Tenets of Testing – A comparison with Test Axioms

Nicholas Snogren posted on LinkedIn a reference to an “Axioms of Testing” presentation from 2009 and asked me to comment on his “Tenets of Software Testing“. There are some similarities but not many I think, some parallel too, but his question prompted me to give a longer response than I guess was expected. I said…

“Hi, thanks for asking for my opinion. Your tenets look interesting – and although I don’t think they map directly to what I’ve written, they raise points in my mind that need a little airing – my mind grows cobwebby over time, and it’s good to brush off old ideas. A bit like exercising muscles that haven’t been used for a while haha.”

I give my response as a comparison with my Tester’s Pocketbook, and Test Axioms website and presentations. (I notice that some of these posts are around 12 years old and some links don’t work (anymore). Some are out of my control, others I’ll have to track down and correct others – let me know if you want that.)

Tenets v Axioms

Firstly, let’s get our definitions right.

According to dictionary.com, a Tenet is “any opinion, principle, doctrine, dogma, etc., especially one held as true by members of a profession, group, or movement.” Tenets might be regarded as beliefs that don’t require proof and don’t provide a strong foundation.

From the same source, an Axiom is, “i) a self-evident truth that requires no proof. ii) a universally accepted principle or rule. iii) Logic, Mathematics. a proposition that is assumed without proof for the sake of studying the consequences that follow from it”

I favoured the use of Axioms as a foundation for thinking in the testing domain. Axioms, if they are defensible, would provide a stronger foundational set of ideas. When I proposed a set of Testing Axioms, there was some resistance – here’s a Prezi talk that introduces the idea.

James Bach in particular challenged the idea of Axioms and suggested I was creating a school of testing (when schools of testing were getting some attention) here.

By and large, by defining the Axioms in terms that are context-neutral, challenges have tended to be easy to acknowledge, disarm and set aside. Critics, almost entirely from the context-driven school, jumped the gun so to speak – they clearly hadn’t read what I had written at the time before critiquing. Only one or two people responded to James’ call to arms to criticise the Axioms and challenged them.

The Axioms are fully described in The Tester’s Pocketbook – http://testers-pocketbook.com/.

The Axioms of Testing website – https://testaxioms.com/ – sets out the Axioms with some explanation and provides around 50% of the pocketbook content for free.

Axioms caught the attention (and criticism) of people because I pitched them as universal principles or laws of testing. Tenets, being less strident in their definition might not attract attention (or criticism) in the same way.

Immediate Comments on the Tenets

The Tenets are numbered and italicised. My comments in plain text.

  1. A software product’s behavior is exhibited by interactions.
  2. There is potentially an infinite number of possible behaviors in software.

These are properties of software. I’m not sure what 1 says other than behavior is triggered by interactions and presumably observed through interactions. Although a lot of software behaving autonomously might respond to internal events such as the passing of time and might not exhibit any behaviour through interactions e.g. a change of internal state. I’m not sure 1 says much.

Tenet 2 is reasonable for reasonably-sized software artefacts.

In the Tester’s Pocketbook, I hardly use the term software. I prefer that we test systems. Software is usually an important part of every system. Humans do not interact with software (except by reading or writing it). Software exists in the context of program compilation, hosted on operating systems, running on devices which have peripherals and other interconnected systems which may or may not have user interfaces.

Basing Axioms on Systems means that the Axioms are open to interpretation as Axioms of testing ANY system (i.e. anything. I don’t press that idea – but it’s an attractive one). Another ‘benefit’ is that all of the Systems Thinking principles can also be brought to bear on our arguments. Outside its context, Software is not a System.

3. Some of those behaviors are potentially negative, that is, would detract from the objectives of the software company or users.

I use the term Stakeholders to refer to parties interested in the valuable, reliable behavior of systems and the outcome and value of testing those systems.

4. The potentiality for that negative behavior is risk.

OK, but it could be better worded. I would simply say ‘potential modes of failure’ rather than negative behaviour.

