Grooming the Backfog

This is “Pub Walks in Warwickshire”. NEW EDITION, it tells me! This particular EDITION was actually NEW back in 2008. It’s no longer in print.

Pub Walks in Warwickshire

Each chapter is a separate short walk, starting and finishing at a pub with a map and instructions to find your way around the walk. Some of the instructions are broken: a farmer has put a barbed wire fence across a field, or a gate has been replaced or removed. You find when you get there that it’s impossible to follow the instructions, and you have to invent a new route to get back on track. You did bring a different map, didn’t you? If not, you’ll be relying on good old-fashioned trial and error.

Other problems are more catastrophic. The Crown at Napton-on-the-hill seems to have closed in about 2013, so an attempt to do a circular walk ending with a pint there is going to run into significant difficulties, and come to an unsatisfactory conclusion. The world has moved on, and those directions are no longer relevant. You might want to start/end at the Folly, but you’ll have to make up a route that joins to the bits described here.

This morning, a friend told me of a team that he’d heard of who were pulling 25 people in to a three-hour backlog grooming session. That sounds like they’re going to write the NEW EDITION of “Pub Walks in Warwickshire” for their software, and that by the time they come around to walking the route they’ll find some of the paths are fenced over and the pubs closed.

Decomposing the Analogy

A lengthy, detailed backlog is not any different from having a complete project plan in advance of starting work, and comes with the same problems. Just like the pub walks book, you may find that some details need to change when you get to tackling them, therefore there was no value in spending the time constructing all of those details in the first place. These sorts of changes happen when assumptions about the organisation or architecture of the system are invalidated. Yes, you want this feature, but you can no longer put it in the Accounts module because you found that customers think about that when they’re sorting their bills, not their accounts. Or you need to put more effort into handling input from an external data source, because the way it really works isn’t quite the same as the documentation.

Or you find that a part of the landscape is no longer present and there’s no value in being over there. This happens when the introduction of your system, or a competitors’, means that people no longer worry about the problem they had back at the start. Or when changes in what people are trying to do mean they no longer want or need to solve that problem at all.

A book of maps and directions is a snapshot in time of ways to navigate the landscape. If it takes long enough to follow all of the directions, you will find that the details on the ground no longer match the approximation provided by the book.

A backlog of product features and stories is a snapshot in time of ways to develop the product. If it takes long enough to implement all of the features, you will find that the details in the environment no longer match the approximation provided by the backlog.

A Feeling of Confidence

We need to accept that people are probably producing this hefty backlog because they feel good about doing it, and replace it with something else to feel good about. Otherwise, we’re just making people feel bad about what they’re doing, or making them feel bad by no longer doing it.

What people seem to get from detailed plans is confidence. If what they’re confident in is “the process as documented says I need a backlog, and I feel confident that I have done that” then there’s not much we can do other than try to change the process documentation. But reality probably isn’t that facile. The confidence comes from knowing where they’re trying to go, and having a plan to get there.

We can substitute that confidence with frequent feedback: confidence that the direction they’re going in now is the best one given current knowledge, and that it’s really easy to get updates and course corrections. Replace the confidence of a detailed map with the confidence of live navigation.

On the Backfog

A software team should still have an idea of where it’s going. It helps to situate today’s development in the context of where we think (but do not know) we will be soon, to organise the system into a logical architecture, to see which bits of flexibility Ya [Probably] Ain’t Gonna Need and which bits Ya [Probably] Are. It also helps to have the discussion with people who might buy our stuff, because we can say “we think we’re going to do these things in the coming months” and they can say “I will give you a wheelbarrow full of money if you do this one first” or “actually I don’t need that thing so I hope it doesn’t get in my way”.

But we don’t need to know the detailed steps and directions to get there, because building those details now will be wasted effort if things change by the time we are ready to tackle all of the pieces. Those discussions we’re having with the people who might buy our stuff? They might, and indeed probably should, change that high-level direction.

Think of it like trying to navigate an unknown landscape in fog. You know that where you’re trying to get to is over there somewhere, but you can’t clearly see the whole path from here. You probably wouldn’t just take a compass bearing and head toward the destination. You’d look at what you can see around, and what paths there are. You’d check a map, sure, but you’d probably compare it with what you can see. You’d phone ahead to the destination, and check that they expect to be open when you expect to get there. You’d find out if there are any fruitful places to stop along the way.

