It’s not a super controversial to assert that the software engineering interview process is broken, but I think it’s worthwhile to do that anyway. The software engineering interview is broken. There are lots of reasons for this:

• interview processes are overoptimized for rejecting candidates that aren’t good, that they often reject candidates that are good. This isn’t a problem if it happens occasionally, but it’s really routine.
• it’s difficult to design an interview process that’s works consistently well across different levels and different kinds of roles, and companies/teams can easily get into a place where they really can only hire one type or level of engineer.
• while many engineering teams know that the hiring process is biased, most of the attempts to mitigate this focus on the bias of the interviewer by making interview processes more consistent across candidate or easier to score objectively, while abdicating for the ways that the process can be biased toward certain kinds of candidates.

I’ve been part of lots of conversations over the years about “making interviews better,” and many of the improvements to the process that come out of these conversations don’t do much and sometimes exacerbate the biases and inefficiencies of the process. I think also, that the move toward remote work (and remote interviewing,) has presented an underrealized opportunity to revisit some of these questions and hopefully come up with better ways of interviewing and building teams.

To unwind a bit, the goals of an interview process should be:

• have a conversation with a candidate to ensure that you can communicate well with them (and them with you!) and can imagine that they’ll fit into your team or desired role.
• identify skills and interests, based on practical exercises, review of their past work (e.g. portfolio or open source work,) that would complement your team’s needs. Sometimes takes “figure out if the person can actually write code,” but there are lots of ways to demonstrate and assess skills.
• learn about the candidates interests and past projects to figure out if there’s alignment between the candidate’s career trajectory and the team you’re building.

Most processes focus on the skills aspect and don’t focus enough on the other aspects. Additionally, there are a bunch of common skills assessments that lots of companies use (and copy from eachother!) and most of them are actually really bad. For example:

• live coding exercises often have to be really contrived in order to fit within an hour interview, and tend to favor algorithims problems that folks either have memorized because they recently took a class or crammed for interviews. As engineers we almost never write code like this, and the right answer to most of these problems is “use a library function”, so while live coding is great for getting the opportunity to watch a candidate think/work on a problem, success or failure aren’t necessarily indicative of capability or fit.
• take home coding problems provide a good alternative to live coding exercises, but can be a big imposition timewise particularly people on people who have jobs while interviewing. Often take home exercises also require people to focus more on buildsystems and project-level polish rather than the kind of coding that they’re likely to do more of. The impulse with take home problems is to make them “bigger,” and while these problems can be a little “bigger” than an hour, a lot of what you end up looking at with these problems is also finishing touches so keeping it shorter is also a good plan.
• portfolio-style reviews (e.g. of open source contributions or public projects,) can be great in many situations, particularly when paired with some kind of session where the candidate can provide context, but lots of great programmers don’t have these kinds of portfolios because they don’t program for fun (which is totally fine!) or because their previous jobs don’t have much open source code. It can also be difficult to assess a candidate in situations where these work samples are old, or are in codebases with awkward conventions or requirements.

There isn’t one solution to this, but:

• your goal is to give candidates the opportunity to demonstrate their competencies and impress you. Have an interview menu¹ rather than an interview process, and let candidates select the kind of problem that they think will be best for them. This is particularly true for more senior candidates, but I think works across the experience spectrum.

• if you want to do a programming or technical problem in real time, there are lots of different kinds of great exercises, so avoid having another candidate implement bubble sort, graph search, or reverse a linked list. Things like:

  • find a class (or collection of types/functions) in your codebase that you can share and have the candidate read it and try and understand/explain how it works, and then offer up suggestions for how to improve it in some way. I find this works best with 100/200 lines of code, and as long as you can explain idioms and syntax to them, it doesn’t matter if they know the language. Reading code you don’t know '
  • provide the candidate with a function that doesn’t have side effects, but is of moderate length and have them write tests for all the edge cases. It’s ok if the function has a bug that can be uncovered in the course of writing tests, but this isn’t particularly important.
  • provide the candidate with a set of stubs and a complete test suite and have them implement the interface that matches the test cases. This works well for problems where the class in question should implement a fairly pedestrian kind of functionality like “a hash map with versioned values for keys,” or “implement an collection/cache that expires items on an LRU basis.”
  • have the candidate do a code review of a meaningful change. This is an opportunity to see what it’s like to work with them, to give them a view into your process (and code!), and most importantly ask questions, which can provide a lot of insight into their mindset and method.

