Refactor Your Codebase as You Go, or Lose it to Early Death

Also, Scrub Your Teeth Twice a Day

Refactoring is badly misunderstood by many software professionals, and that misunderstanding causes software teams of all kinds – traditional and agile – to forgo refactoring, which in turn dooms them to waste millions of dollars. This is because failure to refactor software systems continuously as they evolve really is tantamount to a death-sentence for them.

To fail to refactor is to unwittingly allow a system to decay, and unchecked, nearly all non-trivial systems decay to the point where they are no longer extensible or maintainable. This has forced thousands of organizations over the decades to attempt to rewrite their business-critical software systems from scratch.

These rewrites, which have their own chronicles of enormous expense and grave peril, are completely avoidable. Using good automated testing and refactoring practices, it is possible to keep codebases extensible enough throughout their useful lifespans that such complete rewrites are never necessary. But such practices take discipline and skill. And acquiring that discipline and skill requires a strategy, commitment, and courage.

So, First of all: Refactoring – What is It?

The original meaning of the word has been polluted and diluted. Here are some of the “refactoring” definitions floating around:

• Some view it as “gold-plating” – work that adds no business value, and merely serves to stroke the egos of perfectionists who are out of touch with business reality.
• Some view it as “rework” – rewriting things that could, and should, have been written properly in the first place.
• Others look at refactoring as miscellaneous code tidying of the kind that is “nice to have,” but should only happen when the team has some slack-time, and is a luxury we can do without, without any serious consequences. This view would compare refactoring to the kind of endless fire-truck-polishing and pushups that firemen do between fires. Busy work, in other words.
• Still others look at refactoring as a vital, precise way of looking at the daily business of code cleanup, code maintenance, and code extension. They would say that refactoring is something that must be done continuously, to avoid disaster.

Of course, not all of these definitions can be right.

The original, and proper, definition of refactoring is that last one. Here I attempt to explain and justify that. But first let’s talk about where refactoring came from as a practice.

What problem does refactoring try to solve?

The Problem: “Code Debt” and the “Cost of Decay” Curve

What is Code Debt?

Warning: Mixed Metaphors Ahead

Veteran programmers will tell you that from day one, every system is trying to run off the rails, to become a monstrous, tangled behemoth that is increasingly difficult to maintain. Though it can be difficult to accept this unless you have seen it repeatedly firsthand, it is in fact true. No matter how thoughtfully we design up front and try to get it entirely right the first time, no matter how carefully we write tests to protect us as we go, no matter how carefully we try to embrace Simple Design, we inevitably create little messes at the end of each hour, or each day, or each week. There is simply no way to anticipate all the little changes, course corrections, and design experiments that complex systems will undergo in any period.

So enough of dental metaphors for a moment. Software decay is like the sawdust that accumulates in a cabinetmaker’s shop, or the dirty dishes and pots that pile up in a commercial kitchen – such accumulating mess is a kind of opportunity cost. It always happens, and it must be accounted for, planned for, and dealt with, in order to avoid disaster.

Programmers increasingly talk about these little software messes as “code debt” (also called “technical debt“) – debt that must be noted, entered into some kind of local ledger, and eventually paid down, because these little messes, if left unchecked, compound and grow out of control, much like real financial debt.

The Software “Cost of Decay” Curve

Years ago it was discovered that the cost of correcting a defect in software increases exponentially over time. Multiple articles, studies, and white papers have documented this “Cost of Change Curve” since the 1970′s. This curve describes how the cost of change tends to increase as we proceed from one waterfall phase to another. In other words, correcting a problem is cheapest in requirements, more expensive in design, yet more expensive in “coding,” yet more costly in testing, yet more costly in integration and deployment. Scott Ambler discusses this from an agile perspective here, talking about how some claim that agile methods generally flatten this curve. Ron Jeffries contends, alternately, that healthy agile methods like XP don’t flatten this curve, but merely insist on correcting problems at the earliest, cheapest part of it. I agree with Ron, but I claim that’s only part of how agility (and refactoring in particular) helps us with software cost of change.

