CS 6v81 Software Process Modeling, Simulation

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CS 6v81Software Process Modeling,Simulation
Control

• João W. Cangussu
• cangussu_at_utdallas.edu
• www.utdallas.edu/cangussu

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Description
The study of modeling, monitoring, and control
techniques applicable to the Software Development
Process (SDP) is the focus of this course.
Important aspects of the SDP such as quality
control, measurement, and scheduling will be
addressed under a modeling/simulation perspective.
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Main Topics

• Software Process
• Discrete Event Simulation
• Software Metrics
• Orthogonal Defect Classification
• Feedback Software Process Control

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When/Why use simulation - I

• Simulation enables the study of complex system.
• Informational, organizational, and environmental
  changes can be simulated.
• The knowledge gained in designing a simulation
  model may be of great value toward suggesting
  improvement in the system under investigation.
• By changing simulation inputs and observing the
  resulting outputs, valuable insight may be
  obtained into which variables are most important
  and how variables interact.

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When/Why use simulation - II

• Simulation can be used to experiment with new
  designs or policies prior to implementation.
• Simulation can be used to verify analytic
  solutions.
• By simulating different capabilities for a
  machine, requirements can be determined.
• Animation shows a system in simulated operation
  so that the plan can be visualized.
• The modern system is so complex that the
  interactions can be treated only through
  simulation.

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When not use Simulation

• when the problem can be solved using common
  sense
• when the problem can be solved analytically
• when it is easier to perform direct experiments
• when the costs exceed the savings
• when the resources or time are not available
• when ability to verify and validate the model is
  very limited
• when managers have unreasonable expectations
• when system behavior is too complex or cannot be
  defined

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Advantages - I

• New policies, operating procedures, decision
  rules, etc. can be explored without disrupting
  ongoing operations of the real system.
• New hardware designs, physical layouts,
  transportation systems, and so on, can be tested
  without committing resources for their
  acquisition.
• Hypotheses about how or why certain phenomena
  occur can be tested for feasibility.
• Time can be compressed or expanded allowing for
  a speedup or slowdown of the phenomena under
  investigation.

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Advantages - II

• Insight can be obtained about the interaction of
  variables.
• Insight can be obtained about the importance of
  variables to the performance of the system.
• Bottleneck analysis can be performed
• A simulation study can help in understanding how
  the system operates rather than how individuals
  think the system operates.
• "What-if" questions can be answered.

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Disadvantages

• Model building requires special training.
• Simulation results may be difficult to interpret.
  
• Simulation modeling and analysis can be time
  consuming and expensive.
• Simulation is used in some cases when an
  analytical solution is possible, or even
  preferable.

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Steps in Simulation Study
1 Problem formulation Every study should begin
with a statement of the problem
2 Setting of objectives and overall project plan
The objectives indicate the questions to be
answered by simulation.
3 Model conceptualization The construction of a
model of a system is probably as much art as
science
4 Data collection There is a constant interplay
between the construction of the model and the
collection of the needed input data Shannon,
1975. As the complexity of the model changes,
the required data elements may also change
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Steps in Simulation Study
5 Model translation the model must be entered
into a computer-recognizable format.
6 Verification Is the computer program
performing properly?
7 Validation Does the simulation model replicate
this system measure?
8 Experimental design. The alternatives that are
to be simulated must be determined
9 Production runs and analysis Production runs,
and their subsequent analysis, are used to
estimate measures of performance for the system
designs that are being simulated.
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Steps in Simulation Study
10 More runs? determines if additional runs are
needed and what design those additional
experiments should follow.
11 Documentation and Reporting There are two
types of documentation program and progress.
12 Implementation.
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Possible Industry Case Studies

• Sun Microsystems
• Travelocity
• Nortel
• Texas Instruments

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State Variable Approach Outline

• Feedback State Model
• Calibration Algorithm
• Simulation Results
• Concluding Remarks

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Regular Feedback Approach
Initial Settings (wf,?)

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State Variable Approach
Actual STP
STP State Model
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Problem Scenario
r0
where cpi check point i
r remaining errors
rf
t- time
cp2
cp3
cp4
cp5
cp6
cp7
cp8
cp9
cp1
t0
deadline
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Feedback Application
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Application of the Calibration Algorithm
1 set initial values and generate data 2 pn
0 3 while accuracy 15 4 pn 5 Calibrate
Model using data from period 1 to pn 6 compute
new accuracy 7 end
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Data Generation
a is generated for time intervals of 10 days
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Initial Schedule Slippage
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Schedule Slippage After Calibration
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Schedule Slippage After Calibration ()
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Convergence Period
68.5 required at most 11 of total time to
converge
90 required at most 21 of total time to
converge
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Conclusions

• State of the art techniques
• Modeling of real development processes
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