOMO II and Enterprise Architecture Modeling Alexander Hjalmarsson 1, Matus Korman 1 and Robert Lagerström 1, 1 Royal Institute of Technology, Osquldas

More information

Cost Estimation Driven Software Development Process

Cost Estimation Driven Software Development Process Cost Estimation Driven Software Development Process Orsolya Dobán, András Pataricza Budapest University of Technology and Economics Department of Measurement and Information Systems Pázmány P sétány 1/D

More information

NASCIO EA Development Tool-Kit Solution Architecture. Version 3.0

NASCIO EA Development Tool-Kit Solution Architecture. Version 3.0 NASCIO EA Development Tool-Kit Solution Architecture Version 3.0 October 2004 TABLE OF CONTENTS SOLUTION ARCHITECTURE...1 Introduction...1 Benefits...3 Link to Implementation Planning...4 Definitions...5

More information

Improving proposal evaluation process with the help of vendor performance feedback and stochastic optimal control

Improving proposal evaluation process with the help of vendor performance feedback and stochastic optimal control Improving proposal evaluation process with the help of vendor performance feedback and stochastic optimal control Sam Adhikari ABSTRACT Proposal evaluation process involves determining the best value in

More information

NIST Cloud Computing Program Activities

NIST Cloud Computing Program Activities NIST Cloud Computing Program Overview The NIST Cloud Computing Program includes Strategic and Tactical efforts which were initiated in parallel, and are integrated as shown below: NIST Cloud Computing

More information

Software Engineering. Dilbert on Project Planning. Overview CS / COE 1530. Reading: chapter 3 in textbook Requirements documents due 9/20

Software Engineering. Dilbert on Project Planning. Overview CS / COE 1530. Reading: chapter 3 in textbook Requirements documents due 9/20 Software Engineering CS / COE 1530 Lecture 4 Project Management Dilbert on Project Planning Overview Reading: chapter 3 in textbook Requirements documents due 9/20 1 Tracking project progress Do you understand

More information

A Characterization Taxonomy for Integrated Management of Modeling and Simulation Tools

A Characterization Taxonomy for Integrated Management of Modeling and Simulation Tools A Characterization Taxonomy for Integrated Management of Modeling and Simulation Tools Bobby Hartway AEgis Technologies Group 631 Discovery Drive Huntsville, AL 35806 256-922-0802 bhartway@aegistg.com

More information

Project Risk Management

Project Risk Management Project Risk Management Study Notes PMI, PMP, CAPM, PMBOK, PM Network and the PMI Registered Education Provider logo are registered marks of the Project Management Institute, Inc. Points to Note Risk Management

More information

Safe and Simple Software Cost Analysis Barry Boehm, USC Everything should be as simple as possible, but no simpler.

Safe and Simple Software Cost Analysis Barry Boehm, USC Everything should be as simple as possible, but no simpler. Safe and Simple Software Cost Analysis Barry Boehm, USC Everything should be as simple as possible, but no simpler. -Albert Einstein Overview There are a number of simple software cost analysis methods,

More information

Software Development Life Cycle (SDLC)

Software Development Life Cycle (SDLC) Software Development Life Cycle (SDLC) Supriyo Bhattacharjee MOF Capability Maturity Model (CMM) A bench-mark for measuring the maturity of an organization s software process CMM defines 5 levels of process

More information

TDWI strives to provide course books that are content-rich and that serve as useful reference documents after a class has ended.

TDWI strives to provide course books that are content-rich and that serve as useful reference documents after a class has ended. Previews of TDWI course books are provided as an opportunity to see the quality of our material and help you to select the courses that best fit your needs. The previews can not be printed. TDWI strives

More information

Set-Based Design: A Decision-Theoretic Perspective

Set-Based Design: A Decision-Theoretic Perspective Set-Based Design: A Decision-Theoretic Perspective Chris Paredis, Jason Aughenbaugh, Rich Malak, Steve Rekuc Product and Systems Lifecycle Management Center G.W. Woodruff School of Mechanical Engineering

More information

Risk Management approach for Cultural Heritage Projects Based on Project Management Body of Knowledge

