Software Process Dynamics III

1
Software Process Dynamics III
Ray Madachy madachy_at_usc.edu CSCI 510 November
17, 2006
2
Outline

• System dynamics modeling review
• Spiral hybrid process model for
  Software-Intensive System of Systems (SISOS)
• References

3
Background

• The evaluation of process strategies for the
  architecting and engineering of complex systems
  involves many interrelated factors.
• Effective systems and software engineering
  requires a balanced view of technology, business
  or mission goals, and people.
• System dynamics is a rich and integrative
  simulation framework used to quantify the complex
  interactions and the strategy tradeoffs between
  cost, schedule, quality and risk.

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Systems and Software Engineering Challenges

• What to build? Why? How well?
• Stakeholder needs balancing, business case
• Who to build it? Where?
• Staffing, organizing, outsourcing
• How to build? When in what order?
• Construction processes, methods, tools,
  components, increments
• How to adapt to change?
• In user needs, technology, marketplace
• How much is enough?
• Functionality, quality, specifying, prototyping,
  test

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System Dynamics Notation

• System represented by x(t) f(x,p).
• x vector of levels (state variables), p set of
  parameters
• Legend
• Example system

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Model Elements
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Model Elements (continued)
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Outline

• System dynamics modeling review
• Spiral hybrid process model for
  Software-Intensive System of Systems (SISOS)
• References

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Spiral Hybrid Process Introduction

• The spiral lifecycle is being extended to address
  new challenges for Software-Intensive Systems of
  Systems (SISOS), such as coping with rapid change
  while simultaneously assuring high dependability
• A hybrid plan-driven and agile process has been
  outlined to address these conflicting challenges
  with the need to rapidly field incremental
  capabilities
• A system-of-systems (SOS) integrates multiple
  independently-developed systems and is very
  large, dynamically evolving, unprecedented, with
  emergent requirements and behaviors
• However, traditional static approaches cannot
  capture dynamic feedback loops and interacting
  phenomena that cause real-world complexity (e.g.
  hybrid processes, project volatility, increment
  overlap and resource contention, schedule
  pressure, slippages, communication overhead,
  slack, etc.)
• A system dynamics model is being developed to
  assess the incremental hybrid process and support
  project decision-making
• Both the hybrid process and simulation model are
  being evolved on a very large scale incremental
  project for a SISOS (U.S. Army Future Combat
  Systems)

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Future Combat Systems (FCS) Network
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Scalable Spiral Model Increment Activities

• Organize development into plan-driven increments
  with stable specs
• Agile team watches for and assesses changes, then
  negotiates changes so next increment hits the
  ground running
• Try to prevent usage feedback from destabilizing
  current increment
• Three team cycle plays out from one increment to
  the next

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Spiral Hybrid Model Features

• Estimates cost and schedule for multiple
  increments of a hybrid process that uses three
  specialized teams (agile re-baseliners,
  developers, VVers) per the scalable spiral
  model
• Considers changes due to external volatility and
  feedback from user-driven change requests
• Deferral policies and team sizes can be
  experimented with
• Includes tradeoffs between cost and the timing of
  changes within and across increments, length of
  deferral delays, and others

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Model Input Control Panel
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Model Overview

• Built around a cyclic flow chain for capabilities
• Arrayed for multiple increments
• Each team is represented with a level and
  corresponding staff allocation rate
• Changes arrive a-periodically via the volatility
  trends time function and flow into the level for
  capability changes
• Changes are processed by the agile team and
  allocated to increments per the deferral policies
• Constant or variable staffing for the agile team
• For each increment the required capabilities are
  developed into developed capabilities and then
  VVed into V Ved capabilities
• Productivities and team sizes for development and
  VV calculated with a Dynamic COCOMO variant and
  continuously updated for scope changes
• Flow rates between capability changes and V
  Ved capabilities are bi-directional for
  capability kickbacks sent back up the chain
• User-driven changes from the field are identified
  as field issues that flow back into the
  capability changes

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Volatility Cost Functions

• The volatility effort multiplier for construction
  effort and schedule is an aggregate multiplier
  for volatility from different sources (e.g. COTS,
  mission, etc.) relative to the original baseline
  for increment
• The lifecycle timing effort multiplier models
  increased development cost the later a change
  comes in midstream during an increment

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Staffing Parameters
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Staffing Profile for Increment 1 Baseline
(Unperturbed)
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Sample Response to Volatility

• An unanticipated change occurs at month 8 shown
  as a volatility trend 1 pulse
• It flows into capability changes 1 which
  declines to zero as the agile team processes the
  change
• The change is non-deferrable for increment 1 so
  total capabilities 1 increases
• Development team staff size dynamically responds
  to the increased scope

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Sample Test Results

• Test case for two increments of 15 baseline
  capabilities each
• A non-deferrable change comes at month 8 (per
  previous slide)
• The agile team size is varied from 2 to 10 people
• Increment 1 business value also lost for agile
  team sizes of 2 and 4

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Total Effort and Schedule
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Total Costs Including Mission Value Loss
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Spiral Hybrid Model Conclusions and Future Work

• System dynamics is a convenient modeling
  framework to deal with the complexities of a
  SISOS
• A hybrid process appears attractive to handle
  SISOS dynamic evolution, emergent requirements
  and behaviors
• Initial results indicate that having an agile
  team can help decrease overall cost and schedule
• Model can help find the optimum balance
• Will obtain more empirical data to calibrate and
  parameterize model including volatility and
  change trends, change analysis effort, effort
  multipliers and field issue rates
• Model improvements
• Additional staffing options
• Rayleigh curve staffing profiles
• Constraints on development and VV staffing
  levels
• More flexible change deferral options across
  increments
• Increment volatility balancing policies
• Provisions to account for (timed)
  business/mission value of capabilities
• Additional model experimentation
• Include capabilities flowing back from developers
  and VVers
• Vary deferral policies and volatility patterns
  across increments
• Compare different agile team staffing policies
• Continue applying the model on a current SISOS
  and seek other potential pilots

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Outline

• System dynamics modeling review
• Spiral hybrid process model for
  Software-Intensive System of Systems (SISOS)
• References

24
References

• Boehm, B., Egyed, A., Kwan, J., Port, D., Shah,
  A., and Madachy, R. Using the WinWin Spiral
  Model A Case Study IEEE Computer, July (1998)
• Boehm, B., Abts C., Brown A., Chulani S., Clark
  B.,Horowitz E., Madachy R.,Reifer D., Steece B.
  Software Cost Estimation with COCOMO II,
  Prentice-Hall (2000)
• Boehm, B., Brown, A.W., Basili, V., Turner, R.
  Spiral Acquisition of Software-Intensive Systems
  of Systems, CrossTalk. May (2004)
• Boehm, B. Some Future Trends and Implications
  for Systems and Software Engineering Processes,
  USC-CSE-TR-2005-507 (2005)
• Boehm, B., Turner, R. Balancing Agility and
  Discipline, Addison Wesley (2003)
• Madachy R., Software Process and Business Value
  Modeling, Proceedings of the 6th International
  Workshop on Software Process Simulation and
  Modeling, St. Louis, MI, IEE, May (2005)
• Madachy R, Software Process Dynamics, Wiley-IEEE
  Computer Society Press, Washington, D.C., 2007
  (to be published)