If you've been in this business long enough, sooner or later you're going to be faced with a terrible problem: fixing the legacy codebase. What follows isn't the only way to proceed, but it's a tried-and-true strategy that unfortunately doesn't appear to be well-known. The core of what follows is risk minimization. Assuming you're facing the problem of fixing a legacy application, you already have risk and you don't need more added to it. What follows is lower risk and lower cost than rewriting the system from scratch.
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Before we start, there are a few things you should know. First, read this now-famous Joel Spolsky article about why you should never rewrite your code (trust me, read it, but don't forget to come back). In that article, Spolsky makes a strong case about why you should refactor your codebase instead of rewriting it. Refactoring, if you're not familiar with the term, is the process of making a series of gradual improvements to code quality without changing the behavior. When you're trying to fix code, trying to change its structure and behavior at the same time is begging for trouble.
That being said, I don't believe in the word "never". If your code is written in UniBasic, rewriting might be your only option since you can't find developers who know the language (or are willing to learn it). Heck, I used to program in UniBasic and I've forgotten the language entirely.
Or if you're working with a relatively small piece of software with low impact, rewriting may not be that dangerous.
But let's say you can find or train developers for the language your software is written in, the software is mission-critical, and it's a very large codebase. Rewriting begins to make much less sense. Refactoring it means that you always have working code, you're not throwing away business knowledge or obscure bug-fixes, and your developers aren't starting from scratch, hoping they can make something work. In other words, you're minimizing your risk.
That being said, many companies (and developers) still opt for the rewrite. New code is exciting. New code promises new opportunities. New code is fun but fixing old code is often seen as drudgery. However, if you have a large, legacy codebase, the new code you're writing is, by definition, a large project and large projects are very high risk (emphasis mine):
In a landmark 1995 study, the Standish Group established that only about 17% of IT projects could be considered "fully successful," another 52% were "challenged" (they didn't meet budget, quality or time goals) and 30% were "impaired or failed." In a recent update of that study conducted for ComputerWorld, Standish examined 3,555 IT projects between 2003 and 2012 that had labor costs of at least $10 million and found that only 6.4% of [IT projects] were successful.
That's an old study, but there's still plenty of newer work which bears this out. The larger the project, the larger the risk. In fact, of the large projects I have been involved with for various companies, few were both on time and on budget. Some were cancelled outright and still others dragged on, long after it was clear they were a disaster, simply because no one wanted to take the blame for failure. One of them today is approaching its fourth year of a one-year schedule and is riddled with bugs and design flaws, but the company made the new software backwards-incompatible, switched over their clients and now have no way out. The only reason the company is still in business is that they bought another company that is very profitable and is paying for the company's mistake.
That last examples alludes to a dirty little secret that's often not talked about in our industry: large-scale rewrites often exchange one pile of spaghetti code for another. Rather than truly solve the underlying problems, the companies have traded a known set of problems for an unknown set of problems. If you need to fix your legacy code it's because you need to minimize your risk; why on earth would you knowingly adopt unquantifiable risk?
Assuming you've decided that you don't want to face the cost and risk of a large-scale rewrite, how do you refactor your code?
First, you need to assess where you are. At a bare minimum:
All of these things need to be written down so that anyone can consult this information at a glance. This information represents the bare necessities for the expert you're going to hire to fix the mess. If the above list seems simplistic, that's because we're refactoring, not rewriting.
And yes, you're probably going to hire an outside expert. Not only will they see things that you didn't, but while your current developers may be good, if they can't clearly lay down a solid plan to fix the legacy codebase while simultaneously minimizing risk, you need to bring in someone with experience with this area. What follows is not always intuitive and the expert's experience will help you navigate the rough waters you're already in. At a minimum, your expert needs to have the following:
The last points seems strange, but hard choices will need to be made and there will be strong disagreements about how to make them. It's hard to find this mix in top-notch developers, but it will definitely pay off.
The first thing you'll want to do is get a rough idea of how you want your new application laid out. Call this your architecture roadmap, but keep in mind that your landscape will change over time and this roadmap should be flexible. This is where your expert's architecture skills will come in. Various functional parts of your application will be decoupled and put into separate areas to ensure that each part of your application has a "specialty" that it focuses on. When each part of your application has one area it focuses on, it's easier to maintain, extend, and reuse, and that's primarily why we want to fix our legacy codebase. However, don't make detailed plans at this time; no battle plan survives first contact with the enemy. Instead, just ensure that you have a rough sense of where you're going to go.
Next, you're going to refactor your application the same way you eat an elephant: one bite (byte?) at a time. You'll pick a small initial target to get familiar with your new tools. Over time, it will get easier, but you don't want to bite off too big a chunk when you get started.
Refactoring a large application means writing tests, but unless you know what you're doing, you're probably going to get it wrong. There's often little TDD here — the code is already written — and you can't write tests for everything — you'll never finish. Instead, you'll be tactically applying integration tests piece by piece.