  I think that the menu approach also works well here: different people have different skills and different ways of framing them, and there’s no real harm in giving people a choice here.

• independent/take home/asynchronous exercises tend to be better (particularly for more senior candidates,) as it more accurately mirrors the way that we, as programmers work. At the same time, it’s really easy to give people problems that are too big or too complex or just take too long to solve well. You can almost always get the same kind of signal by doing smaller problems anyway. I also believe that offering candidates some kind of honoraria for interview processes are generally a good practice.

• one of the big goals of the interview processes is to introduce a candidate to the team and give them a sense for who’s on the team and how they operate, which I think has given rise to increasingly long interview sequences. Consider pairing up interviewers for some or all of your interview panel to give candidates greater exposure to the team without taking a huge amount of their time. This is also a great way to help your team build skills at interviewing.

• assessing candidates should also balance the candidates skills, alignment with the team, with the team’s needs and capacity for ramping new members. Particularly for organizations that place candidates on teams late in the process, it’s easy to effectively have two processes (which just takes a while,) and end up with “good” candidates that are just haphazardly allocated to teams that aren’t a good fit.

These are hard problems, and I think its important both to be open to different ways of interviewing and reflecting on the process over time. One of the great risks is that a team will develop an interview process and then keep using it even if it turns out that the process becomes less effective as interviewers and needs change. Have (quick) retrospectives about your interview process to help make sure that stays fresh and effective.'

I think this is a follow up, in someways, to my earlier post on Staff Engineering. If you liked this, check that out!

A while ago I packaged up my emacs configuration for the world to see/use and I’m pretty proud of this thing: it works well out of the box, it’s super minimal and speedy, and has all of the features. I don’t think it’s the right solution for everyone, but I think there are a case of users for whom this configuration makes sense. I’ve definitely also benefited a lot for thinking about this “configuration” as a software project at least in terms of keeping things organized and polished and reasonably well tested. It’s a good exercise.

Historically, I’ve used my emacs configuration ans as a sort of “fun side project” and while I tried to avoid spending too much time tweaking various things, it did feel like the kind of thing that was valuable (given how much time I spend in a text editor,) without being too distracting. Particularly, early in the pandemic, or during periods over the summer when I was between jobs.

Then, I put the configuration in a public repo, and I basically haven’t made any meaningful changes since then. One part of this is clearly that I put a lot of time into polishing things in the initial push to get it released, and there haven’t been many bugs that have inspried any kind of major development effort. Another part is that, the way I use an editor isn’t really changing. I’m writing code and English and using a couple of applications (e.g. email and org-mode) within emacs, but I’m not really (often) adding new or different kinds of work, and while this isn’t exacting from a blogging perspective* it is exciting from a “things just work perspective.”

I have refered to myself as a degenerate emacs user. I’ve sometimes said unrepentant, but I think it’s basically the same. I’ve also realized that, given that I’ve basically been using emacs the same way since 2008 or so, I’m kind of an old timer, even if it doesn’t much feel like that, and there are lots of folks with longer histories.

I think I used care more about what tools other people used to edit text, even a couple of years ago, I thought that having good initial configuration and better out of the box experiences for emacs would lead to more people using emacs, which would be cool because they’d get to use a cool piece of software and we’d get more emacs users.

Increasingly, however, while I think emacs is great and people should use it, I’m less concerned: people should use what they want, and I think there will always be a enough people here and there who want to use emacs and that’s good enough for me. I think having good out of the box experiences are important, but it’s not a one-size fits all kind of situation. I also think that VS Code is pretty great software, and I like a lot of the implications for remote editing, even if I’m not particularly interested in it for myself.

Enjoy the repo, and let me know if there’s anything terrible about it. I’ve been getting back into blogging recently, and have started tweaking a few things about the ways I use computers/emacs, mostly in terms of exploring tmux (hah!) and also considering avoiding GUI emacs entirely. Stay tuned if you’re interested!

There’s this relatively minor Emacs bug that I’ve been aware of for a long time, years. The basic drift is that on Linux systems, when running with GTK/Emacs as a daemon, and the X11 session terminates for any reason the Emacs daemon terminates. Emacs daemons are great: you start Emacs once, and it keeps running independently of what ever windows you have open. You can leave files open in Emacs buffers and not have move between different projects with minimal context switching costs.

