Obstacle Driven Development is the latest engineering process and combines Test Driven Development with safety critical V-model development. This updated presentation demonstrates how ODD extends and combines requirements analysis with Test Driven Development and V-models. Please see the series for further details.

1. Obstacle Driven Development
Extending Requirements Analysis 2
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2. Obstacle Driven Development
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3. ODD Circle Model
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4. ODD Process
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5. Background
Ideas of Obstacle Driven Development (ODD) are based on
numerous development processes including:
• ISO V-model
• Test Driven Development
• ISO specifications
• Requirements analysis
• Agile principles
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6. About Requirements Analysis
Requirements analysis encompasses tasks that determine the
needs or conditions necessary for a new or altered product.
Tasks necessary for requirements analysis include:
• Analysing, documenting, validating and managing software or
system requirements
• Identify and resolve conflicting requirements of stakeholders
• Identifying business needs or opportunities using testable and
traceable processes
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7. Requirements Analysis 1
Requirements analysis spiral and
Safety Integrity Levels are adapted
to give ODD processes.
Requirements analysis is performed
in numerous ways
• Spiral model
• Use case analysis
• Safety integrity Levels (SILs)
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8. Requirements Analysis 2
A spiral model is superimposed
with an M-model and adaptions
made.
• Agreed Behaviours substitutes
Agreed Requirements
• Quality Assurance equivalent to
Testing
• Negotiation similar to Verification
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9. Requirements Analysis 3
Further adaption leads to an ODD
model for requirements analysis.
• Product, Consolidated
Requirements and Documents
are checkpoints
• Verification substituted for
Negotiation
• Validation substitutes Evaluation
• Testing substitutes Quality
Assurance
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10. ODD Analysis
Requirements analysis is adapted to
allow for ODD and combined with
the first stages of an M-model.
• Safety Integrity Levels used to
measure and process hazards
• Decision tree approach used to
create situations
• Verification and validation of
specification
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11. ODD M-model
Adding Solution and Production
stages of development results in an
ODD M-model.
• ODD process is linked from start
to finish, and beyond
• Verification and validation
between all stages
• Tests are ran as additions and
editing occurs
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12. ODD M-model Checkpoints 1
Checkpoints are determined for the
solution and production stages.
• Prototype created from
integrated solution
• Product is result of production
• Linked to other checkpoints
horizontally
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13. ODD M-model Checkpoints 2
Checkpoints allow linking and
testing of results to previous stages.
• Each checkpoint links another
• Prototype fulfils identified
requirements
• Product should behave as
described in documents
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14. Fire Triangle 1
A fire triangle is an educational tool
for understanding and preventing
fires.
• If the fire triangle is completed
then a fire will occur
• Preventing one situation from
occurring will prevent a fire
• Requirements often regard
preventing fires
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15. Fire Triangle 2
Using a reordered fire triangle it is
seen that components combine to
create a hazard.
• Process is adaptable to all fire
hazards and environments
• Extendible to any number of fire
hazard situations
• Components can be given SIL
ratings for Probability, Severity
and Controllability
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16. Fire Triangle 3
Reordering again gives a decision
tree for fire prevention.
• Investigated for requirements of
a situation
• Each branch is analysed and
processed
• Useful for any and all fire hazards
• To simplify oxygen is assumed
present
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17. Fire Triangle 4
Decision tree shows
the hazards of each
situation.
• Top branch ignites a
fire
• Next 2 branches are
fire hazards
• Each situation is
analysed separately
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18. Probability Tree 1
Probability trees measure likelihood
of an event occurring from a
defined situation.
• A common example is probability
of coin tosses
• Probability of heads occurring
assumed to be 50%
• Each branch gives an individual
situation
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Heads 50%
Heads 50%
Tails 50%
Tails 50%
Heads 50%
Tails 50%

19. Binomial Distribution 1
Binomial distributions determine
probability for any number of
events with 2 possible outcomes.
• Binomial process illustrates how
decision trees can be extended
• Potential use to model complex
interactions
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20. Binomial Distribution 2
Binomial distributions determines
probability for any number of
events with 2 possible outcomes.
• Decision trees can be extended
for infinite number of events
• Only used to model true or false
experiments
𝑃 𝑋 = 𝑘 =
𝑛
𝑘
𝑝 𝑘(1 − 𝑝) 𝑛−𝑘
𝑃 𝑋 = 𝑘 = Probability of Event
𝑘 = Number of Events
𝑛 = Number of Trials
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21. Probability Tree 2
Coin toss example is extended into
engineering by substituting system
components.
• Working component replaces
heads
• Failing component replaces tails
• Potential hazards of a series of
failures can be determined
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Component 1
Pass 99%
Component 2
Pass 98%
Component 2
Fail 2%
Component 1
Fail 1%
Component 2
Pass 98%
Component 2
Tails 2%

