CHARACTERIZING THE IMPACT OF REQUIREMENTS VOLATILITY ON SYSTEMS ENGINEERING EFFORT

1

University of Southern California

Center for Systems and Software Engineering

November 2010Mauricio Peña

Dr. Ricardo Valerdi

CHARACTERIZING THE IMPACT OF

REQUIREMENTS VOLATILITY ON

SYSTEMS ENGINEERING EFFORT

2

University of Southern California 25th Annual COCOMO Forum


Agenda

• Overview

• Introduction and Background

• Implications to COSYSMO

• Method

• Causal Model

• Survey Results

• Conclusions

• Next Steps

3



Motivation

“Requirements are the foundation of the project. They form the basis for design, manufacture, test and operations….changes in requirements later in the development cycle can have a significant cost impact, possibly resulting in cancellation”

INCOSE Systems Engineering Handbook (2006)

4



Overview of Research Findings• Field research validated findings from prior studies:

– Requirements volatility is linked to an increase in rework and project size

– The impact of changing a requirement increases the later the change occurs in the system lifecycle

• The research provided additional insights:

1. Causal model linking volatility to a number of technical, organizational and contextual factors

2. The level of volatility is a function of lifecycle phase

3. Respondents from S/W intensive projects tend to expect more volatility than those who work on H/W intensive systems

4. There are spikes in volatility after the transitions between lifecycle phases

5. Requirements changes early in the lifecycle may not be considered “volatility”

5



0

100

200

300

400

500

F M A M J J A S O N

NewModified

Deleted

Time (Months)

# of

Req

uire

men

ts

Requirements Volatility Definitions

Requirements volatility is the % change in requirements (added, deleted, and modified) over a given time interval

Also known as:

Requirements creep: An increase in scope and/or number of system requirements

Requirements churn: Instability in the requirements set – requirements are frequently modified or reworked without necessarily resulting in an increase in the total number of requirements

Costello, R. and Liu, D. (1995). “Metrics for Requirements Engineering,” Journal of Systems and Software. Vol 29 (No. 1), pp. 39-63MIL-STD-498. 1994. Software Development and Documentation. U.S. DoD

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

J F M A M J J A S O N

Actual Volatility

Expected Volatility

Volatility Metrics (Monthly)

% o

f to

tal R

equi

rem

ents

Cha

ngin

g

6



SE Leading Indicators Guide

Leading Indicators are defined as “measures for evaluating the effectiveness of the systems engineering activities on a program in a manner that provides information about impacts that are likely to affect the system or program performance objectives.”

Rhodes, D., Valerdi, R., and Roedler, G. (2009). “Systems engineering leading indicators for assessing program and technical effectiveness.” Systems Engineering Vol. 12 (No. 1), pp 21-35.

7



Requirements Trends as a Systems Engineering Leading Indicator

• Evaluates trends in the growth and change of the system requirements

• It helps to determine the stability and completeness of the system requirements which could potentially impact project performance

Systems Engineering Leading Indicators Guide, Version 2.0, 2010

8



Implications to COSYSMO

• During the development of COSYSMO, volatility was identified as a relevant adjustment factor to the model’s size drivers

• However, there was insufficient data to incorporate volatility effects into the initial version of the model

• The primary objective of the research is to complete the requirements volatility extension to COSYSMO within the existing structure and scope of the model

Valerdi, R. (2005). The constructive systems engineering cost model (COSYSMO). Doctoral Dissertation. University of Southern California, Industrial and Systems Engineering Department.

9



COSYSMO Volatility Factor

COSYSMO

SizeDrivers

EffortMultipliers

Effort

Calibration

# Requirements# Interfaces# Scenarios# Algorithms

- Application factors-8 factors

- Team factors-6 factors

ReuseCategories

Volatility Factor

COSYSMO

SizeDrivers

EffortMultipliers

Effort

Calibration




ReuseCategories

Volatility Factor

COSYSMO

SizeDrivers

EffortMultipliers

Effort

Calibration




ReuseCategories

Volatility Factor

Fortune, J. (2009). Estimating systems engineering reuse with the constructive systems engineering cost model (COSYSMO 2.0). Doctoral Dissertation. University of Southern California, Industrial and Systems Engineering Department.

