Andrew on Agile

During my ScrumMaster certification, Mike Cohn recommended that the size of a cross functional scrum team should be between 5 and 9.  In a recent exercise, we tried splitting a scrum team three ways – 5-6 people in each team - for the next release.

At the end of last year I counted 16 people in a scrum.  For most of the release the team size averaged 12, consisting of a mix of product managers, QA, front-end engineers, software engineers and the ScrumMaster.  16?  It never meant to get that big, but with an addition here and an addition there, it did.  It was too big.

The scrums were starting to take too long, there were too many stories in the sprint for one team to truly keep track and impediments were flying around everywhere.  Risk was starting to creep into each sprint and eventually the release.

I put it to the team that they should split in half for the next release and while some members were ok, others really objected to the idea.  They felt it would generate unnecessary tension between a team that had grown strong together.

Due to the type of work within the first sprint in that next release the team worked as one again, but agreed tentatively to an unequal split (1/3 to 2/3) for the following two (and final) sprints.  It was ok, both teams had a couple of issues mainly down to the fact that they were building three applications which all needed to communicate with one another, but the smaller team certainly preferred being small.

So now they’ve split into 3.

It’s brilliant.  The teams are made up of 1-2 QA, 2-3 Engineers and a Product Manager.  The teams were encouraged to go through the agile scrum motions how they saw fit.  The smaller size enabled intense collaboration during a short planning phase, resulting comprehensive stories.  Poker and sprint planning was a breeze, less big personalities to get in the way of proceedings, and it was so much easier to keep on top of the progress during the sprint.  The software’s of high quality.  It’s done.  It contains a good level of tests and the quality of the code is great.

Now that risk is greatly reduced.  In the sprints before if something started to go wrong it could have affected the whole team.  All members would get distracted by the issue and if it didn’t get resolved and the goal compromised, the team would be left demoralised by all the drama such failed sprints bring.  Oh yeah, and the team members love it.

So if it was going good, then the team gets bigger and you start to have problems, or if you’re trying things for the first time and some members aren’t engaging, break it down.

I’ve been having a series of conversations lately with one of my engineers about learning on the job.   By this I mean that technological problems get figured out during a sprint.  These problems usually arise from implementing technologies that aren’t so well known by the engineering team.  A recent example for us would be finding solutions for complex queries with Fluent NHibernate.  The conversations were sparked by recent events during our last release where a sprint goal was compromised by the introduction of a new Domain Driven Design (DDD) architecture, and the previously mentioned object relational mapper (ORM), Fluent NHibernate.

The engineer’s argument for learning on the job centres on the satisfaction gained by solving technical challenges presented by new technological ideas.  As a manager, engineer’s job satisfaction is very important to me.  In an unsettled economic climate such as the one we’ve just been (and perhaps still are going) through, it becomes even more important to use everything available to keep morale high.  Our favourable stance to adopting new technologies over the past couple of years – we built a production app in .Net MVC right through its preview stages, releasing the app as MVC was released – has kept the engineers interested in their jobs.   That being said, introducing a new technology is accompanied by risk, risk that can turn into a high cost.  Learning on the job during the sprint is risky because it can create impediments that are very difficult to remove.

In order to find the right balance it is important that all the engineers know what they’re doing throughout the sprint.  If a sticking point arises, e.g. a situation where you notice an engineer stuck with their head down for hours on end, a couple of scrums pass where they say they’ve got 1 or 2 hours left and they’ll be finished that day but they never do, then you know you have a problem.  Sometimes this can be solved by pairing, but probably it’s a sign the team hasn’t done enough homework.

We just halted the introduction of a messaging technology into the next release and are mid way through a two week planning phase.  The aim is to take a small sideways step in order to make sure the engineers are technically up to speed with what’s going into it. 

The focus of the first planning week has been on story generation, next week we’ll be exploring those stories further to find potential engineering sticking points. When they are found the whole engineering team will come together to investigate the solution.  This object of this exercise is not to create solutions for all our potential problems up front, as any changes to the backlog during the release could render this a waste of time, but to use the potential problems as case studies to ensure the engineers have enough knowledge about the technologies we have introduced before embarking on the sprints.

This is not something we will do between every release.  A series of weekly, hour long sessions to contextually spike the implementation of the messaging technology using a current production app will get underway as soon as the release starts.  We will only introduce it across our range of production apps when the team feels ready.

This is what I consider learning on the job.  There will inevitably be some learning during the sprint, but the aim should be to control that as much as possible.

Previously in our sprints we included QA time by adding a buffer to the engineer’s task estimates.   From now on, to provide greater visibility of the challenges QA might face whilst testing each story, we have started to include them in both the story point estimating and tasking sessions.

We started out by suggesting that the team (QA and Engineering) collectively estimate a story point value not only for how long it takes to engineer, but also how long it may take to test (relative to another of course). 

This didn’t go as expected. 

Some members got it and were able to take into account the advice or suggestions from their peers and re-evaluate accordingly.  Others found it very difficult to accept how QA and engineering could effectively estimate each other’s time.  

Such reservations were confirmed during the estimation of the second story.  The engineers agreed a 5, QA initially put down a 1.  The engineers then suggested to QA what might be involved in the story, but QA re-evaluated only a 2.  This resulted in a lively debate about what we were doing, how “pointless” this all was, etc, etc and the session pretty much broke down.

The strongest opinion of what to do next was for the engineers to story point their value, QA to story point their value and then add the two together.  For the sake of moving on we tried it.  We went back to the first story, engineers a 2, QA a 1 so we got 3.  Fine.  Next, engineers 5, QA 3 and we have 8.  This is easy!  Story 3: engineers re-evaluate a bit between 13 and 20 but settled on 20, QA 5.  Ok, so we suggest either re-evaluating again collectively to see if we accept 20, or we round up to 40.  Nope.  Some of the engineers wanted to stick with the story point value of 25 and the previous debate flared up again.

Both engineers and QA understand why we story point; that we use the Fibonacci sequence to balance out the estimations.   They’d heard the bucket of water metaphor Mike Cohn used during my ScrumMaster certification ”… you have 25 litres of water, that’s not going to fit in a 20 litre bucket, you should use a 40″.  They want (and agree with the need) to be more integrated, but just struggled with the concept of collectively settling on a figure.  There was alot of confusion over the level of accuracy required.

After a lo————ng debate, to get through the session a consensus was reached.  In order to obtain our team velocity we just needed to be consistent over time.  The team settled on this: QA story point their value, engineer’s their value and to accept the highest.  From that point the session finished quite quickly; everyone was a bit frazzled but content with the result.

What we have done is wrong.  Settling on the highest value is fine if QA vote 1 and engineers vote 13.  But what if engineers vote 13 and QA vote 8?  Or QA vote 20 and engineers vote 13?  The real estimates for those last two examples would surely be nearer 20 and 40?  We are not providing an accurate estimate to the best of our knowledge at the time.  This is important for management to plan a release.  It’s difficult to accurately estimate for a long release period, but we should not knowingly underestimate.  This will need to be addressed again during the next session.

The goal is that the team will naturally start considering each other during estimating sessions.  They will get there.  Even the senior engineer who raised the most questions about the process at one point put down a 40 and justified it by saying “well there’s a lot to test”. 

We have just started with test automation which will help to bring QA and engineering together further.  We might also get there more quickly with smaller teams; yesterday’s was 3 QA and 7 engineers.

The positives from the story pointing session?  We can now clearly see the different challenges that QA face from story to story, which sometimes do not have a direct correlation with engineering time for the story.  This is definitely going to help us with both planning and execution.