Friday, 29 June 2018

On What the Famous John Barnes Rap Can Teach us about Software Engineering Practice

A Short and Tongue-In-Cheek Treatise on what the famous John Barnes Rap (from England's World Cup 1990 official song) can teach us about development and Software Engineering practice.

1) "You've got to hold and give but do it at the right time"

Knowing what and when to delegate is a critical skill of any dev or test lead. Be always aware of the demands placed on your staff and ensure that you delegate work they have the skills and time to finish and no more.

2) "You can be slow or fast but you must get to the line"

There are times when it is understandable to prioritise speed over some quality (i.e. for a new startup with limited resources pushing to be the first to market), however generally it is good to prioritise project quality and delivery over fast completion for the sake of it. Be wary of demands by management for unrealistic or deleterious deadlines. Getting to project completion with good quality is all important.

3) "They'll always hit you and hurt you, defend and attack."

It is true that implementing process change in software engineering may well cause friction in teams. You may face resistance, be it by fearful defensive colleagues or naysayer managers. Influencing skills are crucial.

4) "There's only one way to beat them, get round the back..."

To implement quality principles you may have to alternatively solicit support and resources from PMs, stakeholders and CIOs.

You could also implement a pilot or smaller solution yourself and demonstrate it.

5) "So catch me if you can cause I'm your England man"...

The English software engineering job market is competitive and favours those with quick thinking and the ability to hit the ground running.

6) "And what you're looking at is the master plan"

Overarching master development and test plans are discouraged in agile development but can be sometimes useful. If you do use them, share and explain them with your team and appropriate stakeholders.

7) "We ain't no hooligans. This ain't a football song"

Hooliganism and other antisocial behaviour is deeply unprofessional and should play no part in well functioning software teams. Also remember that loud football chants in quiet workplaces may not be appreciated by colleagues.

Monday, 25 June 2018

10 Minute Rant 2: Ten Skills that are Probably More Important for Testers than Tools and Test Automation

My second ten minute rant blog, designed to break out of a spate of blogger procrastination.

(With credit to Sprint 4: The Bloggers Club on the Club, Ministry of Test)

1) Analysis and Systems Thinking

2) The Ability to Communicate with Tech Colleagues, Support, Management, Business People and Stakeholders

3) Observation, Modelling and Note Taking

4) Curiosity and a Desire for Exploration

5) Tenacity

6) Empathy for the User

7) Business Domain Knowledge

8) Formulating Rational and Consistent Arguments

9) Persuasiveness and Influencing Skills

10) Common Sense

Each of these so-called "soft skills" are critical for testers to truly perform well (IMHO) and yet most generic testing or QA job ads I see these days gloss over the above (if they are even mentioned at all) and involve a checklist of test case management and defect reporting tools, programming and test automation technologies. People can learn tools and how to program - it is much harder to rectify deficiencies in most of the above.

I suspect that the reason for this is that employers and recruiters tend to think that programming and test automation can either overcome deficiencies in the above or that testers are fungible and all have some default level of skill in the above simply by virtue of the fact that they call themselves testers.

Sad really…

Saturday, 14 April 2018

Reflections on the Scripted Test Case

I have my reservations about the pre-scripted test case. I am more happy to use exploratory testing charters, automating the checking part, in my work. Being quite confident in coming up with test ideas on the fly, I find it far more interesting and personally find it results in more defects.
In my career I have only ever worked in one company that used exploratory in a major way (and it was I who implemented it as test lead!). In my experience, where manual approaches are taken the majority of the work is done using pre-scripted tests - based on the examination of requirements documents or user story in some tracking system like JIRA, approved in advance by a test manager or stakeholder and then executed sequentially. The test lead or project manager would monitor the execution of each test, produce lovely graphs and may even set rough targets of “X” tests completed per day.

In fact, according to this 2017 survey on behalf of Practitest, 58% of respondents still used scripted testing (the second highest testing methodology behind exploratory) and 56% created detailed scripted test cases - the latter having actually gone up slightly from 2016.

As a tester I have an uneasy and visceral relationship with this approach, although I have and still do work in projects as a tester with scripted tests. The idea of testing as crossing off a long list of manual checks is a very poor definition – especially in an agile context. When test execution metrics are applied and a daily test execution target set the incentive is not to enquire and investigate but to get through as many tests as possible. We are judged on our efficiency, if this is such a measure of it, and not on our ability to evaluate the product, think outside the book, come up with new real-time strategies – the tasks that should be the “bread and butter” of the testing role. It also relegates our conception and reporting of the product to a sum of minute behaviours taken separately from other. A product is fit for purpose when “enough” of its test pass. Test cases may be regarded as a type of document but as far as documentation goes they are usually pretty poor. If they weren’t then we would be including them in manuals and posting them in our site support section, however this is never done without substantial rewrite.

They also make great presumptions about the product before its release, especially when detailed steps are included. It takes an incredible amount of detailed documentation or a very precise oracle to come up with test case steps that are still relevant at the end of the development process. Done afterwards, the current behaviour of the product can bias their steps and expected outcomes, not to mention the perverse fact that to write the test case steps you have to “test” the product anyway.
Nevertheless, from the perspective of a busy test manager these can be seen as a dream, especially in reporting, communicating and negotiation with the PM, development team and key stakeholders. They provide easy (if somewhat naïve) metrics, almost instant evidence that the test team is “doing something”, and fast answers.