Risk Management approach for Cultural Heritage Projects Based on Project Management Body of Knowledge 1 Extreme Heritage, 2007 Australia, 19-21 July 2007, James Cook University, Cairns, Australia Theme 6: Heritage disasters and risk preparedness approach for Cultural Heritage Projects Based on Project

More information

IT Financial Management and Cost Recovery

IT Financial Management and Cost Recovery WHITE PAPER November 2010 IT Financial Management and Cost Recovery Patricia Genetin Sr. Principal Consultant/CA Technical Sales David Messineo Sr. Services Architect/CA Services Table of Contents Executive

More information

Knowledge Area Inputs, Tools, and Outputs. Knowledge area Process group/process Inputs Tools Outputs

Knowledge Area Inputs, Tools, and Outputs. Knowledge area Process group/process Inputs Tools Outputs HUMAN RESOURCE MANAGEMENT Organizational planning Staff Acquisition Project interfaces such as organizational interfaces, technical interfaces and interpersonal interfaces. Staffing requirements Staffing

More information

Project Management. [Student s Name] [Name of Institution]

Project Management. [Student s Name] [Name of Institution] 1 Paper: Assignment Style: Harvard Pages: 10 Sources: 7 Level: Master Project Management [Student s Name] [Name of Institution] 2 Project Management Introduction The project management also known as management

More information

A Review of the Impact of Requirements on Software Project Development Using a Control Theoretic Model

A Review of the Impact of Requirements on Software Project Development Using a Control Theoretic Model J. Software Engineering & Applications, 2010, 3, 852-857 doi:10.4236/jsea.2010.39099 Published Online September 2010 (http://www.scirp.org/journal/jsea) A Review of the Impact of Requirements on Software

More information

Session 4. System Engineering Management. Session Speaker : Dr. Govind R. Kadambi. M S Ramaiah School of Advanced Studies 1

Session 4. System Engineering Management. Session Speaker : Dr. Govind R. Kadambi. M S Ramaiah School of Advanced Studies 1 Session 4 System Engineering Management Session Speaker : Dr. Govind R. Kadambi M S Ramaiah School of Advanced Studies 1 Session Objectives To learn and understand the tasks involved in system engineering

More information

SOFTWARE DEVELOPMENT STANDARD FOR SPACECRAFT

SOFTWARE DEVELOPMENT STANDARD FOR SPACECRAFT SOFTWARE DEVELOPMENT STANDARD FOR SPACECRAFT Mar 31, 2014 Japan Aerospace Exploration Agency This is an English translation of JERG-2-610. Whenever there is anything ambiguous in this document, the original

More information

Risk Workshop Overview. MOX Safety Fuels the Future

Risk Workshop Overview. MOX Safety Fuels the Future Risk Workshop Overview RISK MANAGEMENT PROGRAM SUMMARY CONTENTS: Control Account Element Definition ESUA Form Basis of Estimate Uncertainty Calculation Management Reserve 1. Overview 2. ESUA Qualification

More information

A Model for Effective Asset Re-use in Software Projects

A Model for Effective Asset Re-use in Software Projects A Model for Effective Asset Re-use in Software Projects Abhay Joshi Abstract Software Asset re-use has the potential to enhance the quality and reduce the time to market of software projects. However,

More information

PMI Risk Management Professional (PMI-RMP) Exam Content Outline

PMI Risk Management Professional (PMI-RMP) Exam Content Outline PMI Risk Management Professional (PMI-RMP) Exam Content Outline Project Management Institute PMI Risk Management Professional (PMI-RMP) Exam Content Outline Published by: Project Management Institute,

More information

CPM -100: Principles of Project Management

CPM -100: Principles of Project Management CPM -100: Principles of Project Management Lesson E: Risk and Procurement Management Presented by Sam Lane samlane@aol.com Ph: 703-883-7149 Presented at the IPM 2002 Fall Conference Prepared by the Washington,

More information

Deducing software process improvement areas from a COCOMO II-based productivity measurement

Deducing software process improvement areas from a COCOMO II-based productivity measurement Deducing software process improvement areas from a COCOMO II-based productivity measurement Lotte De Rore, Monique Snoeck, Geert Poels, Guido Dedene Abstract At the SMEF2006 conference, we presented our

