Building a Safety Net for Continuous Delivery with Developer Tests

It is impossible to develop a software system with a certain level of complexity unless it is built on top of a smaller working system.

I wanted to credit Bjarne Stroustup for expressing this point of view as early as 1985 in his book The C++ Programming Language, but after re-reading his Notes to the Reader, I see that the quote I remembered was on the importance of well-structured code (in which C++ excels over C), not correctness.

Still, I don’t think I am alone when I claim that we software developers find it natural to develop iteratively, thereby continuously building on top of the last iteration, the last working system.

The question is, how do we know that the system we build on is working?

The truth is that unless we have a very good verification process we don’t know if we build on a working system.

In a good continuous delivery process, we will have waves of verification in the form of continuous integration builds and deployments, automatic and manual testing by testers etc.

Naturally, developers will also develop unit tests as an integrated part of developing code, thereby ensuring that each implemented responsibility behaves as expected in isolation.

But is this good enough? Will it ensure that each iteration builds on a working system?

I think it is not good enough because,

  • Testing is usually decoupled in time and space from the development process.
  • Unit testing only verifies tiny pieces of logic in isolation, but bugs typically show up when these pieces of logic are composed into higher level behaviour.

If you ask me, developers needs to write what I call developer tests.

Developer Tests

A developer test is similar to a unit test, the difference being that we never mock any dependencies unless we absolutely must. For example, we mock external web services that our code calls, but we do not mock database access.

When we run a developer test, we run the exact same code as is run in the production system, which means that the behaviour of the test will closely match the behaviour of the production system. This means that the verification which is done by a developer test is very reliable.

When I develop new code, I always exercise the new behaviour through developer tests. This is typically much easier than setting up the production system with the relevant users with relevant permissions and relevant data to query and alter.

When my new developer tests turn green, I feel confident that my new behaviour works as intended, not only in isolation but also when run in context with huge parts of the existing functionality.

When I have verified that the existing developer tests are green I feel confident that I did not introduce regressions.

Then I check the code changes into the main branch and the new feature will be in the next release a short while after.

What Makes Developer Tests Work

I have developed the concept of developer tests over the last couple of years while working on TradingFloor.com. Since it is now second nature to use developer tests as an integral part of the software development process, it is difficult to remember why this seemed difficult, or impossible, to do just a couple of years ago.

A major part of the reason that developer tests work in TradingFloor.com is that the code is (largely) written with sensible principles in mind, and in this context one of the SOLID principles, the Dependency Inversion Principle (DIP), is essential. And furthermore, using Dependency Injection is practical.

This means that when I exercise my new behaviour through the method Foo on class Bar …

public class Bar
{
Bar(IMyDependency1 dep1, IMyDependency2 dep2) { /*…*/}
void Foo() { /*… use dep1 and dep2 */ }
}

… then I also run the code of the two dependencies (and their dependencies, and their dependencies …), including any kind of logging, interception and whatnot. This is in contrast to a unit test in which I would mock the two dependencies.

In addition to DIP, our experience is that the Command Query Separation (CQS) principle is a great help in general in our code structure, and in particular this principle makes writing developer tests easy. I suppose you can imagine that a code base composed of queries (we call them readers) and command handlers are very handy when building up a test scenario and when asserting the outcome of a test.

Why is the Entire World not Using Developer Tests

Developer tests allow for faster development, they provide fast feedback on correctness during development and they provide a safety net for the future.

Yet, I have not seen a rush for all other developers to get on board and start to use developer tests. Why?

Here are some of the counter arguments I have heard so far,

  • It cannot be done.
    That argument is a couple of years old. Today we are doing it on a daily basis.
  • It is too slow.
    No, our 850+ tests run in one minute on a typical developer PC.
  • Developer tests are very brittle.
    No, it is the other way around. Unit tests are often very brittle because you need to re-do your mocking when refactoring code. Developer tests don’t have this problem and they are surprisingly solid towards refactoring.
  • I cannot do it because my code is much more complex than your code.
    If your code is really complex, working without a safety net is not an option! You can do it.
  • I run a heavy SQL database, tests will be too slow and difficult to set up.
    Right, we run a no-SQL database so building up an entire database per test is fast and easy. Installing the database locally and on any build or test system is also easy and fast. All that will be a hassle with some SQL databases, but not impossible. If you have to, you can isolate SQL access and mock it out but I would prefer not to.

Where Are We Now

I would love to share more details but I feel that I need to introduce developer tests to at least one more project before I can express myself without going into too much detail.

I will come back with more information once I have done that. In the meantime, if you would like me to elaborate on this or that, please ask.