Test cases are a deliverable in themselves (and I have often seen them required as a deliverable to stakeholders before allowing the start of testing).

What are testers doing right now? Writing Test Cases! Are they testing what we want? Take a look for yourself, read the test cases! Is testing efficient? We completed an average of X tests per day! How serious is that defect? It breaks Y test cases, it must be serious! Why do you need more testing staff? Our "velocity" shows that we will only have 75% test case completion by the end of the test cycle, without the extra hands we can’t complete testing!

Test cases, in much the same way as widget creation in factories, act to de-skill the testing process. The real mental effort is involved in coming up with testing scenarios beforehand, however once the cases are done, depending on their detail they can be given to anyone, tester or not, to complete. In this point the tester becomes a pair of hands, an admin assistant with a checklist. Once we decide we want to, we can replace the tester with an automation script – think of all the money saved!

Also, despite its inherent inefficiencies, deskilling, ennui and lack of real depth and flexibility a test strategy of rote execution of a suite of manual scripted checks can be effective and I have worked in successful projects where this has been done. Much of this depends on how much the initial requirements do not change between their elucidation into test cases and the completion of test execution, how “high level” and open to exploration the test scenarios are and the amount of time and human resources available.

The above reasons are why scripted manual tests are so common in IT teams outside of tech companies and those relying on heavy automation, CI and fast regular releases. In fact, where I see a test approach consisting of mostly scripted manual checks, it is nearly always requested from top-down or mandated as part of an overall quality strategy. Otherwise they are enforced by the use of tools such as QTP which have very prescriptive views of testing.

Scripted testing methods are very easy to manage and report on. I do not believe that they are ever going away unless we can apply reporting of exploratory, bug bash and other testing methods that address all of the management communication points above. James Bach and Michael Bolton, in Session Based Test Management, have provided some answers to the reporting challenges above – thus showing that addressing these is not intractable.

Friday, 23 February 2018

On “Testing vs Checking” and other Oppositions - Dialectics and Deconstruction

Software testing appears to be riddled with contradictory narratives argued from opposing sides. We argue about what is "testing" vs "checking", "traditional" vs "modern" testing approaches, "scripted" vs "exploratory" and make arbitrary distinctions to argue about "automated" vs "manual" testing. In these cases, the opposition is not just based on semantics and meaning but also on value (i.e. that one is wholly “better” than the other). Despite efforts of testing practitioners to debate these topics online or at conferences it is arguable that the arguments on either side are not significantly more sophisticated than before and no more likely to be resolved by consensus. In the second of my blogs touching on postmodernism and testing, I look at the nature of oppositional debate and touch on why that could be.

I also, through concepts developed by the philosophers Hegel and Derrida, theorize that we can question the oppositions above when we see them in a text by a process of dialectic and  deconstruction to develop more sophisticated, contextual and progressive propositions.

On Dialectics, the study of Discourse

The reasoned discourse between two opposing arguments being debated is known as a dialectic, a philosophical term dating from the classical Greek period of Socrates and Plato. Among the earliest noted examples are the Socratic dialogues, where statements are clarified via questioning and their logical consequences studied until a contradiction arises that refutes these statements. Socrates' aim was to find truth, therefore he had no qualms about changing his arguments to further that aim. It is closely related to rhetoric (the public defending of beliefs and attacking opponents) but seen as a counterpart to it (notably by Aristotle). In the middle ages it was included within the teaching of Logic.

In modern times the dialectic was revived by the great German philosophers JG Fichte and later, GWF Hegel. Based on ideas by Immanuel Kant, Fichte described the dialectic as the triad Thesis-Antithesis-Synthesis (later misattributed to Hegel), with the definitions below -

• Thesis - Some Statement or Proposition

• Antithesis - A statement that negates the Thesis

• Synthesis - A process in which the Thesis and Antithesis are reconciled to form a new proposition.

In Fichte’s definition, the Synthesis could be used as a Thesis in a future dialectic. The continual synthesis of thesis and antithesis in a chain would constitute more sophisticated understanding and progress.

Credit to

Example 1: Hegel's Being and Nothing

Hegel's book "Logic" provides the following example regarding existence.

•Thesis - Existence must be posited as pure Being

•Antithesis - Pure Being, upon examination, is found to be indistinguishable from Nothing

•Synthesis - When it is realized that what is coming into being is, at the same time, also returning to nothing (in life, for example, one's living is also a dying), both Being and Nothing are united as Becoming.

Example 2: Marx’s Theory of History

Marx and Engels took the Hegelian dialectic from a context purely of ideas to a materialist context (that of the real world of production and economics) to denote how they saw change and progress would develop in societies. In Marx’s theory of history, the thesis and antithesis were different classes within society and the synthesis would be societal transformation resulting from inevitable conflict between these classes.