So yes, share the high-level direction, it’s helpful. But share the uncertainty too. The thing we’re doing next should definitely be known, the thing we’re doing later should definitely be guesswork. Get confidence not from colouring in the plan all the way up to the edges, but by knowing how ready and able you are to update the plan.

On the continuous history of approximation

The Difference Engine – the Charles Babbage machine, not the steampunk novel – is a device for finding successive solutions to polynomial equations by adding up the differences introduced by each term between the successive input values.

This sounds like a fairly niche market, but in fact it’s quite useful because there are a whole lot of other functions that can be approximated by polynomial equations. The approach, which is based in calculus, generates a Taylor series (or a MacLaurin series, if the approximation is for input values near zero).

Now, it happens that this collection of other functions includes logarithms:

\(ln(1+x) \approx x – x^2/2 + x^3/3 – x^4/4 + \ldots\)

and exponents:

\(e^x \approx 1 + x + x^2/2! + x^3/3! + x^4/4! + \ldots\)

and so, given a difference engine, you can make tables of logarithms and exponents.

In fact, your computer is probably using exactly this approach to calculate those functions. Here’s how glibc calculates ln(x) for x roughly equal to 1:

  r = x - 1.0;
  r2 = r * r;
  r3 = r * r2;
  y = r3 * (B[1] + r * B[2] + r2 * B[3]
    + r3 * (B[4] + r * B[5] + r2 * B[6]
        + r3 * (B[7] + r * B[8] + r2 * B[9] + r3 * B[10])));
  // some more twiddling that add terms in r and r*r, then return y

In other words, it works out r so that it is calculating ln(1+r), instead of ln(x). Then it adds together r + a*r^2 + b*r^3 + c*r^4 + d*r^5 + ... + k*r^12…it does the Taylor series for ln(1+r)!

Now given these approximations, we can combine numbers into probabilities (using the sigmoid function, which is in terms of e^x) and find the errors on those probabilities (using the cross entropy, which is in terms of ln(x). We can build a learning neural network!

And, more than a century after it was designed, our technique could still do it using the Difference Engine.

Java By Contract: a Worked Example

Java by Contract is an implementation of Design by Contract, as promoted by Bertrand Meyer and the Eiffel Software company, for the Java programming language. The contract is specified using standard Java methods and annotations, making it a more reliable tool than earlier work which used javadoc comments and rewrote the Java source code to include the relevant tests.

Which is all well and good, but how do you use it? Here’s an example.

The problem

There is a whole class of algorithms to approximately find roots to a function, using an iterative technique. Given, in Java syntax, the abstract type MathFunction that implements a function over the double type:

interface MathFunction {
    double f(double x);
}

Define the abstract interface that exposes such an iterative solution, including the details of its contract.

The solution

The interface is designed using the Command-Query Separation Principle. Given access to the function f(), the interface has a command findRoot(seed1, seed2) which locates the root between those two values, and a query root() which returns that root. Additionally, a boolean query exhaustedIterations() reports whether the solution converged.

Both of the queries have the precondition that the command must previously have successfully run; i.e. you cannot ask what the answer was without requesting that the answer be discovered.

The contract on the command is more interesting. The precondition is that for the two seed values seed1 and seed2, one of them must correspond to a point f(x) > 0 and the other to a point f(x) < 0 (it does not matter which). This guarantees an odd, and therefore non-zero, number of roots[*] to f(x) between the two, and the method will iterate toward one of them. If the precondition does not hold, then an even number of roots (including possibly zero) lies between the seed values, so it cannot be guaranteed that a solution exists to find.

In return for satisfying the precondition, the command guarantees that it either finds a root or exhausts its iteration allowance looking. Another way of putting that: if the method exits early, it is because it has already found a convergent solution.

Many, but not all, of these contract details can be provided as default method implementations in the interface. The remainder must be supplied by the implementing class.

