We make a lot of decisions in software development. Compared to other fields of engineering, the process of building software relative to designing it is very rapid, shrinking the time between decisions and also typically shrinking the time spent on decisions. Modern agile software development methodology only exacerbates this problem. “Move fast and break things”.
On top of this, software systems will often stay under active development for decades, growing to be unfathomably large and complex. In complex systems decisions in one area often have implications in other seemingly unrelated areas, making us hesitant to make quick decisions.
As a consultant I bounce in and out of different organisations every few months, meaning I’m constantly onboarding and offboarding. In between onboarding and offboarding, my job is often to help make a bunch of decisions. Each time I onboard I have to quickly understand existing decisions so that my work aligns to them, and as I offboard I have to make sure any decisions I made are understood by the rest of the team. Having clearly defined intentions behind decisions is critical for this.
Good intentions not only help understand why decisions were made, they help you remember and work with the decision itself. As an example, I fairly recently moved to Melbourne—a city notorious for its confusing hook turns, where you have to make right turns from the left lane when the traffic lights change. But not at every intersection, which is what makes them so confusing. In the rush of inner city traffic it’s easy to miss the signage and end up in the wrong lane for your turn. So how can you remember which intersections use hook turns?
To answer this, we have to understand why hook turns exist at all. About half of the main streets in the CBD have tram tracks. Turning right from the right lane would require either sitting on the tram tracks blocking trams waiting for traffic to clear, or turning right with tram tracks on your inside lane and running the risk of getting t-boned by a tram. The purpose of the hook turn is to keep cars off the tram tracks. With this in mind, it’s easy to remember as you’re driving down a street with tram tracks that any right turn will be a hook turn, and the signs become merely confirmation rather than direction.
Good intentions don’t just help us understand the decisions of others. Have you ever looked at some code that looked wrong, wondered who could possibly have written it and why, run git blame and realised it was you. Two weeks ago. With the amount of context required to deeply understand parts of a system and the speed that we move from one thing to the next, the importance of being clear about intentions is critically important even when working alone. I find it safest to pretend that the me of two weeks into the past or future is for all intents and purposes a different person, and make sure to document my intentions appropriately.
Decisions fall into one of these three types: Start doing A, stop doing A, or A vs B (vs C vs …). You could argue that these are actually all just the same type, a choice between two (or more) things, but I like to distinguish between them because the frame of reference is important in the way choices are discussed.
Imagine that you have an API that, among other tasks, needs to perform some operation which over time is starting to take longer and longer. Somebody on the team proposes putting a message onto Queue Service A so that this operation can be performed asynchronously. Somebody else on the team says this is a bad idea because Queue Service A doesn’t guarantee delivery, or because it might deliver twice, or some other attribute of Queue Service A specifically.
This discussion can quickly devolve into an unproductive argument, when in reality they mostly agree with each other. The problem is that the first person is really arguing in favour of using any queuing service (start doing A). The second person is implicitly comparing Queue Service A to Queue Service B (A vs B), but their arguments against Queue Service A are perceived to be arguments against using a queuing service at all.
Good intentions also help us avoid focusing on non-goals. When discussing a solution to a problem, it’s easy to conflate the positive attributes of the solution with our goals. This might not be a problem in the start doing A frame of reference, but can be extremely problematic with A vs B. It can mean that we start evaluating options not by how they compare to our requirements but by how they compare to each other, placing importance on attributes which aren’t relevant to our problem.
This is especially bad if this misplaced importance on irrelevant attributes is perceived to outweigh other more important attributes and we end up selecting the worse option, possibly even worse than the alternative of not selecting either. This flaw in our decision making abilities, called anchoring, happens because of our frame of reference.
Let’s look at some more concrete examples.
Continuing with the theme of queuing services, here are some bullet points used to describe three different Azure services. “Build in resilience”, “Build reliable cloud applications”, “Build scalable cloud solutions”, “Scale for bursts”, “Simplify enterprise cloud messaging”, “Simplify event delivery”, “Implement complex messaging workflows”, “Decouple components”, “Focus on product innovation”. All of them sell me on the idea of using a queuing service to improve my application, against the alternative of not using any service at all—they sell me on start doing A. But once I’ve decided I need such a service, none of these helps me understand which one is right for my needs—A vs B.
Fortunately, there’s a page that directly compares two of these services—Storage queues and Service Bus queues. Unfortunately, it starts like this.
