This is Paul Gerrard's track talk at Eurostar 2011 i9n Manchester UK Novemb

1. The Pursuit of Quality: Chasing
Tornadoes or Just Hot Air?
Gerrard Consulting Limited
PO Box 347
Maidenhead
Berkshire
SL6 2GU
Tel: +44 (0) 1628 639173
Fax: +44 (0) 1628 630398
Web: gerrardconsulting.com
Intelligent Testing, Improvement and Assurance Slide 1

2. Agenda
• What is Quality?
• Models for quality and testing
• Examples of models
• Models and stakeholders
• Failures of systems, failures of models
• Close
Intelligent Testing, Improvement and Assurance Slide 3

3. Weather
• Rain is great for farmers and their crops, but
terrible for tourists
• Wind is essential for sailors and windmills but
bad for the rest of us
• Quality, like weather, can be good or bad and
that depends on who you are.
Intelligent Testing, Improvement and Assurance Slide 4

4. That’s
Fantastic!
That’s
Terrible!

5. Quality is a relationship
• Quality is not an
attribute of a system
• It is a relationship
between systems and
stakeholders who
take different views
• The model of Quality
that prevails has more
to do with
stakeholders than the
system itself
Intelligent Testing, Improvement and Assurance Slide 6

6. The concepts of quality, risk,
comfort, intuitiveness …
• Concepts that most
people understand, but
few can explain
• But it‟s a lot worse than
that
• Quality is an all-
encompassing, collective
term for these and many
other difficult concepts
• A term that means all
things to all people
• (I try and avoid the
Q-word).
Intelligent Testing, Improvement and Assurance Slide 7

7. Models for Quality and
Testing
Intelligent Testing, Improvement and Assurance Slide 8

8. Models
Models are everywhere
Intelligent Testing, Improvement and
Slide 9
Assurance

9. Models and reality
• In our minds we build mental models of everything we
experience (and also, many things we don‟t experience)
• When we pick up a glass of water, we build models
– The 3-dimentional location and relationship between the
glass, the water, the table it sits on and our body
– As we reach for the glass, our brain processes the signals
from our eyes, our muscles and the feelings in our
fingertips
– It continuously compares experience with the model and
adjusts/rebuilds the model many times
• … just to lift a cup of water – incredible!
Intelligent Testing, Improvement and Assurance Slide 10

10. Some familiar models
• The project plan is a model
– The resources, activities, effort, costs, risks and
future decision making
• System requirements are a model
– The “what and how” of the system
– What: the features and functionality
– How: how the system works (fast, secure, reliable)
• User personas (16 year old gamer, 30 year old
security hacker, 50 year old Man United fan).
Intelligent Testing, Improvement and Assurance Slide 11

11. Where quality comes from
• Quality is the outcome of a comparison
– Our mental model of perfection
– Our experience of reality
• Mental models are internal,
personal and unique to us
• We could share them using some
kind of Vulcan mind meld
• But usually, we can write them
down or we can talk about them
• However we communicate, there is noise and
information gets corrupted/lost in translation.
Intelligent Testing, Improvement and Assurance Slide 12

12. A quality model?
• The requirements and design describe the
behaviour of a system
• Functional
– Mapping test cases to requirements is all we need
• Non-Functional
– All technical attributes are defined and measured
• Quality and therefore testing assumes a model
– Often undocumented, the model may not be shared,
understood, complete, consistent, correct…
Intelligent Testing, Improvement and Assurance Slide 13

13. Test design is based on models
• Models describe the environment, system, usage, users,
goals, risks
• They simplify the context of the test - irrelevant or
negligible details are ignored in the model
• Focus attention on a particular aspect of the behaviour
of the system
• Generate a set of unique and diverse tests (within the
context of the model)
• Enable the testing to be estimated, planned, monitored
and evaluated for its completeness (coverage).
• Models help us to select tests in a systematic way.
Intelligent Testing, Improvement and Assurance Slide 14