10



Method

8USC-CSE Annual Research Review – 3/17/03

Analyze Existingliterature

1

2

3

4

5

6

7

PerformBehavioral Analysis

Identify RelativeSignificance

Perform Expert-Judgement, DelphiAssessment

Gather Project Data

Determine BayesianA-Posteriori Update

Gather more data;refine model

A-PRIORI MODEL+

SAMPLING DATA=

A-POSTERIORI MODEL

First Phase of the Study:• Review of relevant literature

• Data collected through field research: surveys and discussions conducted at industry/academic conferences and workshops

Boehm, B. (1981). Software Engineering Economics. Prentice Hall.

11



Literature Background

• Most of the requirements volatility research to date has been focused on software systems

• Various research methods have been utilized to investigate the causes and effects of requirement volatility, including:

5

23

21

Simulation Model

Survey / Model

Survey

Project data analysis

Interviews

There is still a lack of empirical data on the quantitative impact of requirements volatility on for a broader base of engineering projects

12



Cost Commitment on Projects

Detail Designand

Development

100

25

50

75

Conceptual-Preliminary

Design

Constructionand/or

Production

System Use, Phaseout,and Disposal

NEED

% Commitment to Technology,Configuration, Performance, Cost, etc.

Cost Incurred

System-Specific Knowledge

Ease of Change

Blanchard and Fabrycky (1998), Systems Engineering & Analysis, Prentice Hall, 1998

Changes to the System are more difficult to implement the later they occur in the lifecycle

13



Causal Model (normative)

Based on the review of the literature, a causal model was developed that relates technical, organizational and contextual project factors to requirements volatility

Survey results were used to rank the level of subject-matter expert agreement with each of the postulated causes of requirements volatility.

Changes in org. structure /

leadership / process

Requirements volatility +/-

+

+/-

+/-Rework

+/-

-

SE Productivity

Number of Sys

Requirements

Project Schedule

SE Project Effort

SE CostProject Cost

Quality

Customer Satisfaction

Customer-Driven Scope Changes

SE Process Maturity

Technology Maturity

+/- (Valerdi 2005)

(Valerdi 2005)+

+

-

(Honour, 2004)(Ferreira, et al 2009)

(Valerdi 2005)(Honour, 2004)

(Houston, 2000)(Ferreira 2002)

(Charette et al,2003)

+/-

+


(Houston, 2000)Zowghi et al. (2000)(Ferreira, et al 2009)

(Houston, 2000)(Ferreira 2002)(Charette et al,2003)

+/-(Valerdi 2005)

(Valerdi 2005)

+/-

Poor Understanding of

the System & Customer needs

Experienced staff

Contextual / Environmental

changes

1 2

Changes in co-dependent

Systems

3 5

76

Changes in COTS

Products

8 9

4

(Jones 1994)

+

(Jones 1994) (Kotonya and Sommerville 1998)

+

(2010 USC ARR Survey)

+

(Ferreira 2002)

-

(GAO, 2004)(Jones 1994)

-

(Curtis et al. 1988)Zowghi et al. (2000)(Kotonya and Sommerville 1998)

+-

(Ferreira, 2002)(GAO, 2004)

+

(Kotonya and Sommerville 1998)

+


Changes in org. structure /

leadership / process

Requirements volatility +/-

+

+/-

+/-Rework

+/-

-

SE Productivity

Number of Sys

Requirements

Project Schedule

SE Project Effort

SE CostProject Cost

Quality

Customer Satisfaction

Customer-Driven Scope Changes

SE Process Maturity

SE Process Maturity

Technology Maturity

Technology Maturity

+/- (Valerdi 2005)

(Valerdi 2005)+

+

-

(Honour, 2004)(Ferreira, et al 2009)

(Valerdi 2005)(Honour, 2004)

(Houston, 2000)(Ferreira 2002)


+/-

+


(Houston, 2000)Zowghi et al. (2000)(Ferreira, et al 2009)

(Houston, 2000)(Ferreira 2002)(Charette et al,2003)

+/-(Valerdi 2005)

(Valerdi 2005)