There is a different (but related) exponential curve I dub the “cost of decay curve.” This curve describes the increasing cost of making any sort of change to the code itself, in any development phase, as the codebase grows more complex and less healthy. As it decays, in other words.

Whether you are adding new functionality, or fixing bugs, or optimizing performance, or whatever, the cost of making changes to your system starts out cheap in release 1, and tends to grow along a scary curve during future releases, if decay goes unrepaired. In release 10, any change you plan to make to your BigBallofMud system is more expensive than it was in release 1. In the graph-like image below, the red line shows how the cost of adding a feature to a system grows from release to release as its decay grows.

The number of releases shown here is arbitrary and illustrative — your mileage will vary. Once more, I am not talking about how, within a project, the cost of detecting and fixing a problem increases inevitably over time, as the Cost of Change curve does. I am saying that we can use the cost of any sort of change (like adding a new feature) to measure how much our increasing decay is costing us. I am using the cost of a change to measure increasing cost of decay.

Back to the dental metaphor. If, in the last few minutes of programming, I just created a tiny inevitable mess by writing 20 lines of code to get a test to pass, and if that mess will inevitably ramify and compound if left uncorrected (as is usually true), then from the organization’s perspective, the cheapest time for the organization to pay me to clean up that mess is immediately – the moment after I created it. I have reduced future change costs by removing the decay. I have scrubbed my teeth, removing the little vermin that tend to eat, multiply, defecate, and die there (I never promised a pleasant return to the metaphor — teeth are, let’s face it, gross).

Again, if a day’s worth of programming, or a week’s worth of programming, caused uncorrected, unrefactored messes to accumulate, the same logic is imposed upon us by the cost of decay curve. The sooner we deal with the messes, the lower the cost of that cleaning effort. It’s really no different than any other “pay a bit now or pay a lot later” practice from our work lives or personal lives. We really ought to scrub our teeth.

Little software messes really are as inevitable as morning breath, from a programmer’s perspective. And nearly all little software messes do ramify, compound, and grow out of control, as the system continues to grow and change. Our need to clean up the mess never vanishes – it just grows larger and larger the longer we put it off, continuously slowing us down and costing us money. But before we talk about how these little messes grow huge, helping to give that cost of decay curve it’s dramatic shape, let’s talk about the worst-case scenario: the BigBallOfMud, and the Complete System Rewrite.

Worst-Case Scenario: The BigBallOfMud, and the Complete Rewrite

Most veteran programmers, whether working in procedural or object oriented languages, have encountered the so-called BigBallOfMud pattern. The characteristics of this pattern are what make the worst legacy code so difficult or impossible to work with. These are codebases in which decay has made the cost of any change very expensive. At one shop at which I once consulted, morale was very low. Everybody seemed to be in the debugger all the time, wrestling with the local legacy BigBallOfMud. When I asked one of them how low morale had sunk, he said something like “You would need to dig a trench to find it.”

With a bad enough BigBallOfMud, the cost of the decay can be so high that the cost of adding the next handful of features is roughly the same as the cost of rewriting the system from scratch. This is a dreadfully expensive and dangerous outcome for any codebase that still retains significant business value. Total system rewrites often blow budgets, teams and careers – unplanned-for resources must be found somewhere for such huge and risky efforts. Below we revisit the cost of decay curve, adding in a blue line showing how we strive to increase our development capacity from release to release. At best, we can achieve this growth linearly, not exponentially.

At the point where the two lines cross, we have our BigBallOfMud. We are out of luck for this particular system – it is no longer possible to add enough resources to maintain or extend it, nor shall it ever be again. Indeed, the cost of decay, and the cost of making any sort of change, can only continue to increase from there, until it becomes essentially infinite – change cannot be made safely or quickly enough at all.

We are then faced with a total system rewrite, because we have lost all of our refactoring opportunity, along with our ability to make any other forms of change. How many expensive, perilous total system rewrites have you seen or taken part in, in your career? How many “legacy codebases” do you know of that just could not be maintained any longer, and which had to be replaced, at great expense, by a rewrite, perhaps in a new technology or with a new approach, perhaps by a completely new team? I have personally seen several over the years. They have not all gone well.

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