Image result for marxist dialectic
Credit to

Each of the above involves some overcoming of the negation (antithesis), resulting in keeping the useful parts of an idea to develop a more sophisticated proposition (which Hegel called Aufhebung or Sublation). Hegel stated that this "negation of the negation" would result in incorporating the opposing idea into itself. In doing so this was the foundation of the Hegelian dialectic, providing a counter-approach to the skeptical Platonic dialectical method of “reducto ad absurdum”, where following a contradiction the thesis would be rejected, leaving us with nothing. Hegel believed that contradictions were a necessary outcome of reasoned argument, thus only a synthetic approach would bring us to a complete truth.

Semiotics - A Dualist Notion of Words and Signs

Later in the 1800s, Ferdinand de Saussure, the so-called Father of Linguistics, proposed a structured interpretation of the use of words, signs and utterances. He called the word or phrase used the "Signifier", and the concept or object referred to by the Signifier the "Signified". His great contribution was to note that the Signifier did not necessarily have to have a connection to the Signified - this is completely arbitrary and the brain acts to combine the signifier word to the signified concept based on some customary or agreed relationship. In doing so, the brain synthesizes and provides meaning to the sign. This is a sort of application of the Hegelian Dialectic. The study of the relationships between and use of signs and words as described above is known as Semiotics. For the purposes of this essay, “word” and “sign” will be treated as synonymous.

How can a word and its meaning have no actual connection? Saussure made the case that words only have meaning relative to other words. Therefore the word "tree" only has meaning when distinguished from the words "bush" or "shrub" - only having use as part of an overarching structure of synonyms and antonyms. Also the relationship between Signifier and Signified changes over time, such that some words become dated as circumstances change. In this respect, Saussure was considered a pioneer in the field of "Structuralism".

Derrida - Spot the Différance

Jacques Derrida

In 1963 the great French Postmodern philosopher Jacques Derrida took de Saussure's ideas to the next level. He stated that signs (including words) can never in themselves denote what they mean. They can only do so by deferring to a potentially endless chain of other words (signifiers) that they differ from. Also our understanding of word meaning changes with each reading and over time with the changing definition of existing and new introduction of new words.

This results in what he called "espacement" or spacing, stating that this differentiation causes binary oppositions and hierarchies.

Derrida coined the term "différance" (sic), a deliberate misspelling to denote not just the difference between words but the concept of hierarchy and deferral (to defer in French is "differer"). It includes the fact that the word can only have meaning within the context of the text ("there is no outside text").
According to Derrida, this chain of signifiers will never get to a point of a complete meaning that transcends context and is understood at all times and by all parties (the so called "transcendental signified", used in philosophy as an ontological argument for the existence of God).

However, he pointed out that the opposition of words and signs with others express not just meaning but values.

"On the one hand, we must traverse a phase of overturning. To do justice to this necessity is to recognize that in a classical philosophical opposition we are not dealing with the peaceful coexistence of a vis-a-vis but rather with a violent hierarchy. One of the two terms governs the other (axiologically, logically etc.) or has the upper hand.

To deconstruct the opposition, first of all, is to overturn the hierarchy at a given moment, To overlook this phase of overturning is to forget the conflictual and subordinating structure of opposition."

Binary Opposition

Many word pairs in binary opposition have not just logical but also value oppositions depending on their use. Examples include "Christian" and "Pagan", "Left" and "Right" (philosophical/political sense), "Speech" and "Writing", "reality" and "fiction" and "reason" and "passion". Different authors and readers at different times will value one over the other. Depending on their understanding by the reader, their definition at the current time and their use in the text, the differences in meaning and value between the two are variable and changing. Derrida called these discrepancies "traces", thus stating that a word not only denotes its own meaning but, by the its partner in binary opposition, what it does not mean (which he referred to as the "absence of the present").

This "deconstruction" was the ongoing practice denoted by Derrida for all texts to be studied for conceptual and temporal oppositions and these broken down to develop new meaning. It formed his life's work and is heavily influential in postmodern philosophy, law, linguistics, LGBT studies, psychoanalysis and literary theory. Note that the structures of word opposition remain and are required for any sort of communication, however the act of deconstruction allows for them to be broken apart and the gaps between the implied sense of each word in opposition to be analysed and understood within the context of the text.

It must be stated that according to Derrida, unlike Hegel's Dialectic, these oppositions (Thesis and Antithesis) are irreducibly complex and unstable. They could be understood and their différance noted and determined, however they could not be synthesised into a whole as Hegel proposed.

"The oppositions simply cannot be suspended once and for all. The hierarchy of dual oppositions always re-establishes itself. Deconstruction only points to the necessity of an unending analysis that can make explicit the decisions and arbitrary violence intrinsic to all texts."

The above ideas are controversial and have been disputed (particularly by John Serle and Alan Sokal) however they can be used as a model to approach the contradictions and arguments stated in my introduction.

Applications to Testing Discourse

By applying the concepts above we can make assertions about the oppositions so heavily debated in testing. The pairs "testing" vs "checking", "scripted" vs "exploratory" and "automated" vs "manual" testing, in the context of how they are usually debated, are treated as binary opposites. As stated in the introduction, arguments around them also imply not just logical or semantic opposition but value opposition (i.e. that one is "better" or more worthy than the other).

An Example - Testing vs Checking

Let us take a look at the first example, “testing” vs “checking”.