+/-





Experienced staff


changes


changes

1 2

Changes in co-dependent

Systems

3 5

76

Changes in COTS

Products

Changes in COTS

Products

8 9

4

(Jones 1994)

+

(Jones 1994) (Kotonya and Sommerville 1998)

+


+

(Ferreira 2002)

-

(GAO, 2004)(Jones 1994)

-

(Curtis et al. 1988)Zowghi et al. (2000)(Kotonya and Sommerville 1998)

+-

(Ferreira, 2002)(GAO, 2004)

+

(Kotonya and Sommerville 1998)

+


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Exploratory Survey

• Developed to gather the perspectives of subject-matter experts on the causes, impacts, and expected level of requirements volatility for a given system of interest

• Piloted at the 2010 USC-CSSE Annual Research Review

• Incorporated feedback and administered the survey at the 2010 Lean Advancement Initiative (LAI) knowledge exchange event in Dana Point, CA

• Organizations represented: – The Aerospace Corporation, Northrop Grumman Corporation

– The Boeing Company, Softstar Systems, Raytheon

– United Launch Alliance, Massachusetts Institute of Technology, University of Southern California, and

– United States Army

– United States Navy

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>20%

>20%

>20% >20%

10-20%

10-20%

10-20%

10-20%

5-10%

5-10%

5-10%

5-10%

<5%<5%

<5%

<5%

don't knowdon't know

don't know don't know

0

5

10

15

20

Conceptualize Development Operational Test &Evaluation

Transition toOperation

Expected Requirements Volatility (%)

Num

ber

of R

espo

nden

ts

Expected Level of Volatility

Most respondents expect >20% volatility during the conceptualize phase of the project, decreasing to <5% in the transition to operation phase

16



Impact of Hardware/Software Project Breakdown on Expected Volatility

Operational Test & Evaluation Lifecycle Phase Transition to Operation Lifecycle Phase

0%

20%

40%

60%

80%

100%

<5% 5-10% 10-20% >20%

Expected Requirements Volatility (%)

75% Hardware, 25% Software 50% Hardware, 50% Software

25% Hardware, 75% Software

% o

f R

espo

nden

ts p

er C

ateg

ory

0%

20%

40%

60%

80%

100%

<5% 5-10% 10-20% >20%

Expected Requirements Volatility (%)75% Hardware, 25% Software 50% Hardware, 50% Software

25% Hardware, 75% Software

% o

f R

espo

nden

ts p

er C

ateg

ory

Respondents from S/W intensive projects tend to expect more volatility later in the lifecycle

17



0%

20%

40%

60%

80%

100%

Re-work of work products Project size (Total # ofrequirements)

% o

f re

spon

dent

s

Impacts of Volatility

• In general, results of the survey support observations from the literature and causal model

• Most respondents stated that requirements volatility will cause a moderate to large increase in the number of system requirements and rework

Moderate decrease Moderate Increase

No impact Large Increase

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Survey Exercise

• Survey Exercise administered during the 2010 Practical Software and Systems Measurement Conference

• Participants were asked to:1. Draw a requirements volatility profile across the lifecycle

phases covered by COSYSMO

2. Draw an “ease of change” profile across the same life cycle phases to determine the volatility weighting factor

3. Discuss variation in 1 and 2 above for:

1. Large and Small Projects

2. Hardware and Software Projects

3. Development and Recurring Projects

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Expected Requirements Volatility Profile

Conceptualize Development Operational Test & Evaluation

Transition to Operation

% o

f R

equ

irem

ents

, A

dde

d, D

elet

ed o

r M

odi

fied

30%

15%

4 out of 9 participants indicated that requirements changes should not be considered volatility during the conceptualize phase

Localized peaks in volatility due to the transitions between lifecycle phases

Profile Representative of Participant Feedback

No significant differences between type of projects

20



Ease of Change Profile



Eas

e of

Cha

nge

1

0.5

0.1

0

0.2

0.3

0.4

0.9

0.6

0.7

0.8

Representative of Participant Feedback



Eas

e of

Cha

nge

1

0.5

0.1

0

0.2

0.3

0.4

0.9

0.6

0.7

0.8

Representative of Participant Feedback

Cost Penalty defined as 1 / ease of change

Average Ease of Change Factor (Estimated)

