It's in people's nature to notice change and differences. It's also in people's nature to make assumptions (or stop and ask questions) when they expect something and, to them, it is missing.
So, if when trying to optimise a number of teams working together for the benefit of customers, something people are familiar with is not obviously there, then people can get tripped up.
Due to this, it can become difficult to discuss and explore new models or approaches due to questions about what is missing - or, really, not visible, not obvious, not understood.
TL;DR -> Aim (Why): Delight Customers; Plan (How): Where to invest effort, observe and act; Execute (What): Experiments to perform and data to gather - to match the "How". These are universal "test skill" attributes - i.e. "testing" is everywhere in well-functioning product development, delivery and operation.
Example:
Consider a model for product development, ideally optimised to get feedback from users of the product and work on customer needs. This can be many software components, many software systems and many configurations. Assume the aim for the company producing a product is: to delight the customers & users of the product.
 |
| Product Inception, Development, Delivery and Monitoring |
Note, that this model can be applied on a team or company level.
Potential problem: Interpretation
If 10 people look and study this model there will be more than 10 interpretations. Very often this is a result of how someone frames the object to study (or problem to solve). This can be a factor of where they "sit" in the model/company, what influence they have and what they "want" to do.
Potential question: Where's testing?
Some people might look at the model and wonder where is "testing" done? This can be a leading question - sometimes as a function of thinking of "testing" as separated from other activities, sometimes as a function of what someone is used to anchoring to. Sometimes it might be a worry - how do we understand the customer is getting what they asked for.
I don't see this model reflects a particular development model or even a type of "testing school". Conversely, I'm not sure any testing school has put the work into supporting such a model (for optimising feedback from customers and working on customer needs).
Potential Approach: Find a place for testing.
One way is to find how testing contributes to each box of the model. There is a trap with this - if the whole is not also considered (or at least adjacent boxes) then this approach will tend to a local-optimisation in each box and not necessarily between boxes. It's an approach that tends to place testing in boxes - in the extreme it creates separated testing boxes. In the ultra extreme it creates a standard for SW testing detached from SW development.
Potential assumption: Specialised testing is not needed.
If you can't see it in the model then it's not needed, right? But, note - I haven't spelled out product architectural and system design. It doesn't mean they are not needed. So, that leads to the question - what is the model conveying. This model is not a WYSIATI (what you see is all there is) model - or rather it is above a level of practices.
Ok, so what use is such a model????
My take?
Yes, the model is on a very high level, but that's the point - use an example where it appears as though the thing you want to talk about is absent...
When I discuss such a picture above and a discussion about testing comes up, my approach is, "think how testing helps each of the above boxes", or really think of the boxes containing:
 | |||
|
To give a number of questions, for example:
Product in Use & User Feedback
- How to observe or get data about the product in use? Getting data about customer opinions, complaints or new wishes and needs?
- How to make judgements and derive opinions about the data (form hypotheses)?
- How to create experiments to gauge and observe the product performance in use or product usage?
- How to evaluate the results of those experiments, data and results?
Product Backlog & Development
- How will observability of the product and product usage be prioritised, developed and in what circumstances?
- How should the product architecture and supporting environment look to be observable?
- How should the product architecture support (fast) feedback on product changes? (hypothesis)
- How should the supporting environment support product changes? (hypothesis)
- How should experiments be created to observe and gather data on the product, its usage and performance?
- How to understand the results and data and whether the experiments are giving data on the hypotheses?
Product Delivery
- How to observe and understand product delivery and deployment?
- How to understand if a product delivery will delight or disappoint a customer (new or old)?
- How to create experiments to gather data on product delivery and potential response from customers?
- How to evaluate the data from the experiments of product delivery? What does the experimental data indicate about product delivery and potential (or actual) customer reaction?
And finally.... the whole:
Product Inception, Development, Delivery and Operation
- How do we observe product usage and customer satisfaction?
- How do we create an understanding of what the customer wants and is happy with?
- How do we create experiments to understand our understanding during development, delivery and operation? Do we have consistency of hypothesis through development & delivery?
- How do we evaluate the data from development, delivery and operation to a consistent picture? Do we have data to help understand delivery to customers, customer perception, feedback to the product development teams? Do we have data to understand what can be improved?
Why->How->What
Most of these questions are "how" questions. They are predicated on supporting a model that optimises feedback from a customer and providing a product that a customer wants - the why. The "what", the implementation, is the least important - although it is important.
Sometimes "where's testing?" questions are really about "what" rather than the purpose and meaning. This is a check observation to keep in mind.
And So....
- Notice, all of the above might be more recognisable as test and fact-based advocacy (observations), test and fact-finding analysis and design (hypothesis & experiments), test and fact-finding execution (experiments and iterating on experiments) and test and (qualitative) data advocacy and reporting (sense making).
- Notice, it is everywhere in the SW development, delivery and operations loop. You might want to be ultra-specialised and constrain your "test" advocacy-design-execution-reporting skills to a small subset of the whole. Or, you might realise that those same observation-hypothesis-experimentation-sensemaking skills are needed (and can be used) everywhere. The trick is to realise that, then to balance the amount of time you want to spend in a small subset of a product development activity - whether as a team, separate team or individual and balance those skills elsewhere.
So - the testing skill set tied to observations, hypothesis forming, experimentation and evaluation and sense making are vitally important all through the product inception, development, delivery and operations flow!! In my experience successful teams and organisations have these skill sets in multiple places, not isolated.
Of course, if your practical skill set (or comfort zone) limits you to a small subset, you might want to work on expanding those boundaries - at least for the good of the people and teams around you.
Potentially Related Post