More information

A Variability Viewpoint for Enterprise Software Systems

A Variability Viewpoint for Enterprise Software Systems 2012 Joint Working Conference on Software Architecture & 6th European Conference on Software Architecture A Variability Viewpoint for Enterprise Software Systems Matthias Galster University of Groningen,

More information

Requirements engineering

Requirements engineering Learning Unit 2 Requirements engineering Contents Introduction............................................... 21 2.1 Important concepts........................................ 21 2.1.1 Stakeholders and

More information

Information Technology Project Oversight Framework

Information Technology Project Oversight Framework i This Page Intentionally Left Blank i Table of Contents SECTION 1: INTRODUCTION AND OVERVIEW...1 SECTION 2: PROJECT CLASSIFICATION FOR OVERSIGHT...7 SECTION 3: DEPARTMENT PROJECT MANAGEMENT REQUIREMENTS...11

More information

A Risk Management System Framework for New Product Development (NPD)

A Risk Management System Framework for New Product Development (NPD) 2011 International Conference on Economics and Finance Research IPEDR vol.4 (2011) (2011) IACSIT Press, Singapore A Risk Management System Framework for New Product Development (NPD) Seonmuk Park, Jongseong

More information

Location: [North America] [United States] [Home Working, United States]

Location: [North America] [United States] [Home Working, United States] Architect II Location: [North America] [United States] [Home Working, United States] Category: Information Technology Job Type: Fixed term, Full-time PURPOSE OF POSITION: The Architect II role is expected

More information

NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation

NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation NCOE whitepaper Master Data Deployment and Management in a Global ERP Implementation Market Offering: Package(s): Oracle Authors: Rick Olson, Luke Tay Date: January 13, 2012 Contents Executive summary

More information

Software Engineering and the Systems Approach: A Conversation with Barry Boehm

Software Engineering and the Systems Approach: A Conversation with Barry Boehm IGI PUBLISHING ITJ4305 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Int l Journal of Tel: Information 717/533-8845; Technologies Fax 717/533-8661; and the Systems URL-http://www.igi-global.com

More information

A SOFTWARE PROJECT DYNAMICS MODEL FOR PROCESS COST, SCHEDULE AND RISK ASSESSMENT. Raymond Joseph Madachy. A Dissertation Presented to the

A SOFTWARE PROJECT DYNAMICS MODEL FOR PROCESS COST, SCHEDULE AND RISK ASSESSMENT. Raymond Joseph Madachy. A Dissertation Presented to the A SOFTWARE PROJECT DYNAMICS MODEL FOR PROCESS COST, SCHEDULE AND RISK ASSESSMENT by Raymond Joseph Madachy A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA

More information

Extending Change Impact Analysis Approach for Change Effort Estimation in the Software Development Phase

Extending Change Impact Analysis Approach for Change Effort Estimation in the Software Development Phase Extending Change Impact Analysis Approach for Change Effort Estimation in the Software Development Phase NAZRI KAMA, MEHRAN HALIMI Advanced Informatics School Universiti Teknologi Malaysia 54100, Jalan

More information

MKS Integrity & CMMI. July, 2007

MKS Integrity & CMMI. July, 2007 & CMMI July, 2007 Why the drive for CMMI? Missed commitments Spiralling costs Late delivery to the market Last minute crunches Inadequate management visibility Too many surprises Quality problems Customer

More information

Space project management

Space project management ECSS-M-ST-80C Space project management Risk management ECSS Secretariat ESA-ESTEC Requirements & Standards Division Noordwijk, The Netherlands Foreword This Standard is one of the series of ECSS Standards

More information

Motivations. spm - 2014 adolfo villafiorita - introduction to software project management

Motivations. spm - 2014 adolfo villafiorita - introduction to software project management Risk Management Motivations When we looked at project selection we just took into account financial data In the scope management document we emphasized the importance of making our goals achievable, i.e.