/**
 * Given a mathematical function f over the doubles, and two bounds for a root to that function,
 * find the root using an (unspecified) iterative approach.
 * A root is an input value x such that f(x)=0.
 */
public interface RootFinder {
    /**
     * @return The function that this object is finding a root for.
     */
    MathFunction f();
    /**
     * A root to the function f() is thought to lie between seed1 and seed2. Find it.
     * @param seed1 One boundary for the root to f().
     * @param seed2 Another boundary for the root to f().
     */
    @Precondition(name = "seedGuessesStraddleRoot")
    @Postcondition(name = "earlyExitImpliesConvergence")
    void findRoot(double seed1, double seed2);
    /**
     * @return The root to the function f() that was discovered.
     */
    @Precondition(name = "guessWasCalculated")
    Double root();
    /**
     * @return Whether the iterative solution used the maximum number of iterations.
     */
    @Precondition(name = "guessWasCalculated")
    boolean exhaustedIterations();

    default Boolean guessWasCalculated() {
        return this.root() != null;
    }
    default Boolean seedGuessesStraddleRoot(Double seed1, Double seed2) {
        double r1 = f().f(seed1);
        double r2 = f().f(seed2);
        return ((r1 > 0 && r2 < 0) || (r1 < 0 && r2 > 0));
    }
    Boolean earlyExitImpliesConvergence(Double seed1, Double seed2, Void result);
}

Example usage

There are swaths of algorithms to implement this interface. See, for example, the book Numerical Recipes. Given a particular implementation, we can look for roots of a simple function, for example f(x) = x^2 - 2:

    RootFinder squareRootOfTwo = SecantRootFinder.finderForFunction((double x) -> x*x - 2);
    squareRootOfTwo.findRoot(1.0, 2.0);
    System.out.println(String.format("Root: %f", squareRootOfTwo.root()));
    System.out.println(String.format("The solution did%s converge before hitting the iteration limit",
            squareRootOfTwo.exhaustedIterations()?"n't":""));

This suggests that a root exists at x~=1.414214, and that it converged on the solution before running out of goes. Let’s see if there’s another root between 2 and 3:

Exception in thread "main" online.labrary.javaByContract.ContractViolationException:
  online.labrary.javaByContract.Precondition seedGuessesStraddleRoot had unexpected value false on object
  online.labrary.jbcTests.TestGeneratorTests$SecantRootFinder@29774679
    at javaByContract/online.labrary.javaByContract.ContractEnforcer.invoke(ContractEnforcer.java:92)
    at jdk.proxy1/com.sun.proxy.jdk.proxy1.$Proxy4.findRoot(Unknown Source)
    at javaByContract/online.labrary.rootFinder.RootFinder.main(RootFinder.java:10)

Whoops! I’m holding it wrong: the function doesn’t change sign between x=2 and x=3. I shouldn’t expect the tool to work, and indeed it’s been designed to communicate that expectation by failing a precondition.

[*] Nitpick: roots _or singularities_.

Updates to JavaByContract

Some improvements to JavaByContract, the design-by-contract tool for Java:

  • Preconditions, Postconditions and Invariants now appear in the Javadoc for types that use JavaByContract. While this is only a small source change, it’s a huge usability improvement, as programmers using your types can now read the contracts for those types in their documentation.
  • There is Javadoc for the JavaByContract package.
  • The error message on contract violation distinguishes between precondition, postcondition and invariant violation.

I’m speaking generally about moving beyond TDD, using JavaByContract as a specific example, at Coventry Tech Meetup next week. See you there!

The App that Wasn’t (Yet)

One of the early goals written into the mission statement of the Labrary was an eponymous app for organising research notes. I’ve used Mekentosj Springer Readcube Papers for years, and encountered Mendeley and others, and found that they were all more focussed on the minutiae of reference management, rather than the activity of studying and learning from the material you’re collecting in your library. Clearly those are successful apps that have an audience, but is there space for something more lightweight?

I talked to a few people, and the answer was yes. There were people in software engineering, data science, and physics who identified as “light” consumers of academic literature, people who read the primary literature to learn from and find techniques to apply, but do not need or even want the full cognitive weight of bibliographic reference management. They (well, “we”, I wanted it too) wanted to make notes while they were reading papers, and find those notes again. We wanted to keep tags on interesting references to follow up. We wanted to identify the questions we had, and whether they were answered. And we wanted to have enough information—but not more—to help us find the original article again.

My first prototype was as simple as I could make it. There’s a picture below: it’s a ring binder, with topic dividers, and paper notes (at least one separate sheet for each article) which quickly converged on a pro forma layout as shown.