14. Examples of test models
• A checklist or sets of criteria
– Goals, risks, process paths, interfaces, message type…
• Diagrams from requirements or design
documents
• Analyses of narrative text or tables
• Some models are documented, many models are
never committed to paper
– Can be mental models constructed specifically to
guide the tester whilst they explore the system under
test and guide their next action.
Intelligent Testing, Improvement and Assurance Slide 15

15. Sources of models
• Test Basis
– We analyse the text or diagrams or information that describe
required behaviour (or use past experience and knowledge)
• System architecture:
– We identify testable items in its user-interface, structure or
internal design
• Modes of failure (product risks):
– We identify potential ways in which the system might fail that
are of concern to stakeholders
• Usage patterns:
– We focus on the way the system will be used, operated and
interacted with in a business context using personas
• Everything looks fine – doesn‟t it?
Intelligent Testing, Improvement and Assurance Slide 16

16. But all models (over-)simplify
• But requirements are never perfect, not all
attributes can be meaningfully measured
• Models incorporate implicit assumptions and are
approximate representations
• All test models are heuristic, useful in some
situations, always incomplete and fallible
• Before we adopt a model, we need to know:
– What aspects of the behaviour, design, modes of
failure or usage the model helps us to identify
– What assumptions and simplifications it includes
(explicitly or implicitly).
Intelligent Testing, Improvement and Assurance Slide 17

17. Formality
• Formal test models
– Derived from analyses of requirements or code
– Quantitative coverage measure can be obtained from a formal
test mode (mostly)
• Informal test models
– E.g. some models are just lists of modes of failure, risks or
vulnerabilities.
– Informal models cannot be used to define quantitative coverage
measures
• Ad-hoc models
– Some models can be ad-hoc, invented by the tester just before
or even during testing
– Can be formal or informal.
Intelligent Testing, Improvement and Assurance Slide 18

18. Examples of Models
Intelligent Testing, Improvement and Assurance Slide 19

19. Basic test design techniques are
based on the simplest models
• Equivalence partitions and boundary values:
– Presume single input, single output responses
– All values in partitions are equivalent, but the
boundaries are the most important
• These techniques are useful, but they date
from the „green-screen‟ era.
Intelligent Testing, Improvement and Assurance Slide 20

20. “Green Screen” equivalence model
• Single input, single output Single Input
• All input is classified and
partitioned with rules
If m<1 then
• One test per rule is “Error”
Else if m>12 then
enough! “Error”
• But we don‟t consider: Else
“OK”
– The state of the system
– Combinations of values.
Single Output
Intelligent Testing, Improvement and Assurance Slide 21

21. State Transition Testing
Customer arrives Customer pays
None Increment room
count
Room Overnight
Booked Checkout
Room available Stay
Decrement room count
Start Room Customer cancels
Requested Room available Increment room
State Decrement room count
Room request
None count
No room available
Add to waiting list
On
Booking
Waiting
Customer cancels Cancelled
List Remove from waiting
list
Intelligent Testing, Improvement and Assurance Slide 22

22. But the number of states is infinite!
• State-Transition considers:
– The states of the system and
– The valid/invalid transitions between states
• Some systems have many, many states
– A real-time system e.g. telecoms switch may have
25,000 distinct states
– State may depend on many variables that can have
infinite values in combination
• How confident can we be in this model?
Intelligent Testing, Improvement and Assurance Slide 23

23. End-to-end/transaction-flow tests
• End–to-end tests can follow a path through a
process or a user journey
• The mechanics of the experience are
simulated but…
Intelligent Testing, Improvement and Assurance Slide 24

24. Bad experience leads to attrition
• Typical form-filling on government sites
intended to allow citizens to „apply online‟
Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7
45% 72% 48% 21% 85% 80% Conversion
by page
45% 32% 16% 3% 3% 2% Cumulative
• Every page „works‟ but the user-experience is so
poor that only 2% finish the journey
• Modelling the journey is good, but not enough…
• We need to model the experience too.
Slide 25