Continuous Delivery – a Safety Net will make you Lazy

I sincerely believe that some kind of safety net is needed when coding.

In fact I believe that having a safety net is especially important when doing continuous delivery.

Before I managed to blog on my opinions regarding this, I read Scott Berkun’s book The Year Without Pants in which Scott makes quite the opposite argument based on his experience from Automattic (and contrary to his experience from Microsoft).

A Safety Net will make you Lazy

Essentially, Scott backs up Automattic’s belief in the philosophy: safeguards don’t make you safe; they make you lazy. This may to a certain extend be true in some cases, as some people drive faster when they get ABS brakes, and football players take more risks because of their padding. And on the same token, if you find yourself in a high tower with no railing, you will be very cautious about every step you take as a fall would kill you. And since you are very cautious, it is unlikely that you will be killed.

Does this philosophy work in software development? Should we skip manual and automatic testing as well as other kinds of verification before we deploy the latest changes to the Live system? Should we essentially skip the entire safety net and rely on developers being very cautious?

No!

Being cautious only takes you part of the way. And if you are too cautious, there will be much needed changes to the code that you will never dare do. Besides, even cautiously made changes could have unexpected effects, regressions, on other parts of the code. If you are too cautious, your code will rot and eventually become unmanageable. (There is a brilliant description of code rot and how to avoid it in Robert C Martin’s book Clean Code)

Still, in a perfect world, coding without a safety net could actually work. In theory it’s simple, and I have already blogged about it. First of all, all changes to code must be small, additive increments – baby steps. Secondly, the code must be crafted by rigorously following the SOLID principles. With a perfect code base with low coupling and high coherence, most baby step changes would consist of adding new code that plug in without changing existing code, or would be a few simple changes to a single existing class, in either case the impact on the system would be restricted and well understood.

Alas, the world is not perfect, neither is the code base that most developers work on.

Besides, sometimes you need to do a refactoring that will impact quite a bit of functionality. Sometimes you change a single or a few lines of code, but there is no simple way to fully understand its impact. In both cases the risk of regressions can be lowered with rigorous verification.

Building a Safety Net

I am quite happy that I read Scott’s book, as it made me think a bit deeper about building up a safety net. (And I can certainly recommend the book to anyone interested in the process of developing software.) Note that what I mention here is regarding the part of the safety net that developers must build and maintain.

Here is my opinion:

  1. Build and maintain automatic tests for non-trivial functionality.
  2. Do not build tests for trivial, unimportant or easily verified functionality.

The second bullet is based on my experience that often huge amounts of tests are made, but the maintenance burden is so high that the tests are not maintained, new tests are not written and (unit) testing in general gets a bad reputation among developers. In such a case, needed refactoring is generally avoided and the code will rot. For these reasons, it is good practice to avoid tests that would only reveal bugs of low severity, of which many would be found anyway, simply with a quick glance at the system.

So the trick is to have exactly the tests that make sense and ensure that they are maintainable.

Even then, a safety net could make a developer lazy. It is never an option to simply throw the code over the fence to the Testing Department, effectively making buggy code somebody else’s problem. Rather, developers must build up a safety net as an integral part of developing code.

Being cautious and having a safety net is the way to go.

Why do some Developers Prefer not to have a Safety Net?

As a final note I have a possible explanation to why Automattic developers prefer working without a safety net.

Scott explains how he once went to India and climbed the stone tower of Jantar Mantar. There was no railing and a fall would kill anyone. But people were cautious because of the lack of safety measures.

I also climbed the stone tower of Jantar Mantar years ago when I was much younger. I clearly remember looking down at our hilarious guide at the ground, but I do not particularly remember the missing railing.

Could it be that focus on safety measures increases with age and experience?

Continuous Delivery

How does the topic Continuous Delivery fit in a blog which is mainly on code architecture and code crafting?

In this post I hope to convince you that it fits quite well.

I am working on a team which has successfully practiced continuous delivery for some time and it turned out that pretty much everything we did affected our ability to actually deliver continuously.

In this context everything literally means everything in the software development life-cycle, from stated business goals to provable value for the customers. This does not only involve crafting the code but also deriving scope from business goals in a way that can guide the further process, which includes various kinds of testing and verification in parallel with coding, as well as operations tasks. Continuously.

The bad part is that if you wish to practice continuous delivery you need to rethink everything you do and you need to break quite a few habits. The good part is that it does not involve radically new practices, you only need to take existing and well-known practices more seriously and apply them consistently. Continuously.

