Faster, smarter programming, with fewer bugs. Those are the promises coming from the creators of the latest round of languages to capture the attention of programmers. Yes, they're the same buzzwords we've heard before, but the lack of novelty is no reason to dismiss them. The future of coding requires stability and good practices so our innovations will work. In fact, our projects are often so much bigger now, we need the innovation more than ever.
If there's a common theme among the languages I describe below, it's that increasing automation can yield code worthy of the terms "faster, smarter, and bug-free." The newer approaches include more structure and more abstraction, allowing the guts of the languages to do what programmers used to have to do themselves. These automated features give the programmer more leverage to concentrate on the big issues. In many cases, they also produce better performance because the automated mechanisms are better able to find opportunities for efficiency and parallel computation while eliminating some of the simple mistakes that lead to errors.
But beyond this one overarching theme, there's little agreement. One of the languages is built for statistical analysis. Several are meant to modernize classic languages. Some aren't even languages at all—they're merely preprocessors. Still, all of them are changing how we're writing code today and laying the foundation for the future of coding.
Here are 13 languages that are changing how we tell computers what to do. Some of these languages are new, some are already very popular, and some aren't actually languages.
1. R
At heart, R is a programming language, but it's more of a standard bearer for the world's current obsession with using statistics to unlock patterns in large blocks of data. R was designed by statisticians and scientists to make their work easier. It comes with most standard functions used in data analysis and many of the most useful statistical algorithms are already implemented as freely distributed libraries. It's got most of what data scientists need to do data-driven science.
Many people end up using R inside an IDE as a high-powered scratchpad for playing with data. R studio and R commander are two popular front ends that let you load up your data and play with it. They make it less of a compile-and-run language and more of an interactive world in which to do your work.
Highlights: Clever expressions for selecting a subset of the data and analyzing it
Headaches: Aimed at desktops, not the world of big data where technologies like Hadoop rule.
2. Java 8
Java isn't a new language. It's often everyone's first language, thanks to its role as the lingua franca for AP Computer Science. There are billions of JAR files floating around running the world.
But Java 8 is a bit different. It comes with new features aimed at offering functional techniques that can unlock the parallelism in your code. You don't have to use them. You could stick with all the old Java because it still works. But if you don't use it, you'll be missing the chance to offer the Java virtual machine (JVM) even more structure for optimizing the execution. You'll miss the chance to think functionally and write cleaner, faster, and less buggy code.
Highlights: Lambda expressions and concurrent code
Headaches: A bolted-on feeling makes us want to jump in with both feet and use Scala (see below).
3. Swift
Apple saw an opportunity when programming newbies complained about the endless mess of writing in Objective C. So they introduced Swift and strongly implied that it would replace Objective C for writing for the Mac or the iPhone. They recognized that creating header files and juggling pointers was antiquated. Swift hides this information, making it much more like writing in a modern language like Java or Python. Finally, the language is doing all the scut work, just like the modern code.
The language specification is broad. It's not just a syntactic cleanup of Objective C. There are plenty of new features, so many that they're hard to list. Some coders might even complain that there's too much to learn, and Swift will make life more complicated for teams who need to read each other's code. But let's not focus too much on that. iPhone coders can now spin out code as quickly as others. They can work with a cleaner syntax and let the language do the busy work.
Highlights: Dramatically cleaner syntax and less low-level juggling of pointers
Headaches: The backward compatibility requires thinking about bits and bytes occasionally.
4. Go
When Google set out to build a new language to power its server farms, it decided to build something simple by throwing out many of the more clever ideas often found in other languages. They wanted to keep everything, as one creator said, "simple enough to hold in one programmer's head." There are no complex abstractions or clever metaprogramming in Go—just basic features specified in a straightforward syntax.
This can make things easier for everyone on a team because no one has to fret when someone else digs up a neat idea from the nether reaches of the language specification.
Highlights: Just a clean, simple language for manipulating data.
Headaches: Sometimes a clever feature is needed.
5. CoffeeScript
Somewhere along the line, some JavaScript programmers grew tired of typing all those semicolons and curly brackets. So they created CoffeeScript, a preprocessing tool that turns their syntactic shorthand back into regular JavaScript. It's not as much a language as a way to save time hitting all those semicolons and curly bracket keys.
Jokers may claim that CoffeeScript is little more than a way to rest your right hand's pinkie, but they're missing the point. Cleaner code is easier to read, and we all benefit when we can parse the code quickly in our brain. CoffeeScript makes it easier for everyone to understand the code, and that benefits everyone.
Highlights: Cleaner code
Headaches: Sometimes those brackets make it easier to understand deeply nested code.
6. D
For many programmers, there's nothing like the very clean, simple world of C. The syntax is minimal and the structure maps cleanly to the CPU. Some call it portable Assembly. Even for all these advantages, some C programmers feel like they're missing out on the advantages built into newer languages.
That's why D is being built. It's meant to update all the logical purity of C and C++ while adding in modern conveniences such as memory management, type inference, and bounds checking.
Highlights: Some of the most essential new features in languages.
Headaches: You trade some power away for the safety net.
7. Less.js
Just like CoffeeScript, Less.js is really just a preprocessor for your files, one that makes it easier to create elaborate CSS files. Anyone who has tried to build a list of layout rules for even the simplest website knows that creating basic CSS requires plenty of repetition; Less.js handles all this repetition with loops, variables, and other basic programming constructs. You can, for instance, create a variable to hold that shade of green used as both a background and a highlight color. If the boss wants to change it, you only need to update one spot.
There are more elaborate constructs such as mixins and nested rules that effectively create blocks of standard layout commands that can be included in any number of CSS classes. If someone decides that the bold typeface needs to go, you only need to fix it at the root and Less.js will push the new rule into all the other definitions.
Highlights: Simpler code
Headaches: A few good constructs leave you asking for more.
8. MATLAB
Once upon a time, MATLAB was a hardcore language for hardcore mathematicians and scientists who needed to juggle complex systems of equations and find solutions. It's still that, and more of today's projects need those complex skills. So MATLAB is finding its way into more applications as developers start pushing deeper into complex mathematical and statistical analysis. The core has been tested over the decades by mathematicians and now it's able to help mere mortals.
Highlights: Fast, stable, and solid algorithms for complex math
Headaches: The math is still complex.
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