Models and iteration speed in coding

“The best material model of a cat is another, or preferably the same, cat.” -Arturo Rosenblueth and Norbert Wiener, The Role of Models in Science

What do programmers do when they are programming? One interpretation is that they are high-throughput empiricists, iteratively developing and testing many small hypotheses about what various pieces of code (including their own!) actually do by quickly running many micro-experiments. Thinking about how to make these experiments fast, cheap, and reliable can be a useful lens for understanding the costs and benefits of various software development settings, tools, and practices.

Software development: a day in the life

A few significant challenges around coding are

  1. not knowing what we are trying to do
  2. not knowing how to do it
  3. not knowing if we’ve successfully done it

Challenge 1 is often a bigger picture business context question that we’ll set aside for this discussion, but Challenges 2 & 3 arguably constitute a sizable chunk of day-to-day “hands on keyboard” development activity. For a very simple example, say we have a data structure representing a set of customer orders and we want to find the order with the largest purchase price. This use case is probably easy to accomplish with the standard libraries, but maybe we don’t know the exact invocations off the top of our heads. By all means we should certainly RTFM, but to know for sure the fastest and easiest thing might be to work it out empirically with a small code snippet. This could be done in the REPL, via a quick script, or in a small unit test. Minutes of trial and error tinkering here could save hours of debugging later when the larger system isn’t behaving as intended.

Fast feedback, high fidelity

This kind of cheap, iterative experimentation with fast feedback is the closest thing to a “free lunch” as is likely to found in software development productivity. So what exactly is going on here? In the above example is our simple script, test, or REPL session is acting a model of the use case in our program, preserving the essential characteristics while being more amenable to rapid experimentation than the full system we’re building. The critical properties of such a model are:

  1. Fast feedback: quick and easy cycles from making changes to observing their results
  2. High fidelity: reasonable likelihood that behaviors will transfer or replicate to the true target context

Fast feedback is important because, as nicely described in “You are solving the wrong problem”, the limiting factor in this mode of developer productivity is the number of experiments we can run, and faster cycle times means more iterations. Back to our example, imagine how painful it would be if understanding each minor change to our hypothetical largestPurchase() function required re-compiling a 1M LOC application, packaging and deploying it, and then manually verifying the behavior from some UI flow.

High fidelty is critical to the usefulness of the experimental findings. If the experimental context is too far removed from the target environment, we run the risk that our results do not apply in the settings we care about. Simple examples of this are a unit test where the test stub of an external dependency diverges siginficantly from the implementation, or the “works on my machine” phenomenon.

In the ideal case, imagine working in some kind of magical “ultimate debug mode” that instantly surfaced arbitrarily detailed information about the entire set of consequences of even the smallest change exactly as they would play out in the full system.

Success stories

We can apply this approach to understand the appeal and utility of various development technologies and approaches:

All of the above leverage interactivity to quickly convey the impacts of small changes back to the user. In the cases of Data Science Notebooks and JavaScript, the target environment itself is well-suited to direct experimentation (data analysis in the web notebook, or webpage behavior in the browser). REPLs and TDD can be used to quickly test and verify the behavior of isolated pieces of code, after which the user can reason about how those findings will translate into the program context. Compile-time or IDE-assisted type checking and linting can also be thought of in this way: these tools embody some very limited model of the program, and can therefore give you very fast feedback about your changes, such as “you are referencing an undefined variable”, “you are trying to do arithmetic on a String”, and so on.

More challenging domains

Looking for cases where it is difficult to get this kind of fast and reliable feedback can also be an interesting exercise. These cases are often instantiations of the “Norbert Wiener’s cat” quoted above: truly high fidelity experiments require basically doing the thing for real (i.e., the only model is the actual cat), which can incur long feedback cycle times, business risk, or other costs.


Coding in a microservices architecture often involves dependencies on other services, which raises the question of how one can quicky experiment and verify your understanding of other services and your own code’s usage of them. Spinning up an entire versions of the full service may be complex or impractical (failing fast feedback), and test fixtures or other simulators of other services may not be fully realistic (failing high fidelity). Adapting development tools and practices to microservices is a huge topic, which is a testament to the difficulties here. For a much deeper and more comprehensive discussion of this issue, see “Testing Microservices, the sane way” by Cindy Sridharan.


“Infrastructure as code” is a popular phrase, but (so far) the IDE is unlikely to be able to tell us what is going to happen when we switch over to a different load balancer, spin up a new service, or change the firewall rules. The lower-level details at play here tend to resist abstraction (hard to get high fidelity), with practices converging towards some variant of “try it and see” (which can suffer from expensive feedback cycles).

X as a Service

Applications increasingly involve stitching together external services provided by cloud providers and other platforms. In some sense, we can consider these as “external microservices”, and they inherit many of the same complications, albeit while being possibly even more opaque.

Evaluating ideas and improving pain points

To the extent that this framework accurately captures characteristics of software development, what can it tell us? Whenever we encounter excessive developer tedium or friction, we can try to see if we are suffering from cases where our models of the target system lack fidelity, have long feedback cycles, or both. Possible approaches to improving things can likewise then be understood as attacking one of these fronts:

  1. developing a higher fidelity proxy or model to the target system
  2. getting faster feedback on the existing target

Many emerging tools or practices around microservices, infrastructure, and cloud applications can be understood as trying to enable faster feedback cycles. Stretching the “Norbert Wiener’s cat” analogy to the breaking point, the “test in prod” school of thought is arguably about builiding a suitably robust, flexible, and well-instrumented cat. On the other hand, trying to develop higher fidelity simulations or models of these environments (without using the actual cat) seems like a relatively less well-explored direction so far.