5. It’s impossible to guarantee a lack of risk as it’s impossible to experience an infinite number of behaviors.

Not really. You can guarantee a no-risk situation if no one cares or no one cares enough to voice their concerns before testing (or after testing). There is always the potential for failure because systems are complex and we are not skilled enough to create perfect systems.

6. Therefore a subset of behaviors must be sampled to represent the risk.

Rather than represent, I would say trigger the failure(s) of concern to explore the risk and better inform a risk-assessment.

7. The ability to take an accurate sample, representative of the true risk, is a testing skill.

Not sure what you mean by sample – tests or test cases, I presume? Representative is a subjective notion, surely; ‘true’ I don’t understand; and a testing skill would need more definition than this, wouldn’t it?

8. A code change to an existing product may also affect the product in an infinite number of ways.

I’d use ‘ANY’ change, to a ‘SYSTEM’. Why ‘also’? What would you say fits into a ‘not only…. but also…’ clause? But I’m not sure I agree with this assertion anyway. A code change changes some software artefact. The infinite effects (faulty behaviors?) derive from infinite tests (or uses in production) – which you say in 5 is impossible to achieve. I’m not sure what you’re trying to say here.

9. It is possible to infer that some behaviors are more likely to be affected by that change than others.

You can infer anything you like by calling upon the great Unicorn in the sky. How will you do this? Either you use tools which are limited in capability or you might use change and defect history or you might guess based on partial knowledge and experience.

10. The risk -of that change- is higher within the set of behaviors that are more likely to be affected by that change.

Do you mean probability of failure or the consequence of failure? I assume probability. At any rate, this is redundant. You have already asserted this in 9. But it’s also more complicated than this – a cosmetic defect on an app can be catastropic and a system failure negligible at times.

11. The ability to accurately estimate a scope of affected behavior is another testing skill.

I would call this the skills of impact analysis rather than testing. Developers are relatively poor at this, even having a far deeper technical knowledge (either they aren’t able or lack the time to impact-analyse to any reliable degree). So we rely on testing to catch regressions which is less than ideal. Testers depend on their experience rather than system internals knowledge. But, since buggy systems behave in essentially unpredictable ways, we must admit our experience is limited and fallible. It’s not a ‘skill’ that I would dwell on.

12. The scope and sampling ideas alone are meaningless without empirical evidence.

The scope and sampling ideas have meaning regardless of whether you implement them. I suppose you might say they are useless ideas if you don’t gather evidence.

13. Empirical evidence is gathered through interactions with the product, observation of resultant behavior, and assessment of those observations.

The word empirical is redundant. I would use the word ‘some’ here. We also get evidence from operation in production, for example. (Unless you include that already?)

14. The accuracy and speed of scope estimation, behavior sampling, and gathering of evidence are key performance indicators for the tester.

If you are implying 13 are tester skills, I suppose you could make this assertion. But you haven’t said what the value of evidence is yet. Is the purpose of testing only to evaluate the performance of testers? Hope not ;O)

15. Heuristics for the gathering of such evidence, the estimation of scope, and the sampling of behavior are defined in the Heuristic Test Strategy Model.

Heuristics are available in a wide range of sources including software, systems and engineering standards. Why choose such a limited source?

Inspiration for the Tenets

These tenets were inspired by James Bach’s “Risk Gap” and Doug Hubbard’s book “How to Measure Anything.” Both Bach and Hubbard discuss a very similar idea from different spaces. Hubbard suggests that by defining our uncertainty, we can communicate the value of reducing the uncertainty. Bach describes the “knowledge we need to know” as the “Risk Gap.” This Risk Gap is our uncertainty, and in defining it, we can compute the value of closing it. In testing, I realized we have three primary areas of uncertainty: 1) what is the “risk gap,” or knowledge we need to find out, 2) how can we know when we’ve acquired enough of that unknown knowledge, and 3) how can we design interactions with the program to efficiently reveal this knowledge.

