As I noted in the previous chapter, in the 2016 version of Programming in Scala, Martin Odersky’s biography states, “He works on programming languages and systems, more specifically on the topic of how to combine object-oriented and functional programming.” Trying to merge the two styles appears to be an important goal for Mr. Odersky. Because I cover this topic in the next chapter, I won’t write any more about it here. An author can get himself into trouble for stating that one programming paradigm can use more memory or be slower than other approaches, so let me begin this section by being very clear: When you first write a simple (“naive”) FP algorithm, it is possible — just possible — that the immutable values and data-copying I mentioned earlier can be a performance problem. A methodology like Extreme Programming also emphasizes spending a lot of time together, but instead of saying that you should spend that time in meetings to measure progress, it advocates spending that time getting real work done, pair programming, creating business assets, learning from each other’s technical knowledge, leveling up together, refactoring, writing tests, etc. Many developers don't realize it, but it was possible to write functional programs in Java before Java 8. Terms like “pure functions” and “referential transparency” can sound like Greek to those who are new to functional programming, or even to functional veterans who haven’t learned all of the terms. At first I thought I was confused about this — that I didn’t understand something — and then I read this quote from Martin Odersky on scala-lang.org: “The IO monad does not make a function pure. You might already be writing some functional code without realizing it! For many people, recursion doesn’t feel natural. Declarative programming languages describe what computation should be performed and not how to compute it. But as it turns out, it’s also crucial to writing a useful application. Then you could have a separate thread/process/computer handle calling reduce across the results, or even set up multiple threads/processes/computers with each working on a section of the reduction. That is, until I became serious about writing pure FP code. So this really is an “update” method, as opposed to a “completely redraw the screen” method. As implementation techniques have improved, this is an undeserved criticism. The functional programming paradigm is investigated here through the actual implementation of a series of common algorithms. Programming language design and implementation, type theory, module systems, program transformation and analysis, functional programming. Abstraction is the greatest strength of functional programming. I'm not looking for some particular language advocate's uninformed criticisms. Aspect-oriented programming is con-cerned with adding functionality to a program that is orthogonal to the program’s functional decomposition [1,5]. Functional programming languages got dismissed in their early years due to inefficient implementation. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Genetic Programming (GP) automatically generates computer programs to solve specified problems. Declarative languages. It makes web pages functional for specific purposes and if … In FP you constantly create intermediate variables like name1 and name2 during the “update as you copy” process, but there are FP techniques that make those intermediate variables transparent. I find it easier to complete each of these actions in series, instead of in parallel. All However in Functional Programming (FP), you probably ran across something mysterious called a “monad” that helps wire functions together. For instance, when I first started working with Scala and needed a way to stub out new SBT projects, I wrote a Unix shell script. It is like a function between categories. 2. Now that you’ve seen some of the drawbacks of FP, I think I can provide a better answer. Functional programming is famous for its high-level abstractions that hide a large number of details of such routine operations like iterating. Any function that uses “hidden” variables, Using an “observable,” it receives a stream of. If you correctly isolate it, you can compose it with functional code and treat the results of the IO as a value. Expanding on what I mentioned just a moment ago, representing state in functional programming isn’t as intuitive as most other functional operations. You don’t mutate existing objects. But once you understand Scala’s collections’ methods, you know that the map method is a replacement for those algorithms: Once you’re comfortable with the collections’ methods, you’ll find that you reach for them before you reach for recursion. Functional programming is based on mathematical functions. Functional programming is an active area of research in the field of programming language theory. The Resurgence of Functional Programming track at QCon Plus featured several experts describing how functional programming makes developing software … In summary, potential drawbacks of functional programming in general are: Potential drawbacks of *functional programming in Scala” are: Having covered the benefits and drawbacks of functional programming, in the next chapter I want to help “free your mind,” as Morpheus might say. LITERARY CRITICISM AND THEORY Page 8 3. For starters, FP is intimately tied to a single programming language yet complex tasks often need > 1 language. 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