First of all, emacs’s daemon mode is weird. I can’t think of another application that starts as a daemon (in the conventional UNIX double-forking manner,) and then a client process runs and spawns GUI (potentially) windows. If there are other applications that work this way, there aren’t many.

Nevertheless, being able to restart the window manager without loosing the current state of your Emacs session is one of the chief reasons to run Emacs in daemon mode, so this bug has always been a bit irksome. Also since it’s real, and for sure a thing, why has it taken so long to address? Lets dig a little bit deeper.

There are two GNOME bugs related to this:

• Closing Displays
• Emacs Disconnect Followup

What’s happening isn’t interesting or complicated: Emacs calls an API, which behaves differently than Emacs expects and needs, but not (particularly) differently than GNOME expects or needs. Which means GNOME has little incentive to fix the bug--if they even could without breaking other users of this API.

Emacs can’t fix the problem on their own, without writing a big hack around GNOME components, which wouldn’t be particularly desirable or viable, and because this works fine with the other toolkit (and is only possible in some situations,) it doesn’t feel like an Emacs bug.

We have something of a stalemate. Both party thinks the other is at fault. No one is particularly incentivized to fix the problem from their own code, and there is a work around,¹ albeit a kind of gnarly one.

This kind of issue feels, if not common, incredibly easy for a project--even one like emacs--to stumble into and quite easy to just never resolve. This kind of thing happens, in some form, very often and boundaries between libraries make it even more likely.

On the positive side, It does seem like there’s recent progress on the issue, so it probably won’t be another 10 years before it gets fixed, but who knows.

As a degenerate emacs user, as it were, I have of course used org-mode a lot, and indeed it’s probably the mode I end up doing a huge amount of my editing in, because it’s great with text and I end up writing a lot of text. I’m not, really, an org mode user in the sense that it’s not the system or tool that I use to stay organized, and I haven’t really done much development of my own tooling or process around using orgmode to handle document production, and honestly, most of the time I use reStructuredText as my preferred lightweight markup language.

I was thinking, though, as I was pondering ox-leanpub, what even is org-mode trying to do and what the hell would a product manager do, if faced with org-mode.

In some ways, I think it sucks the air out of the fun of hacking on things like emacs to bring all of the “professionalization of making software” to things like org-mode, and please trust that this meant with a lot of affection for org-mode: this is meant as a thought experiment.

Org has a lot going on:

• it provides a set of compelling tools for interacting with hierarchical human-language documents.
• it’s a document markup and structure system,
• the table editing features are, given the ability to write formula in lisp, basically a spreadsheet.
• it’s a literate programming environment, (babel)
• it’s a document preparation system, (ox)
• it’s a task manager, (agenda)
• it’s a time tracking system,
• it even has pretty extensive calendar management tools.

Perhaps the thing that’s most exciting about org-mode is that it provides functionality for all of these kinds of tasks in a single product so you don’t have to bounce between lots of different tools to do all of these things.

It’s got most of the “office” suite covered, and I think (particularly for new people, but also for people like me,) it’s not clear why I would want my task system, my notes system, and my document preparation system to all have their data intermingled in the same set of files. The feeling is a bit unfocused.

The reason for this, historically makes sense: org-mode grew out of technically minded academics who were mostly using it as a way of preparing papers, and who end up being responsible for a lot of structuring their own time/work, but who do most of their work alone. With this kind of user story in mind, the gestalt of org-mode really comes together as a product, but otherwise it’s definitely a bit all over the place.

I don’t think this is bad, and particularly given its history, it’s easy to understand why things are the way they are, but I think that it is useful to take a step back and think about the workflow that org supports and inspires, while also not forgetting the kinds of workflows that it precludes, and the ways that org, itself, can create a lot of conceptual overhead.