The first thing you need to do is understand what won't change in your application. By "won't change" I mean whatever it is that uses your application's output, whether it be through a JSON API, a Web site, a SOAP interface or what have you. Since something has to use the software, that something is what is going to make everything work. You're going to be writing integration tests against whatever that something is. For the sake of argument, we'll assume we're refactoring a Web application. You've decided that you'll start by writing tests to verify that you can list users on your admin page.
Inside those tests, you'll create a browser object, log in as an admin user, fetch the users page and write tests to assert that the expected users show up on that page. Just getting to this point can often take a huge amount of work. For example, how do you get code to connect to a test database? How do you ensure data isolation between tests (in other words, the order in which tests are run should not matter)? Heck, how do you create that browser object (hint: Selenium is a good choice here)? These and many more questions need to be answered when you're first starting out.
Getting to to this point may be easy if you already have some tests, or it may be very hard if you don't, but it's the important first step in the refactoring.
Once you have that first integration test targeting a small and (relatively) unchanging part of your interface, run your code coverage tools over the test(s) to see what code is covered with these high-level integration tests. Code which is covered is code which is generally safe to refactor (there are plenty of exceptions, but that's another article entirely).
Now you can start looking at which functional parts of the application are embedded in that tested code and make a plan for moving those sections into your architecture roadmap. At this point, it's tempting to rip everything apart, but don't give in to that temptation. Instead, focus on one piece at a time. For example, if you have SQL scattered throughout the code, start pulling that out into your architecture roadmap so that you have a clean API to work with the data you need. Or perhaps you have a Web application and you have been printing the HTML directly: look at using a templating system and start pulling the HTML out into templates. Don't fix everything at once or you'll be trying to do too much. Instead, focus on one area of responsibility and understand it well.
Note that we've been talking about integration testing but not unit testing. There's a very good reason for that: with heavy refactoring of a legacy system, your units will change quite heavily when you first start, but the integration tests focusing on the rather static interfaces will not. You want to spend time refactoring your application, not your tests, so until you've stabilized how the code works internally, unit tests can actually be a distraction. Integration testing has the advantage that you can cover (if not actually test) huge portions of your code at once and if done correctly, can be very fast to write. Further, with poorly structured applications, unit testing may be very difficult, if not impossible. Integration testing will also help uncover bugs that unit testing cannot: bugs where different components have different expectations when talking to one another. However, there are some downsides to integration testing:
That being said, the advantage of integration testing at this stage is clear: refactoring is much easier when you have some basic tests to protect against the worst errors. It's also worth keeping in mind that if you've done little to no testing before this, you're not significantly worse off if you have some solid tests than if you have none. Don't obsess too much on this point: you don't want perfect to be the enemy of the good.
If you haven't already implemented a continuous integration (CI) system, this is the time to start. Even if your developers forget to run the tests, your CI system shouldn't. You want to find out fast if tests are failing.
After you've started refactoring one functional piece of a small part of your system, you'll probably quickly uncover some bad assumptions made in the original plan. That's OK. You've started small to minimize your risk. Correct those bad assumptions and then start integration tests with code coverage for another small part of your system, pulling out the functional portions (database calls, HTML, or whatever) that you've already been working on. When you feel comfortable that you've shaken out some of the worst issues, start looking at another functional bit of the system that your currently tested code shares and see if you can pull that out.
Note that this is where your expert's architectural skills are going to shine. They'll understand the importance of decoupling different functional portions of the application. They'll understand how to write robust, flexible interfaces. They'll learn to recognize patterns in your business logic which can be abstracted out. Do not hand this responsibility over to an existing programmer unless you are absolutely confident they have the skills and experience necessary to get this done.
At this point, what follows is a wash/rinse/repeat cycle which in worst case scenarios can take years to finish. It takes a long time, but it has some significant advantages:
Why does this approach work? Any large project can seem daunting, but by breaking it down into smaller, manageable pieces, you can at least know where to start and get a sense of where you are going without the nail-biting worry about whether or not a huge project is going to fail.
When I've used this technique before, I've often found that it's a pleasure to finally have a cleaner sense of how the code is evolving and the current experienced team doesn't face the demoralizing prospect of watching their jobs disappear. The downside of this technique is that while code quality improves tremendously, there's always a feeling that it's not good enough. However, as I previously alluded to, many rewritten systems merely create new design flaws to replace old ones. This is far too common of a problem and it means swapping known problems for unknown ones.
The above strategy isn't appealing to many people and it can be a hard sell to those who are convinced that newer is better. In fact, in many respects it can be viewed as boring (though I love refactoring code), but I've successfully used this approach on multiple legacy codebases. However, if you're still trying to decide between a rewrite and a refactor, keep in mind that this approach is a relatively low-cost, low-risk approach. If it proves unworkable, you've likely risked very little. If the rewrite proves unworkable, you could cost the company a ton of money.
So the final question to ask yourself is when you should consider fixing your legacy codebase. The only advice I can offer is to suggest that you not wait until the storm before you fix your roof. Fixing a legacy code base isn't rocket science, but it does require a degree of expert knowledge in how to transform an existing codebase. Sadly, it's not a skill most developers seem interested in acquiring, but then, most don't seem interested in working on legacy codebases in the first place.
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