Personally I would define “testing” as a systematic exploration and investigation of the application, less algorithmic and more a constant thinking process. I would equally define checking as a more systematic procedure of experimentation and comparison of a scenario outcome with some expected result or test oracle, then moving on. However my definitions are meaningless without appeal to the implied meanings of the other words in my propositions above - and the words they are related to. I also admit that the simple words “testing” and “checking” are probably inadequate in explaining what I had in mind. In taking sides or commenting on a debate of “testing” vs “checking” it may be that I state or imply a hierarchy of value - i.e. that “testing” is better (or worse) than “checking”.

"Testing" defers meaning in its relation to other words such as "exploration", "analysis", "scrutiny", "thinking", "investigation" - which themselves can only be defined by relation to others. Equally the word "checking" also only has meaning in its relationship to other words such as "control", "find out", "learn", “comparison” - each deriving meaning by relation to other words. At some point the "web of language" (another term defined by Derrida) may overlap and both words may derive meaning from at least some other common words, however this does not necessarily mean that they are the same.

They are (contentiously) defined by what they are not. "Testing" is "not" "checking" (same as "Automated" is not "Manual" etc). Their meanings cannot be determined outside of the context of the text they were written in, the time they were written and the fickle views and understandings of the writer and reader. No transcendental meaning exists outside of this.

These represent a dialectic, and from a Hegelian perspective an effort should be made to synthesize them into a whole. However a deconstructionist perspective would dictate that these differences are necessary (at least until some new words are developed with more sophisticated meanings) and irreconcilable and contain an inherent violence of meaning and value. This may explain why after many years we are still having angry debates online and at conferences about these oppositions.

So what can be done? The way to address this is to deconstruct each use of the words in their context and time.

• For example, when we see the word "testing", does this mean a specific incident of testing or the general practice?

• Instead of testing, what other words can be used to describe what the practitioner is doing? Is the word "testing" adequate?

• Who is apparently doing the "testing" - one person, a machine, a group of people, a context driven tester?

• When was the text written and how was testing defined at that time?

• Can a single word apply to all investigative approaches in every company? What does the author think of testing and what other words are used to describe it? What about the reader?

• Are there instances where "testing" and "checking" could mean the same thing? What may these be?

• Regarding the "value" of testing vs checking, who says that one is better than the other? How is it, and under what circumstances can that hierarchy be overturned?

• What "testing" are they referring to, based on the questions above, and in what context? Has one always been better than the other?

What can those who engage in the debates do to clarify the points above to the reader? One way is to publicly state clear and sophisticated definitions of “testing” and “checking” as one sees them. James Bach and Michael Bolton, in their co-written 2013 article “Testing and Checking Refined”, go to great lengths to do just that - along with their definitions of related words that would fall within each one’s “web of language” (such as evaluation, exploration, learning etc.). Also included are narratives of the definitions along with various implications.

“Testing is the process of evaluating a product by learning about it through exploration and experimentation, which includes to some degree: questioning, study, modeling, observation, inference, etc.

(A test is an instance of testing.)

Checking is the process of making evaluations by applying algorithmic decision rules to specific observations of a product.

(A check is an instance of checking.)“

However, while this is better, from Derrida’s perspective the extensive web of language behind these definitions and their related words, in the specific text and time they were written in, will always prevent these definitions from being perfect and unchanging, the basis of a founding philosophy of software testing.

Michael Bolton, in a follow-up piece on his blog, also attempts to take the hierarchy of value out of the debate. He makes it clear that his (and James’) side of the debate is to reconcile checking in a more complex definition of testing that embodies but distinguishes between the human, reflective and thought driven tasks and algorithmic/mechanical and process driven tasks.

“I would like to emphasize our goals here. Our purpose is not to denigrate checking, nor to disparage the use of tools, nor to deplore those people who are asked to do human checking. On the contrary: we’re attempting to deepen our understanding of our craft; to show that checking is deeply embedded in testing; to emphasize that tools and the skilled use of them are essential to our work in many ways; to realize that humans will always inject human elements into the things they do; to realize the value of those human elements and the risks involved in asking humans to behave like machines. We must be clear on the differences between what humans do and how our processes and tools—media, as McLuhan would call them—do. Or more accurately, the differences between what we do and how our tools affect what we do.”

This removes the opposition entirely by making checking a subset of testing - arguably a successful Hegelian, synthetic approach.


The questions posed in the example of the binary opposition “testing” / “checking” can be applied to texts containing the other contentious testing oppositions mentioned.

The process above is necessary, if not to remove or synthesise oppositions out of the picture, to create propositions and assertions with more contextually accurate and sophisticated meanings. With the debates we are currently having, unless we deconstruct the concepts we are using in the texts they are written in we are arguing over shadows. Then can we make headway in some of the tense binary oppositions that have dogged the testing profession for many years.

Useful Links and References

James Bach, “We use tools”

Jermais Rossler, “Testing vs Checking, so what?”

James Bach (cowritten with Michael Bolton), “Testing and Checking Refined”

Michael Bolton “On Testing and Checking Refined”

Wednesday, 3 January 2018

Some of My 2018 Learning Goals, and Free Resources to Help in Yours

With us now in January 2018 and the Christmas pies and frivolities becoming a distant memory, it is traditional to see this time as that act of penance and discipline! We enrol for the local gym, take a month off the drinking and increasingly look to improve our skills.

