alikhalid

Autonomy is a cornerstone of agile transformation. However I’ve seen it taken the wrong way too.

IMHO, autonomy means:

– Self organizing teams who don’t have to wait for someone to take decisions on their behalf

– Who have the autonomy to estimate work (within reasonable guidelines) and drive decisions related to completing the work

– As they can take technical decisions, so are they responsible for those solutions – you code it, you own it

What it does NOT mean:

– Create a bubble and make sure no one from the outside has any visibility into the working of the team (no one can come to our stand ups / retros or other ceremonies). Agile is about transparency remember!

– Take autonomy as a ‘do whatever and get away with badge’ – for example, give unrealistic estimations and having no technical reasoning for why it’s going to take that long.

A big factor in all this I guess is having a purpose and being driven also.

And make no mistake – this will have a direct impact on quality of your product – and hence should be factored into the KPIs to measure.

What is your experience on autonomy in teams?

Introduction to data pipelines in 2 minutes:

For analytics, AI products lots of data is needed – commonly dubbed as Big data

To get the data needed, and in a usable format – we need to pass it through a lot of different data processing stages, a collection of which can be called data pipeline.

Data pipelines have 3 main stages:
– Ingestion: Gather data from various sources in different formats
– Data hub & warehouse – Cleanse, model data at different stages
– Analytics – Run analytics or use specific data sets for machine learning algorithms

Join me at TestBash New Zealand Online 2020 where I’ll talk about a lot more around testing in big data projects!

How would you go about testing an AI product? Join me at QA&TEST Conferences where we will debate the topic on Oct 30.

I’ve been fortunate to work on that problem a few times in past years, in my experience this is not a straight forward answer..

Biggest reason for me – it’s not easy to establish clear oracles (I didn’t use the word requirements..) which creates a complete new dynamics to test these products.

The test objectives of these systems are therefore going to be a bit different, and wouldn’t be just about testing the actual model,

I’d stretch it from the beginning – ‘capturing of data’ and then take to the other extreme – ‘is the model predictions good enough’ – not an easy answer to give, but worth investigating.

Anti-agile test case writing:
– Extremely lengthy test cases for every scenario anyone has ever thought of, and make sure not to rank them
– Then do a ‘regression testing’ cycle and try execute all of them
– Then try to code ALL the written tests as automated scripts

Test case writing the Agile way:
– Collaborate to identify acceptance tests in Three amigo sessions – these are highest priority scenarios
– Automate tests across the tech stack – and DON’T document them – they exist in your code already
– Write checklists to serve as heuristics / references to do exploratory testing

Some reminders for me after being part of Program Increments (PI) since past week..

1. Have a rough estimate of your capacity plans prepared before hand. That includes public holidays, annual leaves

2. Have your features and related stories defined and get the roughly prioritized before planning meetings

3. PI plans (typically across 6 sprints / 3 months) are ‘plans’ – not to be set in stone and can fluctuate over course of the PI

4. Mark your dependencies and get them prioritized – especially critical dependencies

5. Above all – PI planning is about collaboration and figuring out where you time is best spent across next 3 months – to deliver value to end customers

About:

PI planning is a ceremony in scaled agile framework for release trains (group of scrum teams) to plan for the next program increment

While defining KPIs, have both – Leading and lagging indicators.

Often I’ve seen people not making that distinction which creates misconceptions about what the KPIs are saying

Leading indicators:

Predictive measurements used to influence change. For example – how good are we at creating & working with user stories.

These are not the actual change we want to see, rather the change which will ‘lead’ to the outcomes we are hoping for

The problem I see – folks take these as the ‘ultimate’ outcome to measure, which is the problem

Same goes for automation – an common one is ‘% tests automated’. This ‘might’ be a leading indicator in some situations – but is definitely not the ultimate goal.

Lagging indicators:

Measuring the outcome we are actually looking for. For transformation projects could be e.g. 50% reduction in bugs from the field, 40% reduction in lead time

So next time defining KPIs – do classify them as leading and lagging indicators.

Both are helpful – but be careful not to measure a leading indicator assuming these are the ultimate objective.

Algorithm design aptitude – IMHO second most important ingredient for an SDET / Engineer

My definition algorithm design aptitude: Ability to design a complex solution using small building blocks.

And to do this, you don’t have to start with Java. The first language I learned in high school was HTML & CSS.

I learned how to find small building blocks, connect them and create a solution.

Those fundamental lessons I used everywhere – at Uni in my engineering, on job in test automation, testing, web development, big data, you name it.

And this is what I teach and stress upcoming engineers / SDETs to learn – languages will come and go, tools pop like mushrooms all over

What will stay with you, and help you through all of that – ability to visualize & design solutions using existing small building blocks..

Oh, and by the way – We all do have an aptitude to design things – we just need to learn to harness it.

As part of the automation training program I designed for Emirates IT, conducted another online session of the BDD workshop today..

While designing the course, I deliberately kept a very small portion on Gherkin and using cucumber,

and more focus on why and how to collaborate as part of BDD,

Unfortunately most people as soon as they talk about BDD – the first thing they mention is cucumber – and forget the whole conversation that’s supposed to happen before that.

If your team is just starting your DevOps journey, where to start improving testing from?

Begin with investing in automating your services layer first.

Folks assume that’s just about writing API tests, to me this is more of a mind set change as well

The rule of thumb is, test nearest to where the production code is written,

For instance, any functionality written within a micro-service, should be tests at the micoservice level, functionality on the mesh gateway should be tested there and so on.

DevOps tools are not magically going to test and debug problems for you, unless the tests are planned at the right place, they will be flaky and have overhead while debugging.

#RedefiningSoftwareQuality #DevOps #QualityTransformation #TestAutomation