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The Pursuit of Quality - Chasing Tornadoes or Just Hot Air?
 

The Pursuit of Quality - Chasing Tornadoes or Just Hot Air?

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

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

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    The Pursuit of Quality - Chasing Tornadoes or Just Hot Air? The Pursuit of Quality - Chasing Tornadoes or Just Hot Air? Presentation Transcript

    • The Pursuit of Quality: Chasing Tornadoes or Just Hot Air?Gerrard Consulting LimitedPO Box 347MaidenheadBerkshireSL6 2GUTel: +44 (0) 1628 639173Fax: +44 (0) 1628 630398Web: gerrardconsulting.com Intelligent Testing, Improvement and Assurance Slide 1
    • 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
    • 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
    • That’sFantastic! That’s Terrible!
    • 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
    • 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
    • Models for Quality and Testing Intelligent Testing, Improvement and Assurance Slide 8
    • Models Models are everywhere Intelligent Testing, Improvement and Slide 9 Assurance
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Examples of Models Intelligent Testing, Improvement and Assurance Slide 19
    • Basic test design techniques arebased 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
    • “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
    • State Transition Testing Customer arrives Customer pays None Increment room count Room Overnight Booked Checkout Room available Stay Decrement room countStart Room Customer cancels Requested Room available Increment roomState 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
    • 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
    • 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
    • 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
    • Models andStakeholdersIntelligent Testing, Improvement and Assurance Slide 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
    • „Measuring quality‟ feels goodbut…• 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
    • 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
    • Helping stakeholders to makebetter 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
    • Failures of Systems, Failures of Models Intelligent Testing, Improvement and Assurance Slide 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
    • Poor test model Slide 33
    • Poor test modelIntelligent Testing, Improvement and Assurance Slide 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
    • Test strategy• Our test strategy must align with our model of quality and our risk-assessment Every focus area requires testTest Phase Focus model(s)Requirements, design etc. Relevance, correctness, completeness, ambiguity etc.Component Input validation, correct behaviour, output validation, statement and branch coverageIntegration Correct, authorised transfer of control, exchange of data, consistency of use and reconciliationsSystem (-system) End-to-end accuracy, consistency, security, performance and reliabilityAcceptance Alignment to business goals, end-to-end ease of use and experience, successful outcomes, user personas Intelligent Testing, Improvement and Assurance Slide 36
    • Failure of testing is usually a failurein 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
    • 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
    • Assurance is NOT Quality AssuranceIt’s not about proof or guarantees, either Intelligent Testing, Improvement and Slide 39 Assurance
    • @paul_gerrardThe 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