There are also some gaps, in org, as a product, which I think grow out of this history, and I think there are

They are, to my mind:

• importing data, and bidirectional sync. These are really hard problems, and there’ve been some decent projects over the years to help get data into org, I think org-trello is the best example I can think about, but it can be a little dodgy, and the “import story” pales in comparison to the export story. It would be great if:
  • you could use the org interface to interact with and manipulate data that isn’t actually in org-files, or at least where the system-of-record for the data isn’t org. Google docs? Files in other formats?
• collaborating with other people. Org-mode files tend to cope really poorly with multiple people editing them at the same time (asynchronously as with git,) and also in cases where not-everyone uses org-mode. One of the side effects of having the implementation of org-features so deeply tied to the structure of text in the org-format, it becomes hard to interact with org-data outside of emacs (again, you can understand why this happens, and it’s indeed very lispy,), which means you have to use emacs and use org if you want to collaborate on projects that use org.
  • this might look like some kind of different diff-drivers for git, in addition to some other more novel tools.
  • bi-directional sync might also help with this issue.
• beyond the agenda, building a story for content that spans multiple-file. Because the files are hierarchical, and org provides a great deal of taxonomic indexing features, you really never need more than one org-file forever, but it’s also kind of wild to just keep everything in one file, so you end up with lots of org-files, and while the agenda provides a way to filter out the task and calendar data, it’s sort of unclear how to mange multi-file systems for some of the other projects. It’s also the case, that because you can inject some configuration at the file level, it can be easy to get stuck.
• tools for interacting with org content without (interactive or apparent) emacs. While I love emacs as much as the next nerd, I tend to think that having a dependency on emacs is hard to stomach, particularly for collaborative efforts, (though with docker and the increasing size of various runtimes, this may be less relevant.) If it were trivially easy to write build processes that extracted or ran babel programs without needing to be running from within emacs? What if there were an org-export CLI tool?

I can’t quite decide if this title is ironic or not. I’ve been trying really hard to not be a build system guy, and I think I’m succeeding--mostly--but sometimes things come back at us. I may still be smarting from people proposing “just using docker” to solve any number of pretty irrelevant problems.

The thing is that docker does help solve many build problems: before docker, you had to either write code that supported any possible execution environment. This was a lot of work, and was generally really annoying. Because docker provides a (potentially) really stable execution environment, it can make a lot of sense to do your building inside of a docker container, in the same way that folks often do builds in chroot environments (or at least did). Really containers are kind of super-chroots, and it’s a great thing to be able to give your development team a common starting point for doing development work. This is cool.

It’s also the case that Docker makes a lot of sense as a de facto standard distribution or deployment form, and in this way it’s kind of a really fat binary. Maybe it’s too big, maybe it’s the wrong tool, maybe it’s not perfect, but for a lot of applications they’ll end up running in containers anyway, and treating a docker container like your executable format makes it possible to avoid running into issues that only appear in one (set) of environments.

At the same time, I think it’s important to keep these use-cases separate: try to avoid using the same container for deploying that you use for development, or even for build systems. This is good because “running the [deployment] container” shouldn’t build software, and it’ll also limit the size of your production containers, and avoid unintentionally picking up dependencies. This is, of course, less clear in runtimes that don’t have a strong “compiled artifacts” story, but is still possible.

There are some notes/caveats:

• Dockerfiles are actually kind of build systems, and under the hood they’re just snapshotting the diffs of the filesystem between each step. So they work best if you treat them like build systems: make the steps discrete and small, keep the stable deterministic things early in the build, and push the more ephemeral steps later in the build to prevent unnecessary rebuilding.
• “Build in one container and deploy in another,” requires moving artifacts between containers, or being able to run docker-in-docker, which are both possible but may be less obvious than some other workflows.
• Docker’s “build system qualities,” can improve the noop and rebuild-performance of some operations (e.g. the amount of time to rebuild things if you’ve just built or only made small changes.) which can be a good measure of the friction that developers experience, because of the way that docker can share/cache between builds. This is often at the expense of making artifacts huge and at greatly expanding the amount of time that the operations can take. This might be a reasonable tradeoff to make, but it’s still a tradeoff.

I end up reviewing a lot of code, and while doing code review (and getting reviews) used to take up a lot of time, I think I’ve gotten better at doing reviews, and knowing what’s important to comment on and what doesn’t

• The code review process is not there to discover bugs. Write tests to catch bugs, and use the code review process to learn about a specific change, and find things that are difficult to test for.
• As yourself if something is difficult to follow, and comment on that. If you can’t figure out what something is doing, or you have to read it more than once, then that’s probably a problem.
• Examine and comment on the naming of functions. Does the function appear to do what the name indicates.
• Think about the interface of a piece code:
  • What’s exported or public?
  • How many arguments do your functions take?
• Look for any kind of shared state between functions, particularly data that’s mutable or stored in globally accessable, or package local structures.
• Focus your time on the production-facing, public code, and less on things like tests and private/un-exported APIs. While tests are important, and it’s important that there’s good test coverage (authors should use coverage tooling to check this), and efficient test execution, beyond this high level aspect, you can keep reading?
• Put yourself in the shoes of someone who might need to debug this code and think about logging as well as error handling and reporting.
• Push yourself and others to be able to get very small pieces of code reviewed at a time. Shorter reviews are easier to process and while it’s annoying to break a logical component into a bunch of pieces, it’s definitely worth it.