Azure supports two types of queue mechanisms: Storage queues and Service Bus queues.
Storage queues, which are part of the Azure storage infrastructure, feature a simple REST-based GET/PUT/PEEK interface, providing reliable, persistent messaging within and between services.
Service Bus queues are part of a broader Azure messaging infrastructure that supports queuing as well as publish/subscribe, and more advanced integration patterns. For more information about Service Bus queues/topics/subscriptions, see the overview of Service Bus.
While both queuing technologies exist concurrently, Storage queues were introduced first, as a dedicated queue storage mechanism built on top of Azure Storage services.
Emphasis mine. That’s right. The main point of contrast apparently is that Storage queues called dibs first. To its credit, the article improves from here, and goes on to show an 11-point comparison table and says the following.
As a solution architect/
developer, you should consider using Storage queues when:
- [3 bullet points omitted.]
As a solution architect/
developer, you should consider using Service Bus queues when:
- [12 bullet points omitted.]
This is a discussion of queuing services in the A vs B frame of reference rather than start doing A.
Another example I like to give is the various Scrum ceremonies and
techniques, like daily scrums/
This frequently leads to the people feeling that meetings are a mandatory yet valueless waste of time. They ask questions like “Should people from the business attend stand-up and retros?” “Should we skip stand-up if the Scrum master is on leave?” “Should stand-up go person-by-person or story-by-story?” They do story point estimation but aren’t sure exactly why. They ask questions like “Should we be estimating tasks, stories, features or epics?” “Should the value of an estimate depend on who is doing the work?” “Should estimates be adjusted after starting and realising there’s more involved than we thought?” They break down large stories, but ask questions like “Should we split stories into layers or vertical slices?” “Should the smaller chunks be stories or just tasks?”
There are no right or wrong answers to these questions. Sure, some answers might work better for a majority of teams, but the point is that the right answer is the one that works best for your team. All of these techniques are supposed to work for you, to make you more productive. There’s little intrinsic value in going through the motions with these techniques. Unless you know what your intentions are, you can’t hope to realise much value from them. Take some time (in your retrospectives) to discuss as a team what you hope to achieve with each technique, and how you can get the most out of them. Don’t be afraid to try different approaches and see what works best for you.
At the risk of getting too political, the final example I want to consider is government like the Australian parliament. The role of government is essentially to create legislation by prioritising the allocation of tax money and making trade-offs between liberty and regulation. Members need to state the intentions of each piece of legislation in order to get the necessary votes for it to pass, which usually involves some amount of debate and negotiation on various amendments. During an election campaign, the major parties state their intentions for government to the electorate in order to win support.
Despite this, the system as a whole is still prone to making the same kinds of mistakes about intentions as we are. Although there is often discussion about intentions at the micro-scale, at the level of individual pieces of legislation, broader intentions are less-well discussed. Elections are often said to be broadly about certain policy areas, but the discussion at this level is less robust, full of hand-waving, question-dodging and point-talking, and subject to media suppression and the Overton window. There is virtually no discussion about intentions above this level, outside of academic debate. People often refer to “traditional Australian values” but they aren’t well defined, policy is not assessed on how it furthers these values, and there are countless examples of policies that contradict the few values which are commonly agreed upon.
One example of a breakdown of intentions in Australian politics is the 1999 Australian republic referendum. There is a lot of analysis about how the framing of questions affects the outcome of referendums. Although it might seem like a start doing A question, where A is become a republic, it was worded in such a way that it opened up A vs B discussion.
To alter the Constitution to establish the Commonwealth of Australia as a republic with the Queen and Governor-General being replaced by a President appointed by a two-thirds majority of the members of the Commonwealth Parliament.
This meant that many people in favour of becoming a republic voted no because they preferred a different method for appointing the president and were hopeful of another referendum in the near-future, and the referendum was ultimately voted down. Twenty-one years later and it’s not clear whether that second referendum is coming.
A more controversial example is the Second Amendment. I can’t even quote the text here, as there is debate about the correct punctuation and capitalisation which subtly affects the meaning (and assumed intention) of the amendment. Would the authors have intended for citizens to keep modern-day assault rifles? Would they still consider a well regulated militia necessary to the security of a free State, considering the vastly more powerful militaries? We’ll never know. People will continue to debate this for decades or centuries to come.