22. Binomial Distribution 3
Binomial distributions used to
determine probability for
components failing.
• Commonly used for measuring
probabilities in manufacturing
• Extendible to model failure in the
field
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Component 1
Pass 99%
Component 2
Pass 98%
Component 2
Fail 2%
Component 1
Fail 1%
Component 2
Pass 98%
Component 2
Tails 2%

23. Probability Tree 3
Probability trees are easily
extended to other situations.
• Coins may also land on their side
• Landing on sider assigned a
probability of 0.017 % (1 per
6000 tosses)
• Probability tree has exponentially
more branches
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Head ≈ 50%
Head ≈ 50%
Tails ≈ 50%
Side ≈
1 / 6000
Tails ≈ 50%
Head ≈ 50%
Tails ≈ 50%
Side ≈
1 / 6000
Side ≈
1 / 6000
Head ≈ 50%
Tails ≈ 50%
Side ≈
1 / 6000

24. Probability Tree 4
Probability trees can be used to
ensure all possible situations are
modelled.
• Systems have unknown states
between pass and fail
• Unknown states include loss of
communication or wear
• Unknown states investigated for
effects
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Component 1
Pass 98%
Component 2
Pass 96%
Component 2
Fail 2%
Component 2
Unknown 2%
Component 1
Fail 1%
Component 2
Pass 96%
Component 2
Fail 2%
Component 2
Unknown 2%
Component 1
Unknown 1%
Component 2
Pass 96%
Component 2
Fail 2%
Component 2
Unknown 2%

25. Safety Integrity Levels 1
Safety Integrity Levels (SILs) are
used to measure potential hazards
of a situation.
• Situation is analysed for
Probability, Severity and
Controllability
• Estimates for a risk of hazards
occurring from the situation
𝑆𝐼𝐿 = 𝑃 𝐸 ∗ 𝑆 𝐸 ∗ 𝐶 𝐸
𝑃 𝐸 = Probability of Event
𝑆 𝐸 = Severity of Event
𝐶 𝐸 = Controllability of Event
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26. Safety Integrity Levels 2
Safety Integrity Levels are used for a
wide range of safety critical
analysis.
• Probability is how likely a
situation will occur
• Severity is potential damage of a
situation
• Controllability is ability to effect
change in a situation
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Component
𝑆𝐼𝐿 = 𝑃 𝐸 ∗ 𝑆 𝐸 ∗ 𝐶 𝐸

27. Safety Integrity Levels 3
Coin toss example is extended to
provide example of SILs.
• Probability of result is different
for each coin
• Severity of outcome measured by
hazards
• Controllability determined by
who flips coin and stakes
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28. Safety Integrity Levels 4
Probability tree and SILs are
combined to form a decision tree.
• Measures added for severity and
controllability
• Each branch is a situation with
SIL ratings and requirements to
be found
• SIL ratings are applied and found
for each situation
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29. ODD Decision Tree 1
A decision tree creates situations
and processes requirements.
• Severity and Controllability are
added to each event
• Requirements are found with SIL
processes using branches
• Facilitates a unit testing approach
for situations
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30. ODD Decision Tree 2
Adding SIL components to a
Probability Tree allows requirement
identification from a decision tree.
• Structure is a branching
probability tree with SILs
• SILs are found by multiplying
along branches of a decision tree
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31. ODD Decision Tree 3
Processing resulting decision tree is
similar to a probability tree.
• SIL ratings processed by
multiplying probability, severity
and controllability
• SIL rating multiplied along
branches
• SIL result between 1 and 4, with
4 being best
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Component 1
Pass
P(P1)*S(P1)*C(P1)
Component 2
Pass
P(P2)*S(P2)*C(P2)
Component 2
Fail
P(F2)*S(F2)*C(F2)
Component 1
Fail
P(F1)*S(F1)*C(F1)
Component 2
Pass
P(P2)*S(P2)*C(P2)
Component 2
Fail
P(F2)*S(F2)*C(F2)

32. ODD Decision Tree 4
Using a decision tree and SILs gives
numerous advantages.
• Branches are used to ensure
predictable situations are found
• Extendible and adaptable to new
situations
• Each branch is a situation
• Situations are found from
combining component situations
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Component 1
Pass
Component 2
Pass
Situation A
Component 2
Fail
Situation B
Component 1
Fail
Component 2
Pass
Situation C
Component 2
Fail
Situation D