After, my post on what I do for work I thought it’d be good to describe the kinds of things that make software easy to work on and collaborative. Here’s the list:

1. Minimal Documentation. As a former technical writer this is sort of painful, but most programning environments (e.g. languages) have idioms and patterns that you can follow for how to organize code, run tests and build artifacts. it’s ok if your project has exceptional requirements that require you to break the rules in some way, but the conventions should be obvious and the justification for rule-breaking should be plain. If you adhere to convention, you don’t need as much documentation. It’s paradoxical, because better documentation is supposed to facilitate accessibility, but too much documentation is sometimes an indication that things are weird and hard.
2. Make it Easy To Run. I’d argue that the most difficult (and frustrating) part of writing software is getting it to run everywhere that you might want it to run. Writing software that runs, even does what you want on your own comptuer is relatively, easy: making it work on someone else’s computer is hard. One of the big advantages of developing software that runs as web apps means that you (mostly) get to control (and limit) where the software runs. Making it possible to easily run a piece of software on any computer it might reasonably run (e.g. developer’s computers, user’s computers and/or production environments.) Your software itself, should be responsible for managing this environment, to the greatest extent possible. This isn’t to say that you need to use containers or some such, but having packaging and install scripts that use well understood idioms and tools (e.g. requirements.txt, virtualenv, makefiles, etc.) is good.
3. Clear Dependencies. Software is all built upon other pieces of software, and the versions of those libraries are important to record, capture, and recreate. Now it’s generally a good idea to update dependencies regularly so you can take advantage of improvements from upstream providers, but unless you regularly test against multiple versions of your dependencies (you don’t), and can control all of your developer and production environments totally (you can’t), then you should provide a single, centralized way of describing your dependencies. Typically strategies involve: vendoring dependencies, using lockfiles (requirements.txt and similar) or build system integration tends to help organize this aspect of a project.
4. Automated Tests. Software requires some kind of testing to ensure that it has the intended behavior, and tests that can run quickly and automatically without requiring users to exercise the software manually is absolutely essential. Tests should run quickly, and it should be possible to run a small group of tests very quickly to support iterative development on a specific area of the code. Indeed, most software development can and should be oriented toward the experience of writing tests and exercising new features with tests above pretty much everything else. The best test suites exercise the code at many levels, ranging from very small unit tests to ensure the correct behavior of the functions and methods, to higher level tests that test the functionality of higher-order functions and methods, and full integration tests that test the entire system.
5. Continuous Integration. Continuous integration system’s are tools that support development and ensure that changes to a code pass a more extensive range of tests than are readily available to developers. CI systems are also useful for automating other aspects of a project (releases, performance testing, etc.) A well maintained CI environment provide the basis for commonality for larger projects with a larger number for projects larger groups of developers, and is all but required to ensure a well supported automated test system and well managed dependency.
6. Files and Directories Make Sense. Code is just text in files, and software is just a lot of code. Different languages and frameworks have different expectations about how code is organized, but you should be able to have a basic understanding of the software and be able to browse the files, and be able to mostly understand what components are and how they relate to each other based on directory names, and should be able to (roughly) understand what’s in a file and how the files relate to eachother based on their names. In this respect, shorter files, when possible are nice, are directory structures that have limited depth (wide and shallow,) though there are expections for some programming language.
7. Isolate Components and Provide Internal APIs. Often when we talk about APIs we talk about the interface that users access our software, but larger systems have the same need for abstractions and interfaces internally that we expose for (programmatic) users. These APIs have different forms from public ones (sometimes,) but in general:
   • Safe APIs. The APIs should be easy to use and difficult to use incorrectly. This isn’t just “make your API thread safe if your users are multithreaded,” but also, reduce the possibility for unintended side effects, and avoid calling conventions that are easy to mistake: effects of ordering, positional arguments, larger numbers of arguments, and complicated state management.
   • Good API Ergonomics. The ergonomics of an API is somewhat ethereal, but it’s clear when an API has good ergonomics: writing code that uses the API feels “native,” it’s easy to look at calling code and understand what’s going on, and errors that make sense and are easy to handle. It’s not simply enough for an API to be safe to use, but it should be straightforward and clear.