My efforts to eliminate the Christmas gut being beyond the scope of a software testing blog, I wish to detail with you a few of my NY resolutions to develop my skills and achieve my various goals. In the hope that you may find them useful in your learning endeavours, I list various relevant free resources I have come across.

1) Practising Programming

I am studying a part time IT graduate diploma, with the option to articulate to a Masters degree if I choose to. This is already very programming-intensive (and I have used Python, SQL, C, Java, Bash, Prolog and Perl as part of my course), however my last module done in the first half of 2017 was programming-lite and I took the second semester of 2017 off entirely. Not working in an automated testing role at the moment and not having a lot of side projects on, my programming skills are (hopefully temporarily) rusty.

In order to hit the ground running when my course restarts in late Feb, and also to develop my coding generally, I am endeavouring to devote an hour per day to learning coding - for application development and test automation (more on that later).

Whilst there are more online resources for this then I could possibly list, in just about any language existent, I am particularly fond of the professional Massive Open Online Courses (MOOCs) at Pluralsight and Edx.

Pluralsight is my personal favourite of all the various online tutorial sites, and its videos and tutorials are generally of a very high quality. It is not free, however for those with an Office Online email account it is easy to obtain a free 3 month subscription to this via registering on Microsoft's Visual Studio Dev Essentials site.

Edx, a well known academic MOOC provider, contains many courses but I recommend a truly excellent introduction to programming - MIT 6.001x Introduction to Computer Science and Programming Using Python - which is self paced and can be audited (done without certification) for free. Completing it is how I learned and got a grounding in Python. It is not an easy course and the weekly programming challenges and quizzes are quite challenging and need to be completed on time. It is however extremely rewarding. The follow-on course for those who complete the above (and which I have not completed myself), MIT 6.002x Introduction to Computational Thinking and Data Science, uses Python to apply various concepts from Computer Science such as graph optimization and dynamic programming before moving onto statistical methods such as Random Walks and Monte Carlo. I plan to return to MIT 6.002x at some point this year.

2) Test Automation

With respect to test automation resources, I am keen to devote myself to learning API testing before returning to UI automation.

In view of this, since I already have some experience of using SoapUI I will try to learn Postman API. Danny Dainton provides some great tutorials and examples on his Github project, which I plan to work through.

3) Machine Learning

I took an AI module a few years ago and was fascinated by it. It was going to study a machine learning module as part of my degree however am not able to do it this year due to missing a certain prerequisite. I therefore wish to, and coming from a strong maths background should be able to study Machine Learning is my own time this year and see how proficient I can get.

I have been avidly watching and doing a few homework exercises from two courses by Nando de Freitas, a professor of Statistics and Machine Learning at the University of Oxford and the University of British Columbia. His lecture style is engaging and easy to follow and the courses are quite entertaining.

The UBC undergraduate machine learning course from 2012, for which all lectures are on youtube and exercises and projects are available, encompasses material from basic probability theory right up to Bayesian Classification and Deep Learning. With the exercises requiring a knowledge of Python, various tutorials are linked to and included.

As a follow on, de Freitas' Oxford course in Deep Learning goes much deeper into the theory, however whilst the videos are freely available the course notes and exercises are sadly hidden to non-students. It is still worth checking out however.


The above projects are what I plan to work on for the first half of this year, my academic studies permitting. If you have any ideas of other resources or projects that I could learn from, I would be most grateful.

Tuesday, 5 December 2017

Some Trust in Models! On Simulations, HyperNormalisation and Distorted Reality in Software Teams (Part 1)

Still from Adam Curtis' 2016 documentary Hypernormalisation

Software is all about abstraction. We take an existing or desired process, model it and codify that model into a set of instructions and data that computers can read and follow.

We come up with different processes, methodologies, notional lifecycles, workflows, test strategies, measurements and quality controls to steer our immensely difficult projects to a satisfactory conclusion. For that we get teams of people with different roles and skills to work together, manage the creation and deployment of code with its own complexities and dependencies and implement what (we believe) users want is incredibly hard.

The processes we use to create software are also abstractions. The problems we face are, how much do the abstract requirements, process and methodologies we implement really reflect a desired real world outcome? In the midst of a software project, how do we maintain the link between abstraction and reality? What happens when a software team loses the ability to know about the difference between simply fulfilling the process and getting to a real world product people actually want? More dauntingly, at what point will the team stop caring?

Everything Was Forever Until It Was No More

A stamp featuring Pimenov's "Wedding on a Tomorrow Street", 1973

Anthropology and Sociology provide some interesting allegories that within limits help us to look at the questions above. The Russian academic Alexei Yurchak in his 2006 book "Everything Was Forever Until It Was No More: The Last Soviet Generation" discussed the paradoxical nature of life in the 70s and 80s Soviet Union. People were realising the disconnect between the ideological and propagandistic announcements of a Soviet state that saw itself as successful and immutable ("the end of history" as Marx envisaged) and the increasingly obvious fact that its economy was stagnant, its institutions were failing, events such as the War in Afghanistan were hurting its collective psyche and their quality of life was getting worse by the day. However in the absence of an alternative, whilst they understood something wasn't quite right they remained dissonant and carried on with the pretense that nothing was wrong, deluding themselves into a self-fulfilling prophecy. All the while the national discourse was piling more upon itself and becoming even more repetitive, in unwavering copying and reinforcement of earlier dogma. When the Soviet Union did collapse so quickly and dramatically, its disintegration was shocking to most Soviet citizens who had seen it as eternal. Yurchak coined this condition "Hypernormalisation".