It is common knowledge that the way we structure code, the way we break down complexity into manageable bits and the way we apply well-understood and agreed upon patterns consistently greatly affects our ability to deliver. So it is not a surprise that when we need to continuously deliver with a short cycle time, then all this becomes even more important.

What Is Continuous Delivery?

The short answer to that is look it up or read the book.

If you ask me, continuous delivery is really Lean Manufacturing principles applied to software development. The principles from physical manufacturing have been modified slightly in order to make sense in the software development world.

Do Lean Manufacturing Principles and Processes Really Fit With Software Development Ditto?

It is surprising how many physical manufacturing processes fit nicely with software development processes.

In lean manufacturing you want to have a short cycle time, meaning the time from feeding raw material into the factory and until the product is finished (and by then the product is hopefully valuable to the target user). This sounds awfully similar to what we want to achieve with continuous delivery, doesn’t it?

One way to achieve a short cycle time is to produce small batches, i.e. producing only a small number of items of a given type before switching to making another type of item. The challenge with small batches is that it takes significant time to set-up machines between two batches. And does it make sense to produce, say, 10 items in two hours, then spend 2 hours setting up a machine in order to produce 10 items of another type in two hours etc.? Wouldn’t it be better to produce 1000 items per batch, thus making the set-up time relatively small? The answer is that, yes it probably makes sense to have a small batch size, and no building up a large inventory is probably not a good idea. Optimize the process of setting up machines rather than increasing the batch size thereby avoiding the large amount of Work In Process (WIP) at any given time. The rationale is really quite simple and the logic makes a lot of sense. If you don’t believe me, I suggest you read the novel The Gold Mine: A Novel of Lean Turnaround. Yes, it’s a novel so it doesn’t really feel like working when you read it. But it will give you a gentle introduction to lean principles in manufacturing. (If you get really fired up on this topic I suggest you also read The Goal: A Process of Ongoing Improvement. It will teach you about the importance of focusing on bottle necks in the process.)

In the world of software development we have a similar challenge with batch sizes, the batch size in this context being the amount of code that we deliver (or anything else we deliver, but let’s focus on code for now).

We see the batch size challenge at multiple levels. At the highest level, the business would like us to turn the business goals into value for the customer as fast as possible, and one way to do that is to initially focus narrowly on minimal functionality. That’s a no-brainer, you say, but the business will never accept it – they always want it all and the want it yesterday, right? Well, it can be done. As Eric Ries describes in The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses it makes a lot of sense to quickly make a Minimal Viable Product (MVP) that can be validated and used in the further process. That is a small batch size which is used to achieve a short cycle time in software development!

We also see the batch size challenge for individual check-ins into a version control system and when merging change sets among branches. If you are really continuous you do continuous deployment and each check-in will be deployed directly to the live production system. This is not as risky as you might think if you have automated as much as possible, including tests, and if you can live with occasional hick-ups which you need to address fast. In my team we do not deploy each check-in, although we do deploy often after a short manual verification process that augments all our automated processes.

My advice here is to always strive at delivering baby steps, meaning small focused check-ins and small focused features. Any process which is in the way of doing baby steps must be optimized. If gated check-in takes 6 hours on a good day, then find another way to check the code. If code reviews have response times of several hours or days you need to look into that part of the process. If testing is a bottle-neck you need to address that, probably by adding resources in the short-term and doing more automation in the long-term (so that manual testing can be focused on new and UI centric features). Our goal must be to have as little WIP as possible, which in this context means code that we have spent time on but which has not yet been fully verified and turned into value for actual users.

That’s It

It is really that simple, deliver baby steps quickly, optimize any process that prevents you from doing that, automating as much as possible on the way.

But even though it is simple to state, it is not always easy to do it. I am writing primarily to code craftsmen, but before we go deep into core coding topics, you should convince those who control your process to read on Lean Manufacturing and Lean Startup. And if they get really fired up, they should also read Specification by Example: How Successful Teams Deliver the Right Software. In fact you, the code craftsman, and your testers should also read it – it could potentially help all three disciplines to work better together.

For true continuous delivery to work, developers must accept to be part of the full process, so a developer must accept to also partly work with operations. Read The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win and accept that we are no longer mere developers – from now on we are DevOps.

This was a long introduction. In my next post I will go deeper into how to craft code in the world of continuous delivery. I hope that by now you agree that successful continuous delivery requires that we think differently about the entire software development process. After my next few posts I hope you agree that we also need to think about code structure differently.

Then again, maybe you already do. After all, the coding practices I am going to describe are all based on existing knowledge and generally accepted practices, so maybe you do it all already.