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.computer science computer science computer science computer science computer science computer science computer science computer science computer science
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.
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).
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.