Ed. Note – This is Part 2 of a Two-Part blog post produced by James Christie, supported by the AST Committee on Standards and Professional Practices. Part 1 is here. To learn more about why so many senior software testers are interested in Cynefin, come to CAST 2017 and see Dave Snowden’s Keynote.
This post is the second of two discussing Dave Snowden’s recent Cynefin masterclass in New York. In the first I gave an overview of Cynefin and explained why I think it is important, and how it can helpfully shape the way we look at the world and make sense of its problem. In this post I will look at some of the issues raised in Dave’s class and discuss their relevant to development and testing.
The dynamics between domains
Understanding that the boundaries between the different domains are fluid and permeable is crucial to understanding Cynefin. A vital lesson is that we don’t start in one domain and stay there; we can and should move between them. Even if we ignore that lesson reality will drag us from one domain to another. Dave said “all the domains have value – it’s the ability to move between them that is key”.
The Cynefin dynamics are closely tied to the concept of constraints, which are so important to Cynefin that they act as differentiators between the domains. You could say that constraints define the domains.
Constraint is perhaps a slightly misleading word. In Cynefin terms it is not necessarily something that compels or prevents certain behaviour. That does apply to the Obvious domain, where the constraints are fixed and rigid. The constraints in the Complicated domain govern behaviour, and can be agreed by expert consensus. In the Complex domain the constraints enable action, rather than restricting it or compelling it. They are a starting point rather than an end. In Chaos there are no constraints.
Dave Snowden puts it as follows, differentiating rules and heuristics.
“Rules are governing constraints, they set limits to action, they contain all possible instances of action. In contrast heuristics are enabling constraints, they provide measurable guidance which can adapt to the unknowable unknowns.”
If we can change the constraints then we are moving from one domain to another. The most significant dynamic is the cycle between Complex and Complicated.
Crucially, we must recognise that if we are attempting something new, that involves a significant amount of uncertainty then we start in the Complex domain exploring and discovering more about the problem. Once we have a better understanding and have found constraints that allow us to achieve repeatable outcomes we have moved the problem to the Complicated domain where we can manage it more easily and exploit our new knowledge. If our testing reveals that the constraints are not producing repeatable results then it’s important to get back into the Complex domain and carry out some more probing experiments.
This is not a one off move. We have to keep cycling to ensure the solution remains relevant. The cadence, or natural flow of the cycle will vary depending on the context. Different industries, or sectors, or applications will have different cadences. It could be days, or years, or anything in between. If, or rather when, our constraints fail to produce repeatable results we have to get back into the Complex domain.
This cycle between Complex and Complicated is key for software development in particular. Understanding this dynamic is essential in order to understand how Cynefin might be employed.
Setting up developments
As I said earlier the parts of a software development project that will provide value are where we are doing something new, and that is where the risk also lies. Any significant and worthwhile development project will start off in the Complex domain. The initial challenge is to learn enough to move it to Complicated. Dave explained it as follows in a talk at Agile India in 2015.
“As things are Complex we see patterns, patterns emerge. We stabilise the patterns. As we stabilise them we can actually shift them into the Complicated domain. So the basic principle of Complexity-based intervention is you start off with multiple, parallel, safe-to-fail experiments, which is why Scrum is not a true Complexity technique; it does one thing in a linear way. We call (these experiments) a pre-Scrum technique. You do smaller experiments faster in parallel… So you’re moving from the centre of the Complex domain into the boundary, once you’re in the boundary you use Scrum to move it across the boundary.”
Such a safe-to-fail experiment might be an XP pair programming team being assigned to knock up a small, quick prototype.
So the challenge in starting the move from Complex to Complicated is to come up with the ideas for safe-to-fail pre-Scrum experiments that would allow us to use Scrum effectively.
Dave outlined the criteria that suitable experiments should meet. There should be some way of knowing whether the experiment is succeeding and it must be possible to amplify (i.e. reinforce) signs of success. Similarly, there should be some way of knowing whether it is failing and of dampening, or reducing, the damaging impact of a failing experiment. Failure is not bad. In any useful set of safe-to-fail experiments some must fail if we are to learn anything worthwhile The final criterion is that the experiment must be coherent. This idea of coherence requires more attention.
Dave Snowden explains the tests for coherence here. He isn’t entirely clear about how rigid these tests should be. Perhaps it’s more useful to regard them as heuristics than fixed rules, though the first two are of particular importance.
- A coherent experiment, the ideas and assumptions behind it, should be compatible with natural science. That might seem like a rather banal statement, till you consider all the massive IT developments and change programmes that were launched in blissful ignorance of the fact that science could have predicted inevitable failure.
- There should be some evidence from elsewhere to support the proposal. Replicating past cases is no guarantee of success, far from it, but it is a valid way to try and learn about the problem.
- The proposal should fit where we are. It has to be consistent to some degree with what we have been doing. A leap into the unknown attempting something that is utterly unfamiliar is unlikely to gain any traction.
- Can the proposal pass a series of “ritual dissent” challenges? These are a formalised way of identifying flaws and refining possible experiments.
- Does the experiment reflect an unmet, unarticulated need that has been revealed by sense-making, by attempts to make sense of the problem?
