java programming springboot

Refactoring and microservices

In recent cloud projects, I keep seeing the same Spring application anti-pattern. There are controllers for a number of REST endpoints. Each REST endpoint calls a separate class, which carries out the business logic for that action. The problem is that such classes can easily grow to a thousand lines or more, and I’ve often seen single methods over a hundred lines long – an anti-patten sometimes referred to as ‘god classes’. Code is sometimes extracted to private methods within these classes, which can obscure that there is a single execution flow hundreds of lines long. The addition of unit testing means that long, repetitive tests with complicated set-up are needed to provide coverage for branches deep within these classes. These complicated tests then make it difficult to refactor the code.

This problem comes from applying sensible principles in the wrong way. We have the Controller logic separate from the Business logic, and the Model managed by Spring Data classes. It’s a rough MVC pattern – and Spring makes this separation very easy. The problem is that the Controller logic is usually trivial, just an annotation that might as well have been put on the Service class. It’s this Service class that you really want to be split out into smaller classes.

One of the promises of microservices is that they should be nimble, something that can be quickly built and replaced. But such large classes produce microservices which are, basically, tiny monoliths. The complex tests act as a drag on refactoring, making the services little tangles of legacy code.

The Single Responsibility Principle is the sort of thing that comes up in interviews as one of the SOLID Principles, and I’ve never heard anyone argue that it’s a bad thing. Which makes it all the stranger that it does not seem to be applied in practise. Everyone seems to agree that god classes are a bad thing,

One answer here, which I’ve proposed before is to use TDD properly. This is the ideal way to solve the problem, preventing it from happening by applying best practise. In his recent book on Software Engineering, Dave Farley suggests that proper use of TDD avoids this sort of coupled code:

The strongest argument against TDD that I sometimes hear is that it compromises the quality of design and limits our ability to change code, because the tests are coupled to the code. I have simply never seen this in a codebase created with “test-first TDD.” It is common—I’d say inevitable—as a result of “test-after unit testing,” though. So my suspicion is that when people say “TDD doesn’t work,”  what they really mean is that they haven’t really tried TDD, and while I am sure that this is probably not true in all cases, I am equally certain that it is true in the majority and so a good approximation for truth.

The other potential solution is to enforce good class design with method size limits in quality-checking tools such as sonar. This restricts developer autonomy in an unpleasant manner, although this is better than the alternative of unmaintainable code. Farley suggests using tools to reject any method of more than a certain number of lines and parameters. He writes:

I will establish a check in the continuous delivery deployment pipeline, in the “commit stage,” that does exactly this kind of test and rejects any commit that contains a method longer that 20 or 30 lines of code. I also reject method signatures with more than five or six parameters. These are arbitrary values, based on my experience and preferences with the teams that I have worked on.

There are actually good arguments for this in that, as Farley points out, “Most optimizers in compilers simply give up trying once the cyclomatic complexity of a block of code exceeds some threshold”. But the most important thing here is that such limits force people out of writing procedural, linear code to produce business actions, and decompose these into single-responsibility classes. There are ways to write poor code within these constraints, but it’s not so easy to do.

java testing

Mutation Testing can help write better unit tests

I was introduced to mutation testing in my last job and I am very excited about its potential. Mutation testing evaluates how good a set of unit tests are. We used pitest and, applying it against an existing project, discovered a number of tests were not working as they should have been, despite providing code coverage. We also found a couple of minor bugs.

Mutation testing works by changing the bytecode for a piece of software then running the tests against this changed code. In theory, one of the tests providing coverage for that line ought to fail if the line changes. If this is not the case, then the code coverage is not actually asserting anything about that piece of code. A good introduction is a video by pitest’s creator, Henry Coles, Testing Like It’s 1971. (The title refers to the fact that mutation testing was invented in the 1970s but is only now achieving its potential).

I’d expected mutation testing to be painfully slow, but pitest can work through large code-bases surprisingly quickly. In smaller experiments, I found I could use pitest as part of the TDD cycle with little pain.

Working with mutation testing forces code coverage to be very high. It’s easy to exclude certain external calls, but all the other code within a project will need to be both covered and asserted. For some legacy codebases, adding such high coverage is going to be difficult. High coverage without TDD often produces brittle codebases that are hard to refactor, and adding tests retrospectively to these is expensive. Rather than using mutation testing for such codebases, it is probably more important to look initially at breaking down the code from large business logic classes (sometimes known as God classes) into smaller classes using the single responsibility principle.

But that’s another story. Whatever your situation, it’s worth looking into mutation testing, and thinking about how you can introduce it into your software build process.


Why Java Still Matters

One of the last things I did before finishing at Mindera was to write a blog post, Why Java Still Matters. This piece begins by looking at the history of Java, particularly the wilderness years, which I’ve previously written about in my post on Bruce A Tate’s Beyond Java.

The Mindera piece goes on to argue that Java’s lack of sophistication, often seen as a weakness, is actually a strength. For me, Java is a more robust language than many of the alternatives – although new features are diluting this.

Java is now over a quarter of a century old. It emerged on a wave of hype in 1996, promising to be a programming language for the Internet. But, unfortunately, it very soon came to feel awkward and was mocked as a boring, corporate language. Ten years later, people were writing book-length obituaries for Java, suggesting that developers move on.

You can read the full post on the Mindera blog.

One thing I couldn’t quite squeeze into the post was a discussion of how applets were withdrawn. I’d have loved to add a link to Simon Ritter’s post No Longer the Applet of the Developer’s Eye, where he tries to run a 1996 demo in Java 8.