An early prototype of the Labrary app.
An early prototype of the Labrary app.

I liked it, in fact I quickly got to a point where I wouldn’t read an article unless I had access to a pad and pen to add a page to my binder. People I showed it to liked it, too. So this seemed like a good time to crack open the software making tools!

The first software prototype was put together in spare time using GNUstep and Renaissance, and evinced two problems:

  • The UI design led back down the route of “bibliopedantry”, forcing students to put more effort into getting the citation details correct than they wanted to.
  • Renaissance lacked support for some Cocoa controls it would have been helpful to use, so there was a choice to be made to invest more into improving Renaissance or finding a different UI layout tool.
A screenshot of the ill-fated "Library" window in Labrary's GNUstep prototype.
A screenshot of the ill-fated “Library” window in Labrary’s GNUstep prototype.

This experience made me look for other inspiration for ways to organise the user interface so that students get the experience of taking notes, not of fiddling with citation data. I considered writing Labrary as a plugin for the free Calibre e-reader app, so that Labrary could focus on being about study notes and Calibre could focus on being about library management. But ultimately I found the tool that solved the problem best: Apple’s Finder.

The Labrary pro forma note as Finder stationery.
The Labrary pro forma note as Finder stationery.

I’ve recreated the pro forma note from the binder as a text file, and set the “Stationery Pad” flag in the Finder. When I open this file, Finder creates a duplicate and opens that instead, in my editor of choice: ready to become a new study note! I put this in a folder with a Zim index file, so I can get the “shoebox” view of all the notes by opening the folder in Zim. It also does full-content searching, so the goal of finding a student’s notes again is achieved.

Zim open on my research notes folder.
Zim open on my research notes folder.

I’m glad I created the lo-fi paper prototype. It let me understand what I was trying to achieve, and show very quickly that my software implementation was going in the wrong direction. And I’m always happy to be the person to say “do we need to write this, or can it be built out of other bits?”, as I explored for this project with Zim and Calibre.

Research Watch, and Java by Contract

I introduced Java by Contract, a tool for building design-by-contract style invariants, preconditions and postconditions in Java using annotations. It’s MIT licensed, contributions are welcome, and I hope this helps lots of people to introduce stronger correctness checking into your software. And book office hours if you’d like me to help you with that.

Java by Contract came about as part of Research Watch, a new blog series over at The Labrary where I talk about academic work and how us “practitioners” (i.e. people who computer who aren’t in academia) can make use of the results. The first post considers a report of Teaching Quality Object-Oriented Programming to computer science students.

By the way, I will be speaking at Coventry Tech Meetup on 10th January on the topic “Beyond TDD”, and Java by Contract will make an appearance there.

Long-time SICPers readers will remember Programming Literate, a Tumblr discussing results from empirical software engineering. And if you don’t, you’ll probably remember your feeds exploding on July 15, 2013 when I imported all of the posts from there to here. You can think of Research Watch as a reboot of Programming Literate. There’ll be papers new and vintage, empirical and opinionated, on a range of computing topics. If that sounds interesting, subscribe to the Labrary’s RSS feed.

Cleaner Code

Readers of OOP the easy way will be familiar with the distinction between object-oriented programming and procedural programming. You will have read, in that book, about how what we claim is OOP in the sentence “OOP has failed” is actually procedural programming: imperative code that you could write in Pascal or C, with the word “class” used to introduce modularity.

Here’s an example of procedural-masquerading-as-OOP, from Robert C. Martin’s blog post FP vs. OO List Processing:

void updateHits(World world){
  nextShot:
  for (shot : world.shots) {
    for (klingon : world.klingons) {
      if (distance(shot, klingon) <= type.proximity) {
        world.shots.remove(shot);
        world.explosions.add(new Explosion(shot));
        klingon.hits.add(new Hit(shot));
        break nextShot;
      }
    }
  }
}

The first clue that this is a procedure, not a method, is that it isn’t attached to an object. The first change on the road to object-orientation is to make this a method. Its parameter is an instance of World, so maybe it wants to live there.

public class World {
  //...

