APL is a notorious language with a reputation for being "write only" and difficult to learn. Yet, many people consider APL to be their single most productive, advantageous tool that they hope never to give up. In particular, it is generally observed that many computer scientists have a poor opinion of the usability of APL, while many data scientists and domain experts find the language to be exceptionally usable.

This session focuses on the experienced programmer's struggle to gain real facility with APL beyond the basic, trivial understanding of its semantics and syntax (which can be taught in a few hours). We will observe this struggle through a "human-centered" experience analysis identifying a set of Pattern/Anti-pattern tensions that are at the heart of the intermediate APL learning "wall." By examining the experience of thinking and working with APL code versus regular code, we can identify principles of APL coding practice that directly oppose the widely taught and embraced best practices of the broader programming community. We'll see not only what these principles are, but also why they survive in APL and how they contribute to the positive development cycle of an experienced APL programmer, instead of the negative impact such practices usually have when writing in other languages.

Understanding these practices will provide a focus point for the discussion of programming experience design and the use of concise array language, as well as provide a structure for becoming better able to write, read, and think in APL expert.

In Indian classical music, we have Jugalbandi, where two lead musicians or vocalist engage in a playful competition. There is jugalbandi between Flutist and a Percussionist (say using Tabla as the instrument). Compositions rendered by flutist will be heard by the percussionist and will replay the same notes, but now on Tabla and vice-versa is also possible.

In a similar way, we will perform Code Jugalbandi to see how the solution looks using different programming languages and paradigms.This time the focus of code jugalbandi will be on solutioning in different paradigms - object-oriented or functional programming and array-oriented paradigms.

During the session, Morten and Dhaval will take turns at coding the same problem using different languages and paradigms. There would be multiple such rounds during the Jugalbandi.

John Hughes and Mary Sheeran in Why Functional Programming Matters [0] list “algebra as a litmus test” as one of the four salient features of functional programming, and Ken Iverson in Notation as a Tool of Thought [1] lists “amenability to formal proof” as one of the five important characteristics of notation. Using the language APL, we prove the correctness of some programs, derive simplifications, and illustrate design validation and program test techniques for functional programs.

[0] Hughes, John, and Mary Sheeran, Why Functional Programming Matters, Code Mesh, London, 2015-11-02 to -04.

[1] Iverson, Kenneth E., Notation as a Tool of Thought, Communications of the ACM, volume 23, number 8, 1980-08.

Using a programming language that supports first class functional programming doesn’t necessarily imply that the code you write is functional. Functional programming has the nice properties of being compositional - you design smaller abstractions and then reuse them to build larger ones. You use the power of algebraic properties that these abstractions offer to build larger elements of your model out of smaller ones.

However often we find that many code bases designed in a functional programming language are inherently imperative, especially when you are using a multi-paradigm language like Scala or Java. There’s nothing wrong with imperative code, but it lacks the niceties of expression based composition that makes reasoning about your code so much easier. Also assignments used in imperative code are inherent side-effects that are best avoided (or at least minimized) in an expression oriented language.

In this session I will discuss some of my experiences of mining imperative code and refactoring them into functional patterns. You will be surprised to see how much functional goodness can be derived through functional thinking. Algebraic structures like Monoids, Monads and Kleislis are just buried within the reams of imperative statements crying to come out. In an exercise of half an hour we will liberate them and see how the code quality improves from the point of view of modularity, compositionality and simplicity.

The slides of the talk has been uploaded at https://www.slideshare.net/debasishg/mining-functional-patterns.