虽然这可能被认为是天真和简单的，但我认为“设计食谱”(一种应用于编程的系统解决问题的方法，如Felleisen等人在他们的书HtDP中所提倡的)将接近你所寻找的东西。

这里有一些链接:

http://www.northeastern.edu/magazine/0301/programming.html

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.8371

好吧,

一般来说，许多函数式编程语言在大学里被用来解决“小玩具问题”已经很长时间了。

由于“状态”的原因，OOP在“并行编程”方面有困难，因此它们现在变得越来越流行。有时函数式风格更适合解决手头的问题，比如谷歌MapReduce。

我敢肯定，当功能人员碰壁(尝试实现超过1.000.000行代码的系统)时，他们中的一些人会提出新的软件工程方法，并使用流行语:-)。他们应该回答这个老问题:如何将系统分成几部分，以便我们可以一次“咬”每一部分?使用功能风格[工作迭代，仪式和进化的方式]。

函数式风格肯定会影响我们的面向对象 风格。我们“保留”了功能系统中的许多概念并加以适应 我们的OOP语言。

但是函数式程序会被用于这样一个大系统吗?它们会成为主流吗?这就是问题所在。

没有人能提出现实的方法，而不实施这样一个大系统，弄脏自己的手。 首先你应该把自己的手弄脏，然后再提出解决方案。解决方案-没有“真正的痛苦和污垢”的建议将是“幻想”。

有一种“程序计算”/“通过计算设计”的风格与Richard Bird教授和牛津大学(英国)的代数编程组有关，我不认为将其视为一种方法论太牵强。

就我个人而言，虽然我喜欢AoP小组的工作，但我自己没有以这种方式实践设计的规程。但这是我的缺点，不是程序计算的缺点之一。

我最近发现了这本书: 功能和反应域建模

我认为这完全符合你的问题。

从书的描述:

Functional and Reactive Domain Modeling teaches you how to think of the domain model in terms of pure functions and how to compose them to build larger abstractions. You will start with the basics of functional programming and gradually progress to the advanced concepts and patterns that you need to know to implement complex domain models. The book demonstrates how advanced FP patterns like algebraic data types, typeclass based design, and isolation of side-effects can make your model compose for readability and verifiability.

老实说，如果你想设计函数式程序，可以看看标准函数库，比如Haskell的Prelude。在FP中，模式通常由高阶过程(对函数进行操作的函数)本身捕获。因此，如果看到了一个模式，通常会创建一个更高阶的函数来捕捉该模式。

fmap就是一个很好的例子。该函数以一个函数作为参数，并将其应用于第二个参数的所有“元素”。因为它是Functor类型类的一部分，所以Functor的任何实例(如列表、图形等)都可以作为第二个参数传递给这个函数。它捕捉了将函数应用于第二个参数的每个元素的一般行为。

感谢上帝，软件工程人员还没有发现函数式编程。这里有一些相似之处:

Many OO "design patterns" are captured as higher-order functions. For example, the Visitor pattern is known in the functional world as a "fold" (or if you are a pointy-headed theorist, a "catamorphism"). In functional languages, data types are mostly trees or tuples, and every tree type has a natural catamorphism associated with it. These higher-order functions often come with certain laws of programming, aka "free theorems". Functional programmers use diagrams much less heavily than OO programmers. Much of what is expressed in OO diagrams is instead expressed in types, or in "signatures", which you should think of as "module types". Haskell also has "type classes", which is a bit like an interface type. Those functional programmers who use types generally think that "once you get the types right; the code practically writes itself." Not all functional languages use explicit types, but the How To Design Programs book, an excellent book for learning Scheme/Lisp/Clojure, relies heavily on "data descriptions", which are closely related to types.

那么，在Lisp或Clojure中，系统地(基于模型的)设计功能应用程序的方法是什么呢?

任何基于数据抽象的设计方法都可以很好地工作。我碰巧认为，当语言有显式类型时，这更容易，但即使没有显式类型，它也可以工作。Barbara Liskov和John Guttag所著的《程序开发中的抽象与规范》(Abstraction and Specification in Program Development)是一本关于抽象数据类型设计方法的好书，很容易适应函数式编程。利斯科夫获得图灵奖的部分原因就是这项工作。

Another design methodology that is unique to Lisp is to decide what language extensions would be useful in the problem domain in which you are working, and then use hygienic macros to add these constructs to your language. A good place to read about this kind of design is Matthew Flatt's article Creating Languages in Racket. The article may be behind a paywall. You can also find more general material on this kind of design by searching for the term "domain-specific embedded language"; for particular advice and examples beyond what Matthew Flatt covers, I would probably start with Graham's On Lisp or perhaps ANSI Common Lisp.

常见的步骤是什么，我使用什么工件?

常见的步骤:

Identify the data in your program and the operations on it, and define an abstract data type representing this data. Identify common actions or patterns of computation, and express them as higher-order functions or macros. Expect to take this step as part of refactoring. If you're using a typed functional language, use the type checker early and often. If you're using Lisp or Clojure, the best practice is to write function contracts first including unit tests—it's test-driven development to the max. And you will want to use whatever version of QuickCheck has been ported to your platform, which in your case looks like it's called ClojureCheck. It's an extremely powerful library for constructing random tests of code that uses higher-order functions.