The disconnect between the official narrative and the reality of life in the final decades of the Soviet Union gave rise to artistic protests of a satirical nature, with films such as Andrei Tarkovsky's "Stalker" describing a world that was outwardly similar to ours and could fulfill all our dreams but at a deeper level profoundly different and nightmarish.

Yurchak's book had a profound influence on the English BBC journalist and filmmaker Adam Curtis. His impressive 2016 documentary "HyperNormalisation" expressed the belief that this had been extended to the whole world. In his view, from the 1970s onwards our leaders and media figures had grown tired of struggling to articulate and tackle the complex problems of the real world, resigned themselves to managing the current order and predicting risks and created a "fake world" which trivialised long-existent complex social, economic and political issues into half-baked narratives of "Good vs Evil" with misplaced or contrived enemies. Instead of challenging themselves and the narratives told to them, people turned to virtual worlds in cyberspace that were designed to be free but had powerful hierarchies of their own - harvesting the information and activities of billions of people and and filtering the media people saw to reinforce instead of challenging their opinions.

Consequently people either grew to be dissonant of the "fake" perception-managed world and the real world, accepted the narrative as fact and opted into solipsism and self-focused activities or used the internet to create huge protests and revolutions. Those who retreated became susceptible to agents who were skilled in maintaining confusion and instability - the film uses the examples of then-candidate Donald Trump and Vladimir Putin's strategist Vladislav Surkov - turned politics and news into bewildering theatre such that facts and truth no longer became politically relevant and rumours and chaos reigned. This left them much more open towards the status quo (as in Putin's Russia) or demagoguery (as in the 2016 US Elections).

For those who revolted, in the absence of a compelling alternative direction of society these revolutions failed tragically and spectacularly, such as the protests in Egypt that were hijacked by the Muslim Brotherhood, eventually to be deposed by the military, or the Syrian Civil War that led to the rise of ISIS.

The Road to Disneyland. Simulation and Simulacra.

Pluto, Dale and Daffy Duck on a firetruck, Disneyland Anaheim, 2010

Both of the narratives above borrow from postmodernism, particularly semiotics (the study of communication with and assigning meanings to symbols and signs). The French sociologist Jean Baudrillard in his controversial 1981 treatise "Simulacra and Simulation" described the process where human culture progressively replaces our direct experience of reality with a series of signs and symbols (which he refers to as "simulations") - copies and representations of the real world with a seductive quality.

Over time the simulation is tweaked and copied to the point where it deviates from the original to the point where it is a (in his words) an "evil appearance—it is of the order of maleficence". This continues to the point where any relationship with the original reality is semantic and arbitrary at best.

The end point of this, which he calls a "simulacrum", is a simulation or copy that has been abstracted to the point where it has no relationship to an original at all - only to other signs that may themselves be simulacra. Our life experience of the simulacrum, not being based on any grounded physical object, is not even acknowledged or cared to be based on reality - yet it becomes a “truth" in its own right. This artificial existence is known as "Hyperreality".

The most common example stated, referred to by Baudrillard and Italian philosopher Umberto Eco, is Disneyland. The facades of Main Street with its allusion to an idealised past create a facade that appeals to our imaginations and desires. The Big Thunder Mountain ride is an exciting allusion to old mining rail tracks in a cowboy setting. Nevertheless, Main Street as a real street never existed and mining rail tracks of bygone times were not anything remotely like Big Thunder Mountain. Of course, Disneyland is so seductive that none of this matters. We engage with the fantasy world as if it were real. This is the definition of the Hyperreal.

Other stated examples of simulacra include historical fallacies that have been promoted widespread as facts, artificial substitutes for human interaction such as sex dolls and AI chatbots, films with digitally created CGI characters and backgrounds and "structured" reality TV shows.

A Postmodern Perspective on Software Delivery

I came across the work of Baudrillard along with Adam Curtis' "HyperNormalisation" at about the same time in 2016, after about nine years of working as a tester. Whilst drawing conclusions from ideas from sociology and anthropology applied to software teams without thought is a flawed and dangerous activity, I believe that they provide a lens through which our inability to deal with the inherent contradictions behind key concepts in software engineering can result in software teams falling into flawed and misused practices - and why even if they are suspected to be flawed and misused, teams fall into dissonance and fail to act right up to product catastrophe.

For this I offer perspectives on four concepts critical to software engineering and testing - quality, measurement, requirements and methodology/process.

