How do you decide whether a programming language is worth using or not? By necessity, such decisions are usually based on assessments that can be made relatively quickly: the ease of using the language, how productive you feel in the first week, and so on. Unfortunately, this tells us very little about the costs involved in continuing to maintain a project past that initial phase. And in reality, the vast majority of time spent on most projects is spent in those later phases.

I'm going to claim, based on my own experience and analysis of language features, that functional programming in general, and Haskell in particular, are well suited for improving this long tail of projects. We need languages and programming techniques that allow broad codebase refactorings, significant requirements changes, improving performance in hotspots of the code, and reduced debug time. I believe Haskell checks these boxes.

We sorely missed the rapid "refresh-based" feedback loop available in Rails (and other dynamically typed web frameworks), while writing Haskell. Change your code, hit save, and refresh your browser!

In this talk we will share a few tips on how we finally hit productivity nirvana with ghcid and automated code-gen.

Best of both worlds -- rock-solid type-safety AND being able to reload code with every change.

Functional programming is built around a foundation of well-defined Types, and when you throw in Typeclasses into the mix, you get the love-child that is Algebraic Data Types. In this talk I aim to explore the mathematical foundations of Type theory and how it can be used practically in Scala for wide variety of applications like Machine Learning (Apache Spark MLlib), API design (using Vertx), DSLs and the like.

I will also be introducing the scalacheck library for Property-based testing and how you can quickly validate your ADT domains.

The talk will further deep-dive into how you can utilize the amazing Cats library and Shapeless to build generic libraries around your ADTs, having Circe as a case-study.

Domain Specific Languages (or Libraries because embedded DSLs are just that) are already quite popular.

Modern languages have many useful language features that are conducive to create DSLs with more ease than ever before. Kotlin from JetBrains is a beautifully blended pragmatic programming language that packages many features from many programming languages. Kotlin also have infix operation which makes code written in a DSL made with Kotlin very easy to read (and therefore less error-prone).

In this demonstration, I shall show couple DSLs made from Kotlin

and will dissect the code LIVE to show audience how several language features in Kotlin (which sometimes requires playing with higher order functions) to develop these languages.

*A Unit Testing DSL (a DSL to simplify unit testing of Kotlin, Java Code) that our grand/m/pa can use.

-- All unit test frameworks serve the purpose but elegance is a different matter. A code that works, and a code that is elegant and works is art. In this example, audience will see how they can use several language features that Kotlin has to offer can be put together to create an elegant and expandable unit testing DSL.

* A DSL for Web Scraping and Transformation.

- - A special case of ETL, where Extraction happens from raw HTML, Transformation happens in memory using the DSL designed. and the Load happens by loading this data to a different schema/db/form/representation.

Category Theory has been found to have a vast field of applications not limited to programming alone.

In this fun-filled talk (Yes! We promise!) , we want to make the audience fall in love with Math & Category Theory in general, and Haskell in particular.

We will address questions such as below:

• What is the mysterious link between the abstract mathematical field of Category Theory and the concrete world of real-world Programming ? And why is it relevant especially in Functional Programming?
• Most of all, how can You benefit knowing Category Theory ? (Examples in Haskell)

Haskell can and does perform as well as C, sometimes even better. However,
writing high performance software in Haskell is often challenging especially
because performance is sensitive to strictness, inlining and specialization.
This talk focuses on how to write high performance code using Haskell. It is
derived from practical experience writing high performance Haskell libraries. We
will go over some of the experiences from optimizing the "unicode-transforms"
library whose performance rivals the best C library for unicode normalization.
From more recent past, we will go over some learnings from optimizing and
benchmarking "streamly", a high performance concurrent streaming library. We
will discuss systematic approach towards performances improvement, pitfalls and
the tools of the trade.

In this talk, we define the principle of functional programming, then go into
detail about what becomes possible by following this principle. In particular,
parametricity (Wadler, 1989) and exploiting types in API design are an essential
property of productive software teams, especially teams composed of volunteers
as in open-source. This will be demonstrated.

Some of our most important programming tools are neglected, often argued away
under a false compromise. Why then, are functional programming and associated
consequences such as parametricity so casually disregarded? Are they truly so
unimportant? In this talk, these questions are answered thoroughly and without
compromise.

