You’ve pushed the code through all of the tests and they came back green. The continuous integration pipeline ran all of the way through. All of the check boxes on the feature list are checked. All of the Post-it notes have moved to the completed part of the wall. Phew.
It’s tempting to call the code finished and head off on vacation. You’ve earned it. The team has earned it. Let the code do its thing for a bit. Isn’t that the whole reason we wrote it? To throw it over the wall where it could just hum along and do its thing?
Alas, the days of being complacent and sitting still are over. Nothing is ever finished these days. Just because you’ve squeezed out the bugs and delivered a working program doesn’t mean that you should relax. There are dozens of things you can still do to improve your code. Some are the mark of the good citizen who cleans up for the next team to come along. Some are opportunities for growth and capturing new markets. Some are the start of a new journey.
Here are 16 things to do when you come back from a bit of relaxation and recovery.
The tool called a lint or a linter is like a code review robot that enforces hundreds of semantic rules. Perhaps thousands of them. Some were written by obsessive scolds from the programming sect that counts blank characters and berates those who use too many or too few. Some were written by serious people who have flagged subtle semantic patterns that can lead to security flaws later. Your programming team has probably chosen a collection of linters and now is the time to run them.
Don Knuth once said, “Premature optimization is the root of all evil,” because it’s silly to spend time improving the parts of the code that only run occasionally. Now that you’re finished coding, it’s time to fire up a profiler and look for those hot spots. It’s often the case that 10 percent of the code runs 90 percent of the time. Sometimes there are tight inner loops that absorb 99 percent of the cycles. If you can flag them now, a few tweaks can really pay off.
Remove debugging tools
It’s tempting to leave in the verbose logging options embedded in the production code just in case, but once the code is running it’s a good time to clean out those tools and turn off the debugging options. The extra data clutters up the computer and may even threaten performance by filling up some disk drive. Leave the debugging out of the production servers.
Analyze with AI
Old programmers used basic regular expressions and statements to look for issues; modern programmers also have artificial intelligence tools. Amazon’s CodeGuru, for instance, is said to “leverage machine learning models” to search out bad code. It’s a fully automated process built upon profiling and tight analysis.
When you’re building an application, it’s easy to take databases and log files for granted. Now that you’re done with the app, it’s time to start optimizing the database for speed and stability. Add indices to the right columns to speed lookup. Add mirroring and timely backup to improve stability after power losses or a disk crash.
Now is the time to start weighing the costs of storage versus the costs of a data loss. Just how valuable are the log files? How much do they cost to maintain? How much does a geographically diverse backup plan cost versus how likely is a catastrophic failure of a data center? These aren’t easy questions to answer but once you understand the cost of backup you can decide on how much you want to gamble. It’s like a paid trip to Las Vegas, only you’re rolling the dice with your career and the jobs of everyone else around you.
Optimize data flow
Many applications can benefit from fast caches, either on the server or spread out across the internet with a content delivery network. Adding a distributed memory cache or integrating a CDN is one of the simplest way to boost the performance experienced by users.
Not all of the data needs to be as big as it is. Images are one of the simplest places to reduce size without trading off too much. Stylistic details like elaborate backgrounds can be replaced by CSS instructions for gradient fills that take a tiny fraction of the disk space and bandwidth. Photographers and artists often love to keep as much information and detail around in case they need it, storing images in RAW format. Tools like ImageOptim will strip away much of the unnecessary detail below the user’s perceptual threshold while also removing extra EXIF values that track extraneous information like the lens of the camera. The result is faster downloads and lower bandwidth bills.
Add an API
Many architects begin with a well-structured API for separating the front-end display code from the business logic underneath, but sometimes there’s a good opportunity to expand the use of the code base by adding another door or window. API toolkits like Swagger make this relatively easy by delivering the parsing, the routing, and even the documentation. If you have some functions that are good, clean entry points to your current block of code, gluing them to a new API can enable new options for automation and integration.
Hah! Documentation is less important today than it used to be, but it is still helpful in the right doses. If you’re writing well-structured code with clue-filled variable names and simple structure, the code won’t need many local comments. But it is still helpful to sketch out the basic role for each section and perhaps indicate how the data flows through the code. It is also helpful to point out some of the potential problems with the code and illustrate how the code recovers from exceptions—if it does.
Some clever programmer rebranded the idea of rewriting our code because, well, the word “rewriting” makes it sound like you made a mistake the first time. “Refactoring” has a better ring. Refactoring doesn’t admit prior mistakes and that makes it easier on the ego. The process of improving the code, often in little jumps, is a good idea to start just after you “finish.” Little improvements and fixes can be rolled into the code immediately.
Many teams are constantly refactoring, shipping, or deploying new versions daily or even hourly. On their own, these small changes seem immaterial, but over the weeks and months they add up to significant improvements. The iterations come so often that they start to blur the line between finishing your code and starting up again. It’s just one continuous cycle of improvement and deployment.