i am a self taught programmer. i don’t know that i’d recommend it to anyone else there are so many different curricula and training programs that are well tested and very efficacious. for lots of historical reasons, many programmers end up being all or mostly self taught: in the early days because programming was vocational and people learned on the job, then because people learned programming on their own before entering cs programs, and more recently because the demand for programmers (and rate of change) for the kinds of programming work that are in the most demand these days. and knowing that it’s possible (and potentially cheaper) to teach yourself, it seems like a tempting option.

this post, then, is a collection of suggestions, guidelines, and pointers for anyone attempting to teach themselves to program:

• focus on learning one thing (programming language and problem domain) at a time. there are so many different things you could learn, and people who know how to program seem to have an endless knowledge of different things. knowing one set of tools and one area (e.g. “web development in javascript,” or “system administration in python,”) gives you the framework to expand later, and the truth is that you’ll be able to learn additional things more easily once you have a framework to build upon.

• when learning something in programming, always start with a goal. have some piece of data that you want to explore or visualize, have a set of files that you want to organize, or something that you want to accomplish. learning how to program without a goal, means that you don’t end up asking the kinds of questions that you need to form the right kinds of associations.

• programming is all about learning different things: if you end up programming for long enough you’ll end up learning different languages, and being able to pick up new things is the real skill.

• being able to clearly capture what you were thinking when you write code is basically a programming super power.

• programming is about understanding problems¹ abstractly and building abstractions around various kinds of problems. being able break apart these problems into smaller core issues, and thinking abstractly about the problem so that you can solve both the problem in front of you and also solve it in the future are crucial skills.

• collect questions or curiosities as you encounter them, but don’t feel like you have to understand everything, and use this to guide your background reading, but don’t feel like you have to hunt down the answer to every term you hear or see that you don’t already know immediately. if you’re pretty rigorous about going back and looking things up, you’ll get a pretty good base knowledge over time.

• always think about the users of your software as you build, at every level. even if you’re building software for your own use, think about the future version of yourself that will use that software, imagine that other people might use the interfaces and functions that you write and think about what assumptions they might bring to the table. think about the output that your program, script, or function produces, and how someone would interact with that output.

• think about the function as the fundamental building block of your software. lower level forms (i.e. statements) are required, but functions are the unit where meaning is created in the context of a program. functions, or methods depending, take input (arguments, ususally, but sometimes also an object in the case of methods) and produce some output, sometimes with some kind of side-effect. the best functions:

  • clearly indicate side-effects when possible.
  • have a mechanism for reporting on error conditions (exceptions, return values,)
  • avoid dependencies on external state, beyond what is passed as arguments.
  • are as short as possible.
  • use names that clearly describe the behavior and operations of the function.

  if programming were human language (english,) you’d strive to construct functions that were simple sentences and not paragraph’s, but also more than a couple of words/phrases, and you would want these sentences to be clear to understand with limited context. if you have good functions, interfaces are more clear and easier to use, code becomes easier to read and debug, and easier to test.

• avoid being too weird. many programmers are total nerds, and you may be too, and that’s ok, but it’s easier to learn how to do something if there’s prior art that you can learn from and copy. on a day-to-day basis, a lot of programming work is just doing something until you get stuck and then you google for the answer. If you’re doing something weird--using a programming language that’s less widely used, or in a problem space that is a bit out of mainstream, it can be harder to find answers to your problems.

Notes

Editoral Note: this is a follow up to my earlier Principles of Test Oriented Software Development post.

In software development, we write tests to make sure the code we write does what we want it to do. Great this is pretty easy to get behind.

Tests sometimes fail.

The goal, is that, most of the time when tests fail, it’s because the code is broken: you fix the code and the test passes. Sometimes when test fail there’s a bug in the test, it makes an assertion that can’t or shouldn’t be true: these are bad because they mean the test is broken, but all code has bugs, and test code can be broken so that’s fine.