Quality - Relative at Best, Simulacrum at Worst

The usual aim of a software team is to deliver a "quality product", however Quality is a vague and shifting target. Gerald Weinberg, seen as the "Godfather of Agile", wrote in his 2012 blog article "Agile and the Definition of Quality" on the "Relativity of Quality' - "what is adequate quality to one person may be inadequate quality to another." He states the following -

"If you examine various definitions of quality, you will always find this relativity. You may have to examine with care, though, for the relativity is often hidden, or at best, implicit.

Take for example Crosby's definition:

"Quality is meeting requirements."

Unless your requirements come directly from heaven (as some developers seem to think), a more precise statement would be:

"Quality is meeting some person's requirements."

For each different person, the same product will generally have different "quality," "

He list various statements defining quality from the point of view of specific project stakeholders.


a. "Zero defects is high quality."

1. to a user such as a surgeon whose work would be disturbed by those defects

2. to a manager who would be criticized for those defects

b. "Lots of features is high quality."

1. to users whose work can use those features–if they know about them

2. to marketers who believe that features sell products

c. "Elegant coding is high quality."

1. to developers who place a high value on the opinions of their peers

2. to professors of computer science who enjoy elegance



His ultimate definition of Quality - that it is "Value to Some Person" - has consequences to agile teams. He states that the definition of quality is "political and emotional" and thus leads to decisions on whose opinions count most. Also these decisions can be less than rational and are hidden from public view.

It is my assertion that in cases where not enough thought has been given to grappling with the contradictions above, a team’s conception of “Quality” has little in relation on the reality of the product (to the extent that the reality can be defined or measured) or the aims of the project. Quality as a symbol or concept becomes nothing more than the biased opinion or dogma of some powerful manager or stakeholder or a vague guess of the desires of some abstract “target market”. However in the perceived absence of anything better, the team acts as if it were the truth. It becomes a simulacrum.

The effect of this is we have started to give up the belief that we can make a “quality" product. James Bach, in his 2009 blog article “Quality is Dead #1", paints a despairing picture -

“A pleasing level of quality for end users has become too hard to achieve while demand for it has simultaneously evaporated and penalties for not achieving are weak…. When I say quality is dead, I don't mean that it’s dying, or that it’s under threat. What I mean is that we have collectively - and rationally - ceased to expect that software normally works well, even under normal conditions. Furthermore there is little any one user can do about it.”

Bach asserts that the result of this disillusion with the possibility of quality is that management have given up and moved towards the lowest common denominator approach - dispensing of good test teams and moving to cheaper and less capable offshoring teams.

“Top management can’t know what they are giving up or what they are getting. They simply want to spend less on testing. When testing becomes just a symbolic ritual, any method of testing will work, as long as it looks impressive to ignorant people and doesn’t cost too much.”

This “going through the motions” while publicly acting as if quality is being improved, as I see it, is a type of mini-hypernormalisation.

The Question of Measurement

Measuring an attribute that does not lend itself to precise and agreed definition and arises from human feelings and politics is evidently problematic and will lead to inconsistent and tenuous results. Rich Rogers, in his 2017 book "Changing Times: Quality for Humans in a Digital Age" writes -

"If stories and feelings tell us more about human responses than cold facts, dates and numbers, this might provide a clue as to why software development teams, and the organisations they work with, sometimes struggle with the question of quality."

"In this field, refuge is often sought in numbers.... By setting numeric quality targets - sometimes called exit criteria - a desired state can be agreed upon: a point at which the product is deemed good enough for whatever follows, including whether it is ready to be released to customers. The numbers act as a comforting security blanket, creating a sense of control. Even if the picture they paint is troubling, at least the provide a means of seeing that picture."

However as Rich Rogers continues to explain, this is an abstraction that while comforting, is limited and prone to mislead. Crude metrics such as counting of passed test cases and defects assume an equivalence between test cases that isn't likely to exist. They also hide the types of investigation and testing outside of these test cases along with defects that were resolved without the need to record.

"Quality is not measurable in the way cost or time might be measured... There is no such unit of measurement for quality."

He makes the case that metrics still have a role to play in discussions about patterns and potential issues. Reports are only useful in triggering those discussions - about what the metrics tell and do not tell.

The Illusion of Requirements

What about requirements? If one takes a list of use cases, desired functionalities and acceptance criteria - and the product contains all of these working to some agreed order - does that mean we have a quality product?

I believe that this is also tenuous at best and a comforting fantasy at worst. Requirements follow the same rules as "quality" defined by Weinberg. They depend on the judgement, political power and knowledge of the person who defined them - as filtered through project stakeholders, business analysts and the printed page.

The person who conceived the requirements, we have to assume, has a correct knowledge of the problem to be solved. Anyone with experience of software development will know of circumstances where that is a bad assumption. They may arise from a high level abstraction of an existing business process, ignorant of the day to day challenges and tacit knowledge of those implementing the process. The person who stated the requirements may be a high level manager without any experience of ever implementing the process, a slave to the reports of subordinates.

The illusion doesn't end there. Various test case management tools allow use cases and acceptance criteria to be linked to one or more test cases, generating requirements traceability. This is also flawed for the same reason that test case metrics are flawed. They make presumptions that quality in a requirement can be neatly expressed by X test cases passed in an associated matrix, or an hour's session based exploratory testing revealing no defects. This is very wrong as any tester with experience will tell you.