33. Processing Decision Trees
Situation A defines a situation
where both components have
passed.
Using a decision tree we can find a
SIL rating for the situation.
𝑆𝐼𝐿 𝐴
= 𝑃 𝑃1 ∗ 𝑆 𝑃1 ∗ 𝐶 𝑃1 ∗
𝑃 𝑃2 ∗ 𝑆 𝑃2 ∗ 𝐶(𝑃2)
Situation D defines a situation
where both components have
failed.
Using the decision tree we can find
a SIL rating for the situation.
𝑆𝐼𝐿 𝐷
= 𝑃 𝐹1 ∗ 𝑆 𝐹1 ∗ 𝐶 𝐹1 ∗
𝑃 𝐹2 ∗ 𝑆 𝐹2 ∗ 𝐶(𝐹2)
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34. ODD Decision Tree 5
Decision tree gives potential for an
infinite range of situations.
• Events can be comprehensively
modelled and extended
• New situations added as
branches
• Models complexity of expected
situations
• Software implementation
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35. ODD Decision Tree 6
Component 1
Pass
Component 2
Pass
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 2
Fail
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 3
Unknown
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 1
Fail
Component 2
Pass
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 2
Fail
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 2
Unknown
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 1
Unknown
Component 2
Pass
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 2
Fail
Component 3
Pass
Component 3
Fail
Component 3
Unknown
Component 2
Unknown
Component 3
Pass
Component 3
Fail
Component 3
Unknown
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A decision tree to illustrate complexity of modelling situations and software will be necessary to
facilitate this. Modelled are 3 components with 3 different states: Pass, Fail and Unknown.

36. Creating Unit Tests 1
Unit testing is used to link
requirements with behaviours.
• Each branch represents a
situation which a behaviour
should cover
• Requirements are found from
situations
• Unit tests are created for each
branch of a decision tree
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37. Creating Unit Tests 2
Each requirement creates a test
which is solved by describing a
behaviour.
• Test is created to ensure
requirement is covered
• Specification describes all
expected behaviours of a product
• Allows cross examination of
behaviours against situations
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38. Creating Unit Tests 3
1. Requirement is chosen from
branch on a decision tree.
2. Behaviour verification test is
created directly from
requirement.
3. Behaviour is described to cover
requirement.
4. Behaviour is validated by
passing test.
5. Repeat for all requirements.
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39. Creating Unit Tests 4
Unit tests link situations and
requirements with behaviours
through creating and solving a test.
• Verification through creation of
test
• Validation through passing test
• Situations are combined to
produce complex situations
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40. Linking Tests 1
Each situation is linked to a
behaviour contained in a
specification.
• Behaviour A covers normal
operation
• Behaviour B and C cover single
failures of components
• Behaviour D covers total failure
of components
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41. Linking Tests 2
Each situation creates a unit test to
link analysis with specification.
• Diagrams shows various stages of
completeness
• Once unit test is passed it
becomes green
• Tests are implemented as a suite
and ran when editing occurs
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42. Linking Tests 3
Each situation is linked to a
behaviour contained in a
specification.
• Behaviour A covers normal
operation
• Behaviour B and C cover single
failures of components
• Behaviour D covers total failure
of components
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43. Linking Tests 4
When using a waterfall type
development then analysis is
complete when all tests are passed.
• Tests run when any changes in
analysis or specification
• Ensures expected situations are
covered
• Potential for infinite situations
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44. ODD Combined Model
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The testing process is
similar throughout
ODD with adaptions
between stages.
• Each set of traffic
light demonstrates
unit testing
• Tests begin with an
element from
previous stage

45. Linking Tests 5
Tests link product features with
analysis through utilisation and
elicitation.
• Requirements are found from
analysing each feature
• Each feature is a single aspect of
the product
• Unit testing is applied
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46. Linking Tests 6
Requirements analysis is the most
difficult and important stage to link.
• Utilisation and elicitation should
concern situations resulting from
product features
• Customers are involved for
utilisation and elicitation
• Each situation encountered is
covered by a requirement
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47. Further Information and Questions
• Website
• Presentations
• Facebook
• Twitter
• Email
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48. Legal Stuff
References
Test Driven Development for Embedded C
James Grenning, 2011
Requirements Analysis
http://en.wikipedia.org/wiki/Requirements_analysis
Safety Integrity Level
http://en.wikipedia.org/wiki/Safety_Integrity_Level
Decision Tree
http://en.wikipedia.org/wiki/Decision_tree
Requirements Spiral Model
www.engineering.uiowa.edu/~kuhl/SoftEng/Slides4.pdf
Unit Testing
http://en.wikipedia.org/wiki/Unit_testing
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