  public void updateHits(){
    nextShot:
    for (Shot shot : this.shots) {
      for (Klingon klingon : this.klingons) {
        if (distance(shot, klingon) <= type.getProximity()) {
          this.shots.remove(shot);
          this.explosions.add(new Explosion(shot));
          klingon.hits.add(new Hit(shot));
          break nextShot;
        }
      }
    }
  }
}

The next non-object-oriented feature is this free distance procedure floating about in the global namespace. Let’s give the Shot the responsibility of knowing how its proximity fuze works, and the World the knowledge of where the Klingons are.

public class World {
  //...

  private Set<Klingon> klingonsWithin(Region influence) {
    //...
  }

  public void updateHits(){
    for (Shot shot : this.shots) {
      for (Klingon klingon : this.klingonsWithin(shot.getProximity())) {
        this.shots.remove(shot);
        this.explosions.add(new Explosion(shot));
        klingon.hits.add(new Hit(shot));
      }
    }
  }
}

Cool, we’ve got rid of that spaghetti code label (“That’s the first time I’ve ever been tempted to use one of those” says Martin). Incidentally, we’ve also turned “loop over all shots and all Klingons” to “loop over all shots and nearby Klingons”. The World can maintain an index of the Klingons by location using a k-dimensional tree then searching for nearby Klingons is logarithmic in number of Klingons, not linear.

By the way, was it weird that a Shot would hit whichever Klingon we found first near it, then disappear, without damaging other Klingons? That’s not how Explosions work, I don’t think. As it stands, we now have a related problem: a Shot will disappear n times if it hits n Klingons. I’ll leave that as it is, carry on tidying up, and make a note to ask someone what should really happen when we’ve discovered the correct abstractions. We may want to make removing a Shot an idempotent operation, so that we can damage multiple Klingons and only end up with a Shot being removed once.

There’s a Law of Demeter violation, in that the World knows how a Klingon copes with being hit. This unreasonably couples the implementations of these two classes, so let’s make it our responsibility to tell the Klingon that it was hit.

public class World {
  //...

  private Set<Klingon> klingonsWithin(Region influence) {
    //...
  }

  public void updateHits(){
    for (Shot shot : this.shots) {
      for (Klingon klingon : this.klingonsWithin(shot.getProximity())) {
        this.shots.remove(shot);
        this.explosions.add(new Explosion(shot));
        klingon.hit(shot);
      }
    }
  }
}

No, better idea! Let’s make the Shot hit the Klingon. Also, make the Shot responsible for knowing whether it disappeared (how many episodes of Star Trek are there where photon torpedoes get stuck in the hull of a ship?), and whether/how it explodes. Now we will be in a position to deal with the question we had earlier, because we can ask it in the domain language: “when a Shot might hit multiple Klingons, what happens?”. But I have a new question: does a Shot hit a Klingon, or does a Shot explode and the Explosion hit the Klingon? I hope this starship has a business analyst among its complement!

We end up with this World:

public class World {
  //...

  public void updateHits(){
    for (Shot shot : this.shots) {
      for (Klingon klingon : this.klingonsWithin(shot.getProximity())) {
        shot.hit(klingon);
      }
    }
  }
}

But didn’t I say that the shot understood the workings of its proximity fuze? Maybe it should search the World for nearby targets.

public class World {
  //...

  public void updateHits(){
    for (Shot shot : this.shots) {
      shot.hitNearbyTargets();
    }
  }
}

As described in the book, OOP is not about adding the word “class” to procedural code. It’s a different way of working, in which you think about the entities you need to model to solve your problem, and give them agency. Obviously the idea of “clean code” is subjective, so I leave it to you to decide whether the end state of this method is “cleaner” than the initial state. I’m happy with one fewer loop, no conditions, and no Demeter-breaking coupling. But I’m also happy that the “OO” example is now object-oriented. It’s now looking a lot less like enterprise software, and a lot more like Enterprise software.

Product teams: our products are not our products

Woah, too many products. Let me explain. No, it will take too long, let me summarise.

Sometimes, people running software organisations call their teams “product teams”, and organise them around particular “products”. I do not believe that this is a good idea. Because we typically aren’t making products, we’re solving problems.

The difference is that a product is “done”. If you have a “product team”, they probably have a “definition of done”, and then release software that has satisfied that definition. Even where that’s iterative and incremental, it leads to there being a “product”. The thing that’s live represents as much of the product as has been done.