25. Models and
Stakeholders
Intelligent Testing, Improvement and Assurance Slide 26

26. Stakeholders and test models
• Stakeholders may not tell testers to use
specific test models; you need to explain them
to stakeholders so they understand
• The challenge(s):
– Stakeholders may be of the opinion that the
models you propose generate too few tests to be
meaningful or too many to be economic
– We need to engage stakeholders.
Intelligent Testing, Improvement and Assurance Slide 27

27. „Measuring quality‟ feels good
but…
• Measurable quality attributes make techies feel
good, but they don‟t help stakeholders if they
can‟t be related to experience
• If statistics don‟t inform the stakeholders‟
vision or model of quality
– We think we do a good job
– They think we waste their time and money.
Intelligent Testing, Improvement and Assurance Slide 28

28. Relevance
• Documented or not, testers need and use models
to identify what is important and what to test
• A control flow graph has meaning (and value) to a
programmer but not to an end-user
• An equivalence partition may have meaning to
users but not the CEO of the company
• Control flow, equivalence partitions are models
that have value in some, but never all, contexts.
Intelligent Testing, Improvement and Assurance Slide 29

29. Helping stakeholders to make
better decisions is the tester‟s goal
• We need models that
– Do more than identify tests
– Take account of the stakeholders‟ perspective and
have meaning in the context of their decision-
making
• If we „measure quality‟ using technical models
– We delude both our stakeholders and ourselves
into thinking we are in control of Quality
– We‟re not.
Intelligent Testing, Improvement and Assurance Slide 30

30. Failures of Systems,
Failures of Models
Intelligent Testing, Improvement and Assurance Slide 31

31. F-16 bug (found in flight)
• One of the early problems was that you could
flip the plane over and the computer would
gladly let you drop a bomb or fuel tank. It
would drop, dent the wing, and then roll off.
• http://catless.ncl.ac.uk/Risks/3.44.html#subj1.1
Poor test model
Intelligent Testing, Improvement and Assurance Slide 32

32. Poor test model
Slide 33

33. Poor test model
Intelligent Testing, Improvement and Assurance
Slide 34

34. Scope of testing for E-Commerce
4. Full E-Business System
3. Order Processing Sub-System Process
2. Web Sub-System
People
Web Server
Environment
Banking System 1. Application
(Credit Card (objects)
Processor) Sub-System
Database Server Legacy System(s)
Training

35. Test strategy
• Our test strategy must align with our model
of quality and our risk-assessment Every focus area
requires test
Test Phase Focus model(s)
Requirements, design etc. Relevance, correctness, completeness, ambiguity etc.
Component Input validation, correct behaviour, output validation,
statement and branch coverage
Integration Correct, authorised transfer of control, exchange of
data, consistency of use and reconciliations
System (-system) End-to-end accuracy, consistency, security,
performance and reliability
Acceptance Alignment to business goals, end-to-end ease of use
and experience, successful outcomes, user personas
Intelligent Testing, Improvement and Assurance Slide 36

36. Failure of testing is usually a failure
in a test model
• If the right models are selected, and commitment
is made to cover them
– The testing usually gets done
• But often, no model is explicitly selected at all
• Where a model fails, it is usually wrong because:
– The model does not represent reality
– The scope of the model is too narrow
– The model ignores critical aspects (context, people,
process, environment or training/capability).
Intelligent Testing, Improvement and Assurance Slide 37

37. Close
• We need to understand what quality is before we
can pursue and achieve it
• Testing often fails because test models are not
used or understood
• Testers need models to test but the „standard‟
quality models are too simple
• We need to take stakeholder views into account
to create relevant testing models
• Using models sounds techy, but it‟s completely
natural – it‟s part of what makes us human.
Intelligent Testing, Improvement and Assurance Slide 38

38. Assurance is NOT
Quality Assurance
It’s not about proof or
guarantees, either
Intelligent Testing, Improvement and
Slide 39
Assurance

39. @paul_gerrard
The Pursuit of Quality: Chasing
Tornadoes or Just Hot Air?
gerrardconsulting.com
maelscrum.com
businessstorymanager.com
test-axioms.com
uktmf.com
Intelligent Testing, Improvement and Assurance Slide 40