We will define the principle of functional programming, then go into
detail about common problems to all of software development. We will build the
case from ground up and finish with detailed practical demonstration of a
solution to these problems. The audience should expect to walk away with a
principled understanding and vocabulary of why functional programming and
associated techniques have become necessary to software development.

In this case study presentation, SumAll's CTO, Todd Sundsted, and Senior Software Engineer, Michael Ho, will discuss the move from Java to Haskell along two parallel paths. First, the business/political story — how SumAll convinced the decision makers, fought the nay-sayers, and generally managed the people impacted by the transition. Second, the technical story — how they actually replaced their Java code with Haskell code. Along the way, they will address their hopes and expectations from transitioning from Java to Haskell, and will conclude with the results they've gained and seen to date.

A virtual cottage industry has sprung up around Purely functional UI development, with many available libraries that are essentially just variants on two distinct approaches: Functional Reactive Programming (FRP), and some form of functional views like "The Elm Architecture". After having worked extensively with each of them, I have found that none of the approaches scale with program complexity. Either they are too difficult for beginners trying to build a hello world app, or they have unpredictable complexity curves with some simple refactorings becoming unmanageably complex, or they "tackle" the scaling problem by restricting developers to a safe subset of FP which becomes painful for experienced developers who start hitting the complexity ceiling.

In this talk I give an overview of the current Purely Functional UI Development Landscape, and then present "Concur", a rather unusual UI framework, that I built to address the shortcomings of the existing approaches. In particular, it completely separates monoidal composition in "space" (i.e. on the UI screen), from composition in "time" (i.e. state transitions), which leads to several benefits. It's also a general purpose approach, with Haskell and Purescript implementations available currently, and can be used to build user interfaces for the web or for native platforms.

The biggest advantage of Concur that has emerged is its consistent UI development experience that scales linearly with program complexity. Simple things are easy, complex things are just as complex as the problem itself, no more. Reusing existing widgets, and refactoring existing code is easy and predictable. This means that Concur is suitable for all levels of experience.

1. For Learners - Concur provides a consistent set of tools which can be combined in predictable ways to accomplish any level of functionality. Due to its extremely gentle learning curve, Concur is well suited for learners of functional programming (replacing console applications for learners).
2. For experienced folks - Assuming you are already familiar with functional programming, Concur will provide a satisfying development experience. Concur does not artificially constrain you in any form. You are encouraged to use your FP bag of tricks in predictable ways, and you are never going against the grain. It's a library in spirit, rather than a framework.

When we talk about domain models, we talk about entities that interact with each other to accomplish specific domain functionalities. We can model these behaviors using pure functions. Pure functions compose to build larger behaviors out of smaller ones. But unfortunately the real world is not so pure. We need to manage exceptions that may occur as part of the interactions, we may need to write stuff to the underlying repository (that may again fail), we may need to log audit trails and there can be many other instances where the domain behavior does not guarantee any purity whatsoever. The substitution model of functional programming fails under these conditions, which we call side-effects.

In this session we talk about how to manage such impure scenarios using the power of algebraic effects. We will see how we can achieve function composition even in the presence of effects and keep our model pure and referentially transparent. We will use Scala as the implementation language.

In discussing effects we will look at some patterns that will ensure a clean separation between the algebra of our interface and the implementation. This has the advantage that we can compose algebras incrementally to build richer functionalities without committing to specific implementations. This is the tagless final approach that offers modularity and extensibility in designing pure and effectful domain models.

Laws, laws, laws. It seems as though whenever we learn about a new abstraction in functional programming, we hear about its associated laws. Laws come up when we learn about type classes like Functors, Monoids, Monads, and more! Usually laws are mentioned and swiftly brushed past as we move on to examples and applications of whatever structure we're learning about. But not today.

In this talk, we'll learn about Functors and Monoids, paying close attention to their laws. Why should our abstractions have laws? We'll answer this question both by seeing powers we gain by having laws, and by seeing tragedies that can befall us without laws.