The implications of there being a “product” that is partially done include optimising for getting more “done”. Particularly, we will prioritise adding new stuff (getting more “done”) over fixing old stuff (shuffling the deckchairs). We will target productish metrics, like number of daily actives and time spent.

Let me propose an alternative: we are not making products, we are solving problems. And, as much out of honesty as job preservation, let me assure you that the problems are very difficult to solve. They are problems in cybernetics, in other words in communication and control in a complex system. The system is composed of three identifiable, interacting subsystems:

  1. The people who had the problem;
  2. The people who are trying to solve the problem;
  3. The software created to present the current understanding of the solution.

In this formulation, we don’t want “amount of product” to be a goal, we want “sufficiency of solution” to be a goal. We accept that the software does not represent the part of the “product” that has been “done”. The software represents our best effort to date at modelling our understanding of the solution as we comprehend it to date.

We therefore accept that adding more stuff (extending the solution) is one approach we could consider, along with fixing old stuff (reflecting new understanding in our work). We accept that introducing the software can itself change the problem, and that more people using it isn’t necessarily a goal: maybe we’ve helped people to understand that they didn’t actually need that problem solved all along.

Now our goals can be more interesting than bushels of software shovelled onto the runtime furnace: they can be about sufficiency of the solution, empowerment of the people who had the problem, and improvements to their quality of life.

Mapping software engineering tools

Despite the theory that everything can be done in software (and of course, anything that can’t be done could in principle be approximated using numerical methods, or fudged using machine learning), software engineering itself, the business of writing software, seems to be full of tools that are accepted as de facto standards but, nonetheless, begrudgingly accepted by many teams. What’s going on? Why, if software is eating the world, hasn’t it yet found an appealing taste for the part of the world that makes software?

Let’s take a look at some examples. Jira is very popular among many people. I found a blog post literally called Why I Love Jira. And yet, other people say that Jira is an anti pattern, a sentiment that gets reasonable levels of community support.

Jenkins is almost certainly the (“market”, though it’s free) leader among continuous delivery tools, a position it has occupied since ousting Hudson, from which it was forked. Again, it’s possible to find people extolling the virtues and people hating on it.

Lastly, for some quantitative input, we can find that according to the Stack Overflow 2018 survey, most respondents (78.9%) love Rust, but most people use JavaScript (69.8%). From this we draw the interesting conclusion that the most popular tool in the programming language realm is not, actually, the one that wins the popularity contest.

So, weird question, why does everybody do this to themselves? And then more specifically, why is your team doing it to yourselves, and what can you do about it?

My hypothesis is that all of these tools succeed because they are highly configurable. I mean, JavaScript is basically a configuration language for Chromium (don’t @ me) to solve/cause your problem. Jira’s workflows are ridiculously configurable, and if Jenkins doesn’t do what you want then you can find a plugin to do it, write a plugin to do it or make a Groovy script that will do it.

This appeals to the desire among software engineers to find generalisations. “Look,” we say, “Jenkins is popular, it can definitely be made to do what we want, so let’s start there and configure it to our needs”.

Let’s take the opposing view for the moment. I’m going to drop the programming language example of JS/Rust, because all programming languages are, roughly speaking, entirely interchangeable. The detail is in the roughness. The argument below still applies, but requires more exposition which will inevitably lead to dissatisfaction that I didn’t cover some weird case. So, for the moment, let’s look at other tools like Jira and Jenkins.

The exact opposing view is that our project is distinct, because it caters to the needs of our customers and their (or these days, probably our) environment, and is understood and worked on by our people with our processes, which is not true for any other project. So rather than pretend that some other tool fits our needs or can be bent into shape, why don’t we build our own?

And, for our examples, building such a tool doesn’t appear to be a big deal. Using the expansive software engineering term “just”, a CD tool is “just” a way to run each step in the deployment pipeline and tell someone when a step fails. A development-tracking tool is “just” a way to list the things the team is or could be working on.

This is more or less a standard “build or buy” question, with just one level of indirection: both building and buying are actually measured in terms of time. How long would it take the team to write a new CD tool, and to maintain it? How long would it take the team to configure Jenkins, and to maintain it?

The answer should be fairly easy to consider. Let’s look at the map:

We are at x, of course. We are a short way from the Path of Parsimony, the happy path along which the generic tools work out of the box. That distance is marked on the map as .