Ideally either pass or fail, and if a test fails it fails repeatedly, with the same error. Unfortunately, this is of course not always true, and tests can fail intermittently if they test something that can change, or the outcome of the test is impacted by some external factor like “the test passes if the processor is very fast, and the system does not have IO contention, but fails sometimes as the system slows down.” Sometimes tests include (intentionally or not) some notion of “randomnesses,” and fail intermittently because of this.

A test suite with intermittent failures is basically the worst. A suite that never fails isn’t super valuable, because it probably builds false confidence, a test suite that always fails isn’t useful because developers will ignore the results or disable the tests, but a test that fails intermittently, particularly one that fails 10 or 20 percent of the time, means that developers always will always look at the test, or just rerun the test until it passes.

There are a couple of things you can do to fix your tests:

• write better tests: find sources of non-determinism in your test and rewrite tests to avoid these kinds of “flaky” outcomes. Sometimes this means restructuring your tests in a more “pyramid-like” structure, with more unit tests and fewer integration tests (which are likely to be less deterministic.)
• run tests more reliably: find ways of running your test suite that produce more consistent results. This means running tests in more isolated environments, changing the amount of test parallelism, ensure that tests clean up their environment before they run, and can be as logically isolated as possible.

But it’s hard to find these tests and you can end up playing wack-a-mole with dodgy tests for a long time, and the urge to just run the tests a second (or third) time to get them to pass so you can merge your change and move on with your work is tempting. This leaves:

• run tests multiple times: so that a test doesn’t pass until it passes multiple times. Many test runner’s have some kind of repeated execution mode, and if you can combine with some kind of “stop executing after the first fail,” then this can be reasonably efficient. Use multiple execution to force the tests to produce more reliable results rather than cover-up or exacerbates the flakiness.
• run fewer tests: it’s great to have a regression suite, but if you have unreliable tests, and you can’t use the multi-execution hack to smoke out your bad tests, then running a really full matrix of tests is just going to produce more failures, which means you’ll spend more of your time looking at tests, in non-systematic ways, which are unlikely to actually improve code.

I want to like test-driven-development (TDD), but realistically it’s not something that I ever actually do. Part of this is because TDD, as canonically described is really hard to actually pratice: TDD involves writing tests before writing code, writing tests which must fail before the implementation is complete or correct, and then using the tests to refactor the code. It’s a nice idea, and it definitely leads to better test coverage, but the methodology forces you to iterate inefficiently on aspects of a design, and is rarely viable when extending existing code bases. Therefore, I’d like to propose a less-dogmatic alternative: test-oriented-development.¹

I think, in practice, this largely aligns with the way that people write software, and so test oriented development does not describe a new way of writing code or of writing tests, but rather describes the strategies we use to ensure that the code we write is well tested and testable. Also, I think providing these strategies in a single concrete form will present a reasonable and pragmatic alternative to TDD that will make the aim of “developing more well tested software” more achievable.

1. Make state explicit. This is good practice for all kinds of development, but generally, don’t put data in global variables, and pass as much state (configuration, services, etc.) into functions and classes rather than “magicing” them.
2. Methods and functions should be functional. I don’t tend to think of myself as a functional programmer, as my tendencies are not very ideological, in this regard, but generally having a functional approach simplifies a lot of decisions and makes it easy to test systems at multiple layers.
3. Most code should be internal and encapsulated. Packages and types with large numbers of exported or public methods should be a warning sign. The best kinds of tests can provide all desired coverage, by testing interfaces themselves,
4. Write few simple tests and varry the data passed to those tests. This is essentially a form of “table driven testing,” where you write a small sequence of simple cases, and run those tests with a variety of tests. Having test infrastructure that allows this kind of flexibility is a great technique.
5. Begin writing tests as soon as possible. Orthodox TDD suggests that you should start writing tests first, and I think that this is probably one of the reasons that TDD is so hard to adopt. It’s also probably the case that orthodox TDD emerged when prototyping was considerably harder than it is today, and as a result TDD just feels like friction, because it’s difficult to plan implementations in a test-first sort of way. Having said that, start writing tests as soon as possible.
6. Experiment in tests. Somehow, I’ve managed to write a lot of code without working an interactive debugger into my day-to-day routine, which means I do a lot of debugging by reading code, and also by writing tests to try and replicate production phenomena in more isolated phenomena. Writing and running tests in systems is a great way to learn about them.