Methodologies and Primacy of the Process

In response to the above, there have been created many different approaches to a software development project along with practices within them. Even within Agile we have XP, Kanban, TDD, BDD, Continuous Integration, Scrum, SAFe, DevOps… Even within testing we have competing schools and methodologies. These are all simulations of how teams work in practice - very rarely do teams follow a prescribed methodology to the letter. How one team defines “agile” can have only a tenuous relationship to another even within the same company.

The danger is that the process is deemed to matter at the expense of the outcome, or implemented to provide benefits to the adherents without respect to the project. For new adherents, as Rich Rogers points out -

"The techniques and tools used in carrying out the work can seem exciting, and there is no shortage of new ideas and skills to learn. The desire to be at the forefront of change, or at the very least to demonstrate awareness and familiarity with current methods, can be a powerful motivator in this field. Adoption of new techniques, training in how to use new tools and acquisition of knowledge and skills related to new ideas, or possibly a desire to enhance a resume."

One area where the hype and impetus to learn is already causing an effect is in test automation. The drive to automate more (if not all) tests, and its effects on tester recruitment, causes testers to devote project and personal time to learning automation tools and programming - and to implement them without experience or real forethought. I know from my own experience the waste and effects of poorly implementing an automation strategy based surreptitiously on a desire to learn a new skill in a commercial context.

Another danger is that processes are defined at the company level and not the team level. Because they were believed to work before, we enforce processes and methodologies on other projects in an effort to "standardise". This may make sense at a management, resource allocation and control level, however projects with ill-considered, enforced processes risk great failure.

Leaky Abstractions in a Complex World

If we understand the flaws in defining quality, measurement and requirements, and our methodologies are idealisations, why do so many teams still persist in making critical project decisions based on their flawed understanding of them? Is it purely ignorance or something else?

Rich Rogers makes the point that metrics, however flawed, act as a comforting security blanket, providing some basis for decision making. We may not be measuring the right thing but we are measuring SOMETHING that may or may not be close to quality.

In the same way, requirements may be incomplete, flawed or at the extreme end utterly irrelevant to the problem we wish to solve, however they do exist. One can use them for contracts, resourcing, planning, development, testing and delivery. In agile methodologies we can continually add changes until (we hope) the end product approaches something the user or stakeholders might want.

In any case these, along with working definitions of quality, are all models of the "real" state of a project or product. For better or for worse they are vital in reducing a development project from something almost ineffable to something that can be planned or achieved. I treat them as "simulations" as defined by Baudrillard. Because of this, they are greatly seductive.

What matters is their relationship to the real state of the product and the development process.

Hyperreality and the Failing Team

My conjecture is that in projects that are dysfunctional or fail to provide what we call a quality outcome, these simulations are so far removed as to be devoid of reality. In the same way that Disneyland is a simulacrum, so are these.

However, simulacra are greatly seductive simply because they are easier than embracing the difficult to manage real world they pretend to describe. Hyperreality is not real but its sway on team certainly is. Also, changing approaches and mindsets within teams and at the company level, especially without either the privilege of management support or a clear narrative of a better solution and how to get there, rates from difficult to impossible. The closer to the project deadline, after so much has been invested and with the probably painful consequences of a late or failed delivery, the less the team will be willing to experiment and the more the team is likely to act is if nothing is wrong. Status reports show green, problems are ignored, process and the simulacra are gospel - and then the Soviet Union collapses.

Nevertheless, as with the final decades of the Soviet Union, it is probable that a few team members at some level know that something was wrong with the system even if they could not articulate the problem and don't see an alternative. They may follow "best practice" to the letter, see great test coverage as defined by the flawed metrics above, defects resolved, requirements ticked off but their stakeholders still complain, or their clients find defects that are tangential to or don't make sense based on the requirements they worked with. Everything is said to be running smoothly but everyone and everything is stressed. This is what I regard as HyperNormalisation - hyperreality with a subtle chink in the armour.

In companies that are greatly hierarchical, have established and immutable processes and teams with egos and dominant personalities reliant on the status quo it is likely that the unnerved team members above, especially if not at senior level, will keep quiet. They may decide that "Nobody got fired for just doing their job", or patiently wait for the end of the project. They may ask to be moved to another project or if the stress becomes an issue take sick leave. They may go through the motions of work without committing fully to it. These are a protest of a sort, but all they do is accelerate the eventual collapse and slump in quality.

The unfortunate truth is that without some shock that forces the team to reflect on its assumptions and processes, most teams trapped in a seductive but vicious hyperreality are not likely to be self aware enough to change until the project suffers dramatically or collapses entirely.


In this text I have covered concepts of hypernormalisation, simulacra and simulation and hyperreality and suggested them as analogies to look at four key concepts in software engineering - quality, measurement and requirements and methodology/process - and their impact on software projects. I have asserted that our inability to understand that these are ultimately seductive simulations with varying levels of relationship to the real world, and the difficulties of teams to reconcile these, cause great risk of projects deluding themselves into poor products and failure.

In Part 2, to be completed shortly, I look at applying these concepts to the IT industry and our tech culture as a whole, and look into various ways suggested to prevent or rescue teams from seductive self delusion. In the meantime, I would be grateful for your comments and considerations.