Think about how you would measure for your team. You would consider the expectations of the out-of-the-box tool. You would consider the expectations of your team, and of your project. You would look at how those expectations differ, and try to quantify the result.

This tells you something about the gap between what the tool provides by default and what you need, which will help you quantify the amount of customisation needed (the cost of building a spur out from the Path of Parsimony to x). You can then compare that with the cost of building a tool that supports your position directly (the cost of building your own path, running through x).

But the map also suggests another option: why don’t we move from x closer to the path, and make smaller? Which of our distinct assumptions are incidental and can be abandoned, which are essential and need to be supported, and which are historical and could be revised? Is there a way to change the context so that adopting the popular tool is cheaper?

[Left out of the map but just as important is the related question: has somebody else already charted a different path, and how far are we from that? In other words, is there a different off-the-shelf product which needs less configuration than the one we’ve picked, so the total migration-plus-configuration cost is less than sticking where we are?]

My impression is that these questions tend to get asked once at the start of a project or initiative, then not again until the team is so far away from the Path of Parsimony that they are starting to get tangled and stung by the Weeds of Woe. Teams that change tooling such as their issue trackers or CD pipeline tend to do it once the existing way is already hurting too much, and the route back to the path no longer clear.

More speed, lower velocity

I frequently meet software teams who describe themselves as “high velocity”, they even have graphs coming from Jira to prove it, and yet their ability to ship great software, to delight their customers, or even to attract their customers, doesn’t meet their expectations. A little bit of sleuthing usually discovers the underlying problem.

Firstly, let’s take a look at that word, “velocity”. I, like Kevlin Henney, have a background in Physics, and therefore I agree with him that Velocity is a vector, and has a direction. But “agile” velocity only measures amount of stuff done to the system over time, not the direction in which it takes the system. That story may be “5 points” when measured in terms of heft, but is that five points of increasing existing customer satisfaction? Five points of new capability that will be demoed at next month’s trade show? Five points of attractiveness to prospects in the sales funnel?

Or is it five points of making it harder for a flagship customer to get their work done? Five points of adding thirty-five points of technical debt work later? Five points of integrating the lead engineer’s pet technology?

All of these things look the same in this model, they all look like five points. And that means that for a “high-velocity” (but really low-velocity, high-speed) team, the natural inclination is to jump on it, get it done, and get those five points under their belt and onto the burn down chart. The faster they burn everything down, the better they look.

Some of the presenting symptoms of a high-speed, low-velocity team are listed below. If you recognise these in your team, book yourself in for office hours and we’ll see if we can get you unstuck.

  • “The Business”: othering the rest of the company. The team believes that their responsibility is to build the thing that they were asked for, and “the business” needs to tell them what to build, and to sell it.
  • Work to rule: we build exactly what was asked for, no more, no less. If the tech debt is piling up it’s because “the business” (q.v.) doesn’t give us time to fix it. If we built the wrong thing it’s because “the business” put it at the top of the backlog. If we built the thing wrong it’s because the acceptance criteria weren’t made clear before we started.
  • Nearly done == done: look, we know our rolling average velocity is 20 bushels of software, and we only have 14 furlongs and two femtocandela of software to show at this demo. But look over here! These 12 lumens and 4 millitesla of software are in QA, which is nearly done, so we’ve actually been working really hard. The fact that you can’t use any of that stuff is unimportant.
  • Mini-waterfall: related to work to rule (q.v.), this is the requirement that everyone do their bit of the process in order, so that the software team can optimise for requirements in -> software out and get that sweet velocity up. We don’t want to be doing discovery in engineering, because that means uncertainty, uncertainty means rework, and rework means lower velocity.
  • Punitive estimation: we’re going to rename “ambiguity” to “risk”, and then punish our product owner for giving us risky stories by boosting their estimates to account for the “risk”. Such stories will never get scheduled, because we’ll never be asked to do that one risky thing when we can get ten straightforward things done in what we are saying is the same time.
  • Story per dev: as a team, our goal is to shovel as much software onto the runtime furnace as possible. Therefore we are going to fan out the tasks to every individual. We are each capable of wielding our own shovel, and very rarely do we accidentally hit each other in the face while shovelling.