There are several interesting anomalies to unpick here:

  • I recall James telling a story about Tom Gilb asserting anything could be measured. James suggested Love and Tom obliged. I don’t think James was impressed.
  • ‘Defining uncertainty’ – how do you do that reliably? Numerically? Objectively? We can put any numbers we like against probability and consequence. Being certain, with or without evidence, is always subjective. People can say they are more certain, but based on … what? How do we correlate data with a human emotion and use that to make engineering decisions? People can be easily deceived – by themselves, not just by others. Consider this, for example, and this.
  • Risk Gap – how is a quantity of knowledge measured? What units? With what certainty? These are aspects that James has argued against since the early 1990s.
  • Your three challenges 1), 2)and 3) are reasonable as goals. How do Bach and Hubbard argue you achieve them, if not by calling on the subjective opinions of other people?

Some More General Comments

You seem to be trying to ‘make a case’ for testing as a tool to address the risk of failure in systems. I (like and) use that same approach in a rounder sense in my conference talks and writings, when practicable. My observations on this are:

  1. The logic doesn’t flow as it should because of flaws in the individual statements
  2. You have no pre-definition of test, testing or its purpose at the outset, so it’s not clear what your destination is
  3. There’s no defined goal of testing, other than to gather evidence to (my words) reassess risks and thereby reduce uncertainty (but you don’t say why that’s a ‘good thing’)
  4. Testing enables a reassessment of risk, but that reassessment may increase risk if, for example, you find a bug in something that was previously deemed reliable. (there’s a bigger conversation to be had, but risk is not a BAD thing, it’s the barrier(s) you need to navigate or break through to gain your REWARD).
  5. Extant, significant risks are a BARRIER to accepting or releasing systems. As such, the goal of testing is to provide evidence that to the people who make the decision, those risks are acceptable or negligible (reducing uncertainty, sure but never eliminating it). But the ultimate goal of testing is to show that the system WORKS. Encountering failures is a detour from that goal. The testing goal is broader than exploring risks.
  6. You don’t mention stakeholders at all. Why do we test? To provide evidence to testing stakeholders – our customers – so they can make better-informed decisions.

Summary

I don’t want to give the impression that I’m criticising Nicholas or am arguing against the concept of Tenets or Principles or Axioms of testing. All I have tried to do is offer reasonable criticism of the Tenets to show that is a) extremely difficult to postulate bullet-proof Tenets, Principles or Axioms and b) it is extremely easy to criticise such efforts by:

  • Exposing flaws in the language used and the logic in an argument that C follows B follows A etc.
  • Identifying implicit assumptions of meaning, scope, dependency and authority and
  • Offering examples of context that contradict, or expose flaws in, the statements made.

I do this because I have been there many times since 2008 and occasionally have to defend the Test Axioms from such criticisms. I have to say, Critical Thinking is STILL a rare skill – I wish criticism were more often proffered as a result of it.

References

  1. The Tester’s Pocketbook – http://testers-pocketbook.com/
  2. Axioms of Testing website – https://testaxioms.com/

Test Management is Dead, Long Live Test Management

Do you remember the ‘Testing is Dead’ meme that kicked off in 2011 or so? It was triggered by a presentation done by Alberto Savoiea here . It caused quite a stir, some copycat presentations and a lot of counter-arguments. But I always felt most people missed the point being made. you just had to strip out the dramatics and Doors music.

The real message was that for some organisations, the old ways wouldn’t work any more, and as time has passed, that prediction has come true. With the advent of Digital, mobile, IoT, analytics, machine learning and artificial intelligence, some organisations are changing the way they develop software, and as a consequence, testing changes too.

Shifting testing left, with testers working more collaboratively with the business and developers, test teams are being disbanded and/or distributed across teams. With no test team to manage, the role of the test manager is affected. Or eliminated.

Test management thrives; test managers come and go.

It is helpful to think of testing as less of a role and more of an activity that people undertake in their projects or organisations. Everyone tests, but some people specialise and make a career of it. In the same way, test management is an activity associated with testing. Whether you are the tester in a team or running all the testing in a 10,000 man-year programme, you have test management activities.