So how can we be clearer about our intentions? Here are some techniques that might work for you.
The first obvious step is to write comments in your code that explain not what it does but why it exists. What were your intentions when writing the code? What does it aim to achieve? Which bug does it solve? Which story does it implement? Links to external sources of context like this are a huge help to understanding and working with other people’s code—which remember means your own code two weeks after you write it. If you don’t want to leave these comments inline, consider making detailed commit messages instead.
There’s another approach you can take to code comments. When somebody is trying to diagnose a bug and they see a suspicious piece of code, their instinct will be to delete it and their concern for making things worse will be the only thing stopping them. When you write tricky code, as well as explaining why it exists explain why it shouldn’t be deleted in the future. Or under what circumstances can it be deleted in the future. If you put in a fix to work around a bug in another part of the codebase, the fix can be removed when the other bug is fixed. If you don’t make this clear, people will be afraid to delete the code.
There’s actually a great story that demonstrates this aversion to removing things—it’s called Chesterton’s fence.
In the matter of reforming things, as distinct from deforming them, there is one plain and simple principle; a principle which will probably be called a paradox. There exists in such a case a certain institution or law; let us say, for the sake of simplicity, a fence or gate erected across a road. The more modern type of reformer goes gaily up to it and says, “I don’t see the use of this; let us clear it away.” To which the more intelligent type of reformer will do well to answer: “If you don’t see the use of it, I certainly won’t let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it.”
The moral of the story is, always document the original use of something you add. When that use is no longer necessary, reformers will be more confident in clearing it away.
There’s also a flipside to this, which is that sometimes people get hung up on what were originally arbitrary decisions. If you happen to make any arbitrary decisions in code that you write, like the value of a magic constant or the order of two operations, be clear about this and leave a comment saying it’s completely arbitrary.
When you write a tool or a library that other people will use, it’s natural that you’ll write down all the strengths and advantages. But everything is a trade-off, and it’s far more likely that the person using your tool is thinking with an A vs B frame of reference than a start doing A. It’s important to be honest about these trade-offs and to also mention the weaknesses and disadvantages, so that your users can make informed decisions about what will best meet their requirements. If possible, provide direct comparisons with alternatives to make this even easier.
Speaking of alternatives, when you are making decisions with several alternatives it’s good to document each one you considered and why you discounted them. If it was an A vs B decision but you only document the eventual selection (or nothing at all), later on you might look back and mistake it for a start doing A decision. If you later need to question or even change the decision, you’ll be better off for having access to the notes on what else was considered.
Try to distinguish between problems and solutions. When a problem comes up and you think of an obvious solution to it, it can be all too easy to create a work item describing the solution and then get straight into it. If later on you need to change the solution and you didn’t properly document the problem, all alternative solutions will need to be compared against the existing solution. That is, you’ll be forced into an A vs B frame of reference rather than start doing A. This means that you might accidentally place importance on qualities of the existing solution which aren’t relevant to solving the original problem.
Actually, go one step further. When you’re documenting the problem be as clear as possible about the requirements that a solution needs to satisfy. Rather than saying “the solution needs to be fast”, say how fast it needs to be. Again, if later on you need to change the solution, you don’t want to discount alternatives just because they aren’t as fast as the current solution, if they still meet the original speed requirements. If it wasn’t your intention to pick the fastest possible solution, don’t accidentally fool your future self into thinking that it was.
And finally, sometimes it helps to flip a start doing A into a stop doing A by pretending that you have already implemented the solution. I’ve often seen teams hold off on deploying a fix to a bug because it doesn’t solve the root problem, or it doesn’t fully mitigate the problem. This is the classic “perfect is the enemy of good” ideology at work, and it only results in unnecessary continued business impact until a fix is finally deployed. If you flipped the frame of reference and pretended that the fix was already deployed, you wouldn’t dream of taking it out and making the problem worse. Your intention when there is a live bug should be to mitigate the impact as quickly as possible, not necessarily to solve the root problem.
My intention with this post has been to help you think more clearly about your intentions. To provide some examples of how clear intentions improve understanding and decision making, and unclear intentions cause confusion and bad decision making. And finally to provide some advice on how to be clearer in the way you think about and state your intentions. Actually that’s a lie. My real intention was to help me think more clearly about my intentions. If you’ve found any of this useful, that’s a bonus.