The two latter criteria refer explicitly to Cynefin techniques. The final one, identifying unmet needs, assumes the use of Cognitive Edge’s SenseMaker. Remember Fred Brooks blunt statement about requirements? Clients do not know what they want. They cannot articulate their needs if they are asked directly. They cannot envisage what is possible. Dave Snowden takes that point further. If users can articulate their needs than you’re dealing with a commoditized product and the solution is unlikely to have great value. Real values lies in meeting needs that users are unaware of and that they cannot articulate. This has always been so, but in days of yore we could often get away with ignoring that problem. Most applications were in-house developments that either automated back-office functions or were built around business rules and clerical processes that served as an effective proxy for true requirements. The inadequacies of the old structured methods and traditional requirements gathering could be masked.
With the arrival of web development, and then especially with mobile technology this gulf between user needs and the ability of developers to grasp them became a problem that could be ignored only through wilful blindness, admittedly a trait that has never been in short supply in corporate life. The problem has been exacerbated by our historic willingness to confuse rigour with a heavily documented, top-down approach to software development. Sense-making entails capturing large numbers of user reports in order to discern patterns that can be exploited. This appears messy, random and unstructured to anyone immured in traditional ways of development. It might appear to lack rigour, but such an approach is in accord with messy, unpredictable reality. That means it offers a more rigorous and effective way of deriving requirements than we can get by pretending that every development belongs in the Obvious domain. A simple lesson I’ve had to learn and relearn over the years is that rigour and structure are not the same as heavy documentation, prescriptive methods and a linear, top-down approach to problem solving.
This all raises big questions for testers. How do we respond? How do we get involved in testing requirements that have been derived this way and indeed the resulting applications? Any response to those questions should take account of another theme that really struck me from Dave’s day in New York. That was the need for resilience.
The crucial feature of complex adaptive systems is their unpredictability. Applications operating in such a space will inevitably be subject to problems and threats that we would never have predicted. Even where we can confidently predict the type of threat the magnitude will remain uncertain. Failure is inevitable. What matters is how the application responds.
The need for resilience, with its linked themes of tolerance, diversity and redundancy, was a recurring message in Dave’s class. Resilience is not the same as robustness. The example that Dave gave was that a seawall is robust but a salt marsh is resilient. A seawall is a barrier to large waves and storms. It protects the harbour behind, but if it fails it does so catastrophically. A salt marsh protects inland areas by acting as a buffer, absorbing storm waves rather than repelling them. It might deteriorate over time but it won’t fail suddenly and disastrously.
An increasing challenge for testers will be to look for information about how systems fail, and test for resilience rather than robustness. Tolerance for failure becomes more important than a vain attempt to prevent failure. This tolerance often requires greater redundancy. Stripping out redundancy and maximizing the efficiency of systems has a downside, as I’ve discovered in my career. Greater efficiency can make applications brittle and inflexible. When problems hit they hit hard and recovery can be difficult.
The six years I spent working as an IT auditor had a huge impact on my thinking. I learned that things would go wrong, that systems would fail, and that they’d do so in ways I couldn’t have envisaged. There is nothing like a spell working as an auditor to imbue one with a gloomy sense of realism about the possibility of perfection, or even adequacy. I ended up like the gloomy old pessimist Eeyore in Winnie the Pooh. When I returned to development work a friend once commented that she could always spot one of my designs. Like Eeyore I couldn’t be certain exactly how things would go wrong, I just knew they would and my experience had taught me where to be wary. I was destined to end up as a tester.
Liz Keogh, in this talk on Safe-to-Fail makes a similar point.
“Testers are really, really good at spotting failure scenarios… they are awesomely imaginative at calamity… Devs are problem solvers. They spot patterns. Testers spot holes in patterns… I have a theory that other people who are in critical positions, like compliance and governance people are also really good at this”.
Testers should have the creativity to imagine how things might go wrong. In a Complex domain, working with applications that have been developed working with Cynefin, this insight and imagination, the ability to spot potential holes, will be extremely valuable. Testers have to seize that opportunity to remain relevant.
There is an upside to redundancy. If there are different ways of achieving the same ends then that diversity will offer more scope for innovation, for users to learn about the application and how it could be adapted and exploited to do more than the developers had imagined. Again, this is an opportunity for testers. Stakeholders need to know about the application and what it can do. Telling them that the application complied with a set of requirements that might have been of dubious relevance and accuracy just doesn’t cut it.
Conclusion is probably the wrong word. Dave Snowden’s class opened my mind to a wide range of new ideas and avenues to explore. This was just the starting point. These two essays can’t go very far in telling you about Cynefin and how it might apply to software testing. All I can realistically do is make people curious to go and learn more for themselves, to explore in more depth. That is what I will be doing, and as a starter I will be in London at the end of June for the London Tester Gathering. I will be at the workshop “An Introduction to Complexity and Cynefin for Software Testers” being run by Martin Hynie and Ben Kelly where I hope to discuss Cynefin with fellow testers and explorers.
If you are going to the CAST conference in Nashville in August you will have the chance to hear Dave Snowden giving a keynote speech. He really is worth hearing.