For better or for worse, many companies have decided that the role of test managers is no longer required. Responsibility for testing in a larger project or programme is distributed to smaller, Agile teams. There might be only one tester in the team. The developers in the team take more responsibility for testing and run their own unit tests. There’s no need for a test manager as such – there is no test team. But many of the activities of test management still need to be done. It might be as mundane as keeping good records of tests planned and/or executed. It could be taking the overall project view on test coverage (of developer v tester v user acceptance testing for example).

There might not be a dedicated test manager, but some critical test management activities need to be performed. Perhaps the team jointly fulfil the role of a virtual test manager!

Historically, the testing certification schemes have focused attention on the processes you need to follow—usually in structured or waterfall projects. There’s a lot of attention given to formality and documentation as a result (and the test management schemes follow the same pattern). The processes you follow, the test techniques you use, the content and structure of reporting vary wherever you work. I call these things logistics.

Logistics are important, but vary in every situation.

In my thinking about testing, as far as possible, I try to be context-neutral. (Except my stories, which are grounded in real experience).

As a consultant to projects and companies, I never knew what situation would underpin my next assignment. Every organisation, project, business domain, company culture, and technology stack is different. As a consequence, I avoided having fixed views on how things should be done, but over twenty-five years of strategy consulting, test management and testing, certain patterns and some guiding principles emerged. I have written about these before[1].

To the point.

Simon Knight at Gurock asked me to create a series of articles on Test Management, but with a difference. Essentially, the fourteen articles describe what I call “Logistics-Free Test Management”. To some people that’s an oxymoron. But that is only because we have become accustomed in many places to treat test management as logistics management. Logistics aren’t unique to testing.

Logistics are important, but they don’t define test management.

I believe we need to  think about testing as a discipline where logistics choices are made in parallel with the testing thinking. Test Management follows the same pattern. Logistics are important, but they aren’t testing. Test management aims to support the choices, sources of knowledge, test thinking and decision making separately from the practicalities – the logistics – of documentation, test process, environments and technologies used.

I derived the idea of a New Model for Testing – a way of visualising the thought processes of testers – in 2014 or so. Since then, I have presented to thousands of testers and developers and I get very few objections. Honestly!

However, some people do say, with commitment, “that’s not new!”. And indeed it isn’t.

If the New Model reflects how you think, then it should be a comfortable fit. It is definitely not new to you!

One of the first talks I gave on the New Model is here. (Skip to 43m 50s to skip the value of testing talk and long introduction).

The New Model for Testing

Now, I might get a book out of the material (hard-copy and/or ebook formats), but more importantly, I’m looking to create an online and classroom course to share my thinking and guidance on test management.

Rather than offer you specific behaviours and templates to apply, I will try to describe the goals, motivations, thought processes, the sources of knowledge and the principles of application and use stories from my own experience to illustrate them. There will also be suggestions for further study and things to think about as exercises or homework.

You will need to adjust these lessons to your specific situation. It requires that you think for yourself – and that is no bad thing.

Here’s the deal in a nutshell: I’ll give you some interesting questions to ask. You need to get the answers from your own customers, suppliers and colleagues and decide what to do next.

I’ll be exploring these ideas in my session at the next Assurance Leadership Forum on 25 July. See the programme here and book a place.

In the meantime, if you want to know more, leave a comment or do get in touch at my usual email address.

[1] The Axioms of Testing in the Tester’s Pocketbook for example, https://testaxioms.com

What is Digital?

Revolution

If you are not working on a “Digital” project, the hype that surrounds the whole concept of Digital and that is bombarding business and IT professions appears off-putting to say the least. But it would be wrong to ignore it. The Digital Transformation programmes that many organisations are embarking on are affecting business across all industry and government sectors. There is no doubt that it also affects people in their daily lives.

That sounds like yet another hype-fuelled statement intended to get the attention. It is attention grabbing, but it’s also true. The scope of Digital[1] is growing to encompass the entirety of IT related disciplines and business that depends on it: that is – all business.