This full day workshop will focus on applying Haskell to normal, everyday programming. We'll be focusing on getting comfortable with common tasks, libraries, and paradigms, including:

• Understanding strictness, laziness, and evaluation
• Data structures
• Structuring applications
• Concurrency and mutability
• Library recommendations

By the end of the workshop, you should feel confident in working on production Haskell codebases. While we obviously cannot cover all topics in Haskell in one day, the goal is to empower attendees with sufficient knowledge to continue developing their Haskell skillset through writing real applications.

Time has a profound impact on the complexity of the systems we build.

A significant amount of this software complexity comes from either an inability to recall previous states or the inability to understand how a state was arrived at.

From the foundations of AI, LISP and functional programming [1], to causality in distributed systems [2], to the more grungy practices of immutable infrastructure, or the unreasonable effectiveness of fact-based approaches to large scale data systems; the ability to adequately cope with time, and the change and conflict it inevitably creates, is a common thread to being able to build and reason about these systems.

This talk looks at the impact of time on system design. We will walk through examples of large-scale systems and their battles with complexity. At the end of the talk, the audience should start to see the common spectre of time and have an appreciation of how understanding time is fundamental to maintaining clarity, correctness and reliability in systems.

[1] Situations, Actions, and Causal Laws
John McCarthy
http://www.dtic.mil/dtic/tr/fulltext/u2/785031.pdf

[2] Times, Clocks and the Ordering of Events in a Distributed System
Leslie Lamport
https://amturing.acm.org/p558-lamport.pdf

It's another attempt to explain Monads to all those who are curious of this M-word.

The famous Mars Rover problem is used to demonstrate a solution for it using basic Haskell tools and then introduces Monads and demonstrates a solution using the State Monad.

Following Tony and Alois' Introduction to Haskell syntax and tools, we will work through the first few modules of Data61's Functional Programming Course. These modules cover writing functions for the optional and list data types.

We will complete enough exercises to cover basic data types, functions and polymorphism. We'll practice the techniques of equational reasoning, parametricity and type/hole driven development. After completing these modules, you should be able to use the techniques to attempt most other exercises in the repository.

This workshop has the same requirements as Tony's introduction, along with a download of a recent version of the fp-course repository (https://github.com/data61/fp-course).

Testing is one of the most fundamental aspects of being a software developer. There are several movements and communities based on different methodologies with regards to testing such as TDD, BDD or design by contract. However, in the FP community testing is often not a large topic and is often glossed over. While it’s true that testing in functional programming tends to be less important, there should still be more resources on how to create tests that add actual value.

This talks aims to provide exactly that, with good examples on how to leverage property based testing, refinement types and the most difficult part: figuring out how to test code that interacts with the outside world.

Building on the earlier two introductions to functional programming with types, property based testing is the extra verification technique you need to ensure working software. We will work through the patterns of property based testing, starting with simple functions, working up to verification of a larger program.

By the end of this workshop participants will have a better understanding of the advantages of property based tests over example based tests, as well as acquiring the skills and confidence to start applying property based testing techniques to their current work.

This workshop has the same requirements as Tony's introduction, and will require a recent clone of the workshop repository available at https://github.com/markhibberd/property-based-testing-workshop.

APL is the original functional programming language, the grand-daddy, the Godfather, and the old workhorse. But don't let Grandpa's age fool you. APL programmers have been leveraging the use of functional programming with arrays long before it was cool to be chasing pointers in an ADT using statically typed pattern matching, and they've refined their own style and approach to getting the most from a "functional paradigm."

In this workshop, you will have the chance to spend some time thinking like a functional array programmer. What makes it different? How does the code look at the end? What thought process do you go through to get there? Get a chance to play around with some classic problems and try solving them "the APL way."

Taijiquan Classics say, "Four ounces deflects a thousand pounds."

APLers might say instead, "Fifty characters solve a thousand problems."

Let's Lens presents a series of exercises, in a similar format to the Data61 functional programming course material. The subject of the exercises is around the concept of lenses, initially proposed by Foster et al., to solve the view-update problem of relational databases.

The theories around lenses have been advanced significantly in recent years, resulting in a library, implemented in Haskell, called lens.

This workshop will take you through the basic definition of the lens data structure and its related structures such as traversals and prisms. Following this we implement some of the low-level lens library, then go on to discuss and solve a practical problem that uses all of these structures.