Average Cost Penalty (Estimated)

0.8 0.4 0.1 0.05

1.25 2.5 10 20

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Conclusions

• Field research validated the literature findings that:

– Link volatility to an increase in rework and project size

– Predict a cost penalty due to late requirements changes

• Additional insights developed through the research:

1. Causal model linking volatility to a number of technical, organizational and contextual factors

2. The level of volatility is a function of lifecycle phase

3. The presence of localized peaks in requirements volatility after the transitions between lifecycle phases

4. Feedback that suggests requirements changes during the conceptualize phase should not be labeled as volatility

5. Respondents from S/W intensive projects tend to expect more volatility later in the lifecycle than those who work on H/W oriented systems

22



Next Steps

• The findings from the field research will be used to further define the volatility extension to COSYSMO

– Additional work is required to understand the cost penalty of late requirements as it relates to systems engineering effort

– The point in the lifecycle during which volatility starts to be measured and accounted for also needs to be further defined

– Interviews with industry experts and mini-case studies will be conducted to validate the usefulness of the causal model

• Industry data will be collected to quantify the impact of requirements changes on systems engineering effort

23



References• Boehm, B. (1991). Software Risk Management: Principles and Practices. IEEE Software 8(1), pp 32-41.• Blanchard, B. and Fabrycky, W. (1998). Systems Engineering & Analysis, Prentice Hall, New York.• Ferreira, S., Collofello, J., Shunk, D., and Mackulak, G. (2009). “Understanding the effects of requirements

volatility in software engineering by using analytical modeling and software process simulation.” The Journal of Systems and Software. Vol. 82, pp 1568-1577.

• Fortune, J. (2009). Estimating systems engineering reuse with the constructive systems engineering cost model (COSYSMO 2.0). Doctoral Dissertation. University of Southern California, Industrial and Systems Engineering Department.

• General Accounting Office (2004). Stronger Management Practices are Needed to Improve DOD’s Software-intensive Weapon Acquisitions (GAO-04-393). Defense Acquisitions

• Honour, E. (2004). “Understanding the Value of Systems Engineering.” INCOSE 14th Annual International Symposium: Systems Engineering Managing Complexity and Change. Toulouse, France

• Houston, Dan X. (2000). A Software Project Simulation Model for Risk Management, Ph.D. Dissertation, Arizona State University

• INCOSE Systems Engineering Handbook, Version 3, INCOSE, June 2006• ISO/IEC (2008). ISO/IEC 15288:2008 (E) Systems Engineering - System Life Cycle Processes.• Kotonya, G., Sommerville, I., (1998). Requirements Engineering: Processes and Techniques. John Wiley and

Sons, Ltd.• MIL-STD-498 (1994). Software Development and Documentation. U.S. Department of Defense.• Roedler, G. and Rhodes, D. (2007). Systems engineering leading indicators guide. Version 1. Massachusetts

Institute of Technology, INCOSE, and PSM.• Valerdi, R. (2005). The constructive systems engineering cost model (COSYSMO). Doctoral Dissertation.

University of Southern California, Industrial and Systems Engineering Department.• Zowghi, D. and Nurmuliani, N. (2002). A Study of the Impact of Requirements Volatility on Software Project

Performance. Proceedings of the Ninth Asia-Pacific Software Engineering Conference

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Call for Participation

• In order to complete the requirements volatility extension of COSYSMO, we are seeking industry data for engineering projects in terms of:

– Systems engineering effort actuals (labor hours)

– Requirements volatility: the number of requirements, added, deleted, and modified added after the requirements baseline

• By providing these data your organization will benefit by:– Improving its ability to estimate the impact of requirements changes on

project cost

– Calibrating and tailoring the updated Model for your application domain

• USC-CSSE and LAI at MIT have proven processes in place to ensure the confidentiality and protection of the data with its Corporate Affiliates and Consortium Members

• Contact:Mauricio E. Pena [[email protected]]

Ricardo Valerdi [[email protected]]

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CHARACTERIZING THE IMPACT OF REQUIREMENTS VOLATILITY ON SYSTEMS ENGINEERING EFFORT

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