It is becoming clear that the scope and scale of Digital will include all the traditional IT of the past, but when fully realised it will include the following too:

  • The IoT– every device of interest or value in the world will become connected; sensors of all types and purpose will be connected – by the billion – to the internet.
  • Autonomous vehicles – cars, planes, ships, drones, buses will become commonplace in the next ten years or so. Each will be a “place on the move”, fully connected and communicating with its environment.
  • Our home, workplace, public and private spaces will be connected. Our mobile, portable or wearable devices will interact with their environment and each other – without human intervention.
  • Robots will take over more and more physical tasks and make some careers obsolete and humans redundant. Robots will clean the city, fight our wars and care for the elderly.
  • Software in the form of ‘bots’ will be our guardian angel and a constant irritant – notifying us of the latest offers and opportunities as we traverse our Smart Cities[2].
  • The systems we use will be increasingly intelligent, but AI won’t be limited to corporates. Voice control may well be the preferred user-interface on many devices in the home and our car.
  • The operations or ‘Digital Storm’ of commerce, government, medicine, the law and warfare will be transformed in the next few years. The lives of mid-21st century citizens could be very different from ours.

Motivation

Still not convinced that Digital will change the world we live in? The suggested scale of change is overwhelming. Why is this happening? Is it hype or is it truly the way the world is going?

The changes that are taking place really are significant because it appears that this decade – the 2010’s – are the point at which several technological and social milestones are being reached. This decade is witness to some tremendous human and technological achievements.

  1. One third of the world is connected; there are plans to connect the remaining two-thirds[3]
  2. The range of small devices that can be assembled into useful things has exploded. Their costs are plummeting.
  3. Local and low power networking technologies can connect these devices.
  4. Artificial Intelligencewhich has promised so much for so many years is finally delivering in the form of Machine Learning.
  5. Virtual and Augmented Reality-based systems are coming. Sony VR launched (13/10/2016) to over 1.8million people and Samsung VR starts at under $100.
  6. Robotics, drone technology and 3D printing are now viable and workable whilst falling in cost.

Almost all businesses have committed to transform themselves using these technological advances – at speed – and they are calling it Digital Transformation.

Ambition

If you talk to people working in leading/bleeding edge Digital projects, it is obvious that the ambition of these projects is unprecedented. The origin of these projects can be traced to some critical, but dateless assumptions being blown away. It’s easy to imagine some Digital expert convincing their client to do some blue-sky thinking for their latest and greatest project. “The rules of the game are changed” they might advise:

  • There need be no human intervention in the interactions of your prospects and customers and your systems[4].
  • Your sales and marketing messages can be created, sent to customers, followed up and changed almost instantly.
  • You have the full range of data from the smallest locale to global in all media formats at your disposal.
  • Autonomous drones, trucks and cars can transport products, materials and people.
  • Physical products need not be ordered, held in stock and delivered at all – 3D printing might remove those constraints.
  • And so on.

Systems of Systems and Ecosystems

According to NASA the Space Shuttle[5] – with 2.5 million parts and 230 miles of wire – is (or was) the most complex machine ever built by man. With about a billion parts, a Nimitz class supercarrier[6] is somewhat more complex. Of course, it comprises many, many machines that together comprise the super-complex system of systems – the modern aircraft carrier.

A supercarrier has hundreds of thousands of interconnected systems and with its crew of 5-6,000 people could be compared to an average town afloat. Once at sea, the floating town is completely isolated except for its radio communications with base and other ships.

The supercarrier is comparable to what people are now calling Smart Cities. Wikipedia suggests this definition[7]:

“A smart city is an urban development vision to integrate multiple information and communication technology (ICT) and IoT  solutions in a secure fashion to manage a city’s assets – the city’s assets include, but are not limited to, local departments’ information systems, schools, libraries, transportation systems, hospitals, power plants, water supply networks, waste management, law enforcement, and other community services.”

The systems of a Smart City might not be as complex as those of an aircraft carrier, but in terms of scale, the number of nodes and endpoints within the system might be anything from a million to billions.