More information

Entire contents 2011 Praetorian. All rights reserved. Information Security Provider and Research Center www.praetorian.com

Entire contents 2011 Praetorian. All rights reserved. Information Security Provider and Research Center www.praetorian.com Entire contents 2011 Praetorian. All rights reserved. Information Security Provider and Research Center www.praetorian.com Threat Modeling "Threat modeling at the design phase is really the only way to

More information

Project Plan. Online Book Store. Version 1.0. Vamsi Krishna Mummaneni. CIS 895 MSE Project KSU. Major Professor. Dr.Torben Amtoft

Project Plan. Online Book Store. Version 1.0. Vamsi Krishna Mummaneni. CIS 895 MSE Project KSU. Major Professor. Dr.Torben Amtoft Online Book Store Version 1.0 Vamsi Krishna Mummaneni CIS 895 MSE Project KSU Major Professor Dr.Torben Amtoft 1 Table of Contents 1. Task Breakdown 3 1.1. Inception Phase 3 1.2. Elaboration Phase 3 1.3.

More information

A Capability Maturity Model (CMM)

A Capability Maturity Model (CMM) Software Development Life Cycle (SDLC) and Development Methods There are some enterprises in which a careful disorderliness is the true method. Herman Melville Capability Maturity Model (CMM) A Capability

More information

Spend Enrichment: Making better decisions starts with accurate data

Spend Enrichment: Making better decisions starts with accurate data IBM Software Industry Solutions Industry/Product Identifier Spend Enrichment: Making better decisions starts with accurate data Spend Enrichment: Making better decisions starts with accurate data Contents

More information

Risk Knowledge Capture in the Riskit Method

Risk Knowledge Capture in the Riskit Method Risk Knowledge Capture in the Riskit Method Jyrki Kontio and Victor R. Basili jyrki.kontio@ntc.nokia.com / basili@cs.umd.edu University of Maryland Department of Computer Science A.V.Williams Building

More information

COCOMO and SCORM: Cost Estimation Model for Web-Based Training. Roger Smith U.S. Army PEO STRI

COCOMO and SCORM: Cost Estimation Model for Web-Based Training. Roger Smith U.S. Army PEO STRI COCOMO and SCORM: Cost Estimation Model for Web-Based Training Roger Smith U.S. Army PEO STRI 1 COSCOMO Prototype Project: Concept It is challenging for both sponsors and developers to estimate the expected

More information

The COCOMO II Estimating Model Suite

The COCOMO II Estimating Model Suite The COCOMO II Estimating Model Suite Barry Boehm, Chris Abts, Jongmoon Baik, Winsor Brown, Sunita Chulani, Cyrus Fakharzadeh, Ellis Horowitz and Donald Reifer Center for Software Engineering University

More information

Negative Risk. Risk Can Be Positive. The Importance of Project Risk Management

Negative Risk. Risk Can Be Positive. The Importance of Project Risk Management The Importance of Project Risk Management Project risk management is the art and science of identifying, analyzing, and responding to risk throughout the life of a project and in the best interests t of

More information

Oracle Real Time Decisions

Oracle Real Time Decisions A Product Review James Taylor CEO CONTENTS Introducing Decision Management Systems Oracle Real Time Decisions Product Architecture Key Features Availability Conclusion Oracle Real Time Decisions (RTD)

More information

Procurement Programmes & Projects P3M3 v2.1 Self-Assessment Instructions and Questionnaire. P3M3 Project Management Self-Assessment

Procurement Programmes & Projects P3M3 v2.1 Self-Assessment Instructions and Questionnaire. P3M3 Project Management Self-Assessment Procurement Programmes & Projects P3M3 v2.1 Self-Assessment Instructions and Questionnaire P3M3 Project Management Self-Assessment Contents Introduction 3 User Guidance 4 P3M3 Self-Assessment Questionnaire

More information

WBS, Estimation and Scheduling. Adapted from slides by John Musser

WBS, Estimation and Scheduling. Adapted from slides by John Musser WBS, Estimation and Scheduling Adapted from slides by John Musser 1 Today Work Breakdown Structures (WBS) Estimation Network Fundamentals PERT & CPM Techniques Gantt Charts 2 Estimation Predictions are

More information

Planning of Project Work (IS PM 6. Lecture, 2011 Spring)

Planning of Project Work (IS PM 6. Lecture, 2011 Spring) Planning of Project Work In planning of project work are in the context of information system development project under attention information system development processes and needed resources. Pictorially

More information