A smart city is not just bigger than an aircraft carrier – it also has the potential to be far more complex. The inhabitants and many of the systems move in the realm of the city and beyond. They move and interact with each other in unpredictable ways. On top of that, the inhabitants are not hand-picked like the military; crooks, spies and terrorists can usually come and go as they please.

Unlike a ship – isolated at sea, the smart city is extremely vulnerable to attack from individuals and unfriendly governments and is comparatively unprepared for attack.

But it’s even more complicated than that.

Nowadays, every individual carries their own mobile system – a phone at least – with them. Every car, bus and truck might be connected. Some will be driverless. Every trash can, streetlight, office building, power point, network access point is a Machine to Machine (M2M) component of a Digital Ecosystem which has been defined thus:

“A Digital Ecosystem is a distributed, adaptive, open socio-technical system with properties of self-organisation, scalability and sustainability inspired from natural ecosystems”[8].

Systems of Every Scale

The picture I’ve been painting has probably given you the impression that the Digital systems being now architected and built are all of terrifying scale. But my real point is this: The scale of Digital ranges from the trivial to the largest systems mankind has ever attempted to build.

The simplest system might be, for example, a home automation product – where you can control the heating, lighting, TV and other devices using a console, your mobile phone or office PC. The number of components or nodes might be ten to thirty. A medium complexity system might be a factory automation, monitoring and management system where the number of components could be several thousand. The number of nodes in a Smart City will run into the millions.

The range of systems we now deal with spans a few dozen to millions of nodes. In the past, a super-complex system might have hundreds of interconnected servers. Today, systems are now connected using services or microservices – provided by servers. In the future, every node on a network – even simple sensors – is a server of some kind and there could be millions of them.

Systems with Social Impact

It might seem obvious to you now, but there is no avoiding the fact that Digital systems almost certainly have a social impact on a few, many or all citizens who encounter them. There are potentially huge consequences for us all as systems become more integrated with each other and with the fabric of society.

The scary notion of Big Brother[9] is set to become a reality – systems that monitor our every move, our buying, browsing and social activities – already exist. Deep or Machine Learning algorithms generate suggestions of what to buy, where to shop, who to meet, when to pay bills. They are designed to push notifications to us minute by minute.

Law enforcement will be a key user of CCTV, traffic, people and asset movement and our behaviours. Their goal might be to prevent crime by identifying suspicious behaviour and controlling the movement of law enforcement agents to places of high risk. But these systems have the potential to infringe our civil liberties too.

The legal frameworks of all nations embarking on Digital futures are some way behind the technology and the vision of a Digital Future that some governments are now forming.

In the democratic states, civil liberties and the rules of law are very closely monitored and protected. In non-democratic or rogue states, there may be no limit to what might be done.

Ecosystems of Ecosystems

The span of Digital covers commerce, agriculture, health, government, the media in its various forms and the military; it will affect the care, travel, logistics, and manufacturing industries. There isn’t much that Digital won’t affect in one way or another.

A systems view does not do it justice – it seems more appropriate to consider Digital systems as ecosystems within ecosystems.

This text is derived from the first chapter of Paul’s book, “Digital Assurance”. If you want a free copy of the book, you can request one here.

[1] From now on I’ll use the word Digital to represent Digital Transformation, Projects and the wide range of disciplines required in the ‘Digital World’.

[2] See for example, http://learn.hitachiconsulting.com/Engineering-the-New-Reality

[3] Internet.org is a Facebook-led organisation intending to bring the Internet to all humans on the planet.

[4] Referred to as ‘Autonomous Business Models’.

[5] http://spaceflight.nasa.gov/shuttle/upgrades/upgrades5.html

[6] http://science.howstuffworks.com/aircraft-carrier1.htm

[7] https://en.wikipedia.org/wiki/Smart_city

[8] https://en.wikipedia.org/wiki/Digital_ecosystem

[9] No, not the reality TV show. I mean the despotic leader of the totalitarian state, Oceania in George Orwell’s terrifying vision, “1984”.