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Refactoring Functional Programs

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Refactoring Functional Programs. Huiqing Li Claus Reinke Simon Thompson Computing Lab, University of Kent www.cs.kent.ac.uk/projects/refactor-fp/. Writing a program. -- format a list of Strings, one per line table :: [String] -> String table = concat . format - PowerPoint PPT Presentation
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Huiqing Li Claus Reinke Simon Thompson Computing Lab, University of Kent www.cs.kent.ac.uk/projects/refactor-fp/ Refactoring Functional Programs
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Page 1: Refactoring Functional Programs

Huiqing LiClaus Reinke

Simon Thompson

Computing Lab, University of Kent

www.cs.kent.ac.uk/projects/refactor-fp/

Refactoring Functional Programs

Page 2: Refactoring Functional Programs

Manchester 04 2

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

format :: [String] -> String

format [] = []

format [x] = [x]

format (x:xs) = (x ++ "\t") : fomrat xs

Page 3: Refactoring Functional Programs

Manchester 04 3

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

format :: [String] -> String

format [] = []

format [x] = [x]

format (x:xs) = (x ++ "\t") : fomrat xs

Page 4: Refactoring Functional Programs

Manchester 04 4

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

format :: [String] -> String

format [] = []

format [x] = [x]

format (x:xs) = (x ++ "\t") : format xs

Page 5: Refactoring Functional Programs

Manchester 04 5

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

format :: [String] -> String

format [] = []

format [x] = [x]

format (x:xs) = (x ++ "\t") : format xs

Page 6: Refactoring Functional Programs

Manchester 04 6

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

format :: [String] -> [String]

format [] = []

format [x] = [x]

format (x:xs) = (x ++ "\t") : format xs

Page 7: Refactoring Functional Programs

Manchester 04 7

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

format :: [String] -> [String]

format [] = []

format [x] = [x]

format (x:xs) = (x ++ “\t") : format xs

Page 8: Refactoring Functional Programs

Manchester 04 8

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

format :: [String] -> [String]

format [] = []

format [x] = [x]

format (x:xs) = (x ++ "\n") : format xs

Page 9: Refactoring Functional Programs

Manchester 04 9

Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

appNL :: [String] -> [String]

appNL [] = []

appNL [x] = [x]

appNL (x:xs) = (x ++ "\n") : appNL xs

Page 10: Refactoring Functional Programs

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Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . format

appNL :: [String] -> [String]

appNL [] = []

appNL [x] = [x]

appNL (x:xs) = (x ++ "\n") : appNL xs

Page 11: Refactoring Functional Programs

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Writing a program

-- appNL a list of Strings, one per line

table :: [String] -> String

table = concat . appNL

appNL :: [String] -> [String]

appNL [] = []

appNL [x] = [x]

appNL (x:xs) = (x ++ "\n") : appNL xs

Page 12: Refactoring Functional Programs

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Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . appNL

appNL :: [String] -> [String]

appNL [] = []

appNL [x] = [x]

appNL (x:xs) = (x ++ "\n") : appNL xs

Page 13: Refactoring Functional Programs

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Writing a program

-- format a list of Strings, one per line

table :: [String] -> String

table = concat . appNL

where

appNL :: [String] -> [String]

appNL [] = []

appNL [x] = [x]

appNL (x:xs) = (x ++ "\n") : appNL xs

Page 14: Refactoring Functional Programs

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Refactoring

Refactoring means changing the design of program …

… without changing its behaviour.

Refactoring comes in many forms

• micro refactoring as a part of program development,• major refactoring as a preliminary to revision,• as a part of debugging, …

As programmers, we do it all the time.

Page 15: Refactoring Functional Programs

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Not just programming

Paper or presentationmoving sections about; amalgamate sections; move inline code to a figure; animation; …

Proof introduce lemma; remove, amalgamate hypotheses, …

Programthe topic of the lecture

Page 16: Refactoring Functional Programs

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Overview of the talk

Example refactorings … what do we learn?

Refactoring functional programs

Generalities

Tooling: demo, rationale, design.

What comes next?

Conclusions

Page 17: Refactoring Functional Programs

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Refactoring Functional Programs

• 3-year EPSRC-funded project Explore the prospects of refactoring functional programs

Catalogue useful refactorings

Look into the difference between OO and FP refactoring

A real life refactoring tool for Haskell programming

A formal way to specify refactorings … and a set of proofs that the implemented refactorings are correct.

• Currently mid-project: the second HaRe release is module-aware.

Page 18: Refactoring Functional Programs

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Refactoring functional programs

Semantics: can articulate preconditions and … … verify transformations.

Absence of side effects makes big changes predictable and verifiable … … unlike OO.

Language support: expressive type system, abstraction mechanisms, HOFs, …

Opens up other possibilities … proof …

Page 19: Refactoring Functional Programs

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Rename

f x y = …

Name may be too specific, if the function is a candidate for reuse.

findMaxVolume x y = …

Make the specific purpose of the function clearer.

Needs scope information: just change this f and not all fs (e.g. local definitions or variables).

Needs module information: change f wherever it is imported.

Page 20: Refactoring Functional Programs

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Lift / demote

f x y = … h …

where

h = …

Hide a function which is clearly subsidiary to f; clear up the namespace.

f x y = … (h y) …

h y = …

Makes h accessible to the other functions in the module and beyond.

Needs free variable information: which of the parameters of f is used in the definition of h?

Need h not to be defined at the top level, … , DMR.

Page 21: Refactoring Functional Programs

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Lessons from the first examples

Changes are not limited to a single point or even a single module: diffuse and bureaucratic …

… unlike traditional program transformation.

Many refactorings bidirectional …

… as there is never a unique correct design.

Page 22: Refactoring Functional Programs

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How to apply refactoring?

By hand, in a text editor

Tedious

Error-prone

Depends on extensive testing

With machine support

Reliable

Low cost: easy to make and un-make large changes.

Exploratory … a full part of the programmer’s toolkit.

Page 23: Refactoring Functional Programs

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Machine support invaluable

Current practice: editor + type checker (+ tests).

Our project: automated support for a repertoire of refactorings …

… integrated into the existing development process: Haskell IDEs such as vim and emacs.

Page 24: Refactoring Functional Programs

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Demonstration of HaRe, hosted in vim.

Page 25: Refactoring Functional Programs

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Proof of concept …

To show proof of concept it is enough to:

• build a stand-alone tool,

• work with a subset of the language,

• ‘pretty print’ the refactored source code in a standard format.

Page 26: Refactoring Functional Programs

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… or a useful tool?

To make a tool that will be used we must:

• integrate with existing program development tools: the program editors emacs and vim: only add to their capabilities;

• work with the complete Haskell 98 language;

• preserve the formatting and comments in the refactored source code;

• allow users to extend and script the system.

Page 27: Refactoring Functional Programs

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Refactorings implemented in HaRe

RenameDeleteLift (top level / one level)DemoteIntroduce definitionRemove definitionUnfoldGeneraliseAdd and remove parameters

All these refactorings are module aware.

Page 28: Refactoring Functional Programs

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Implementing HaRe: an example

-- This is an example

module Main where

sumSquares x y = sq x + sq y

where sq :: Int->Int

sq x = x ^ pow

pow = 2 :: Int

main = sumSquares 10 20

Promote the definition of sq to top level

Page 29: Refactoring Functional Programs

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Implementing HaRe: an example

-- This is an example

module Main where

sumSquares x y = sq x + sq y

where sq :: Int->Int

sq x = x ^ pow

pow = 2 :: Int

main = sumSquares 10 20

Identify the definition of sq to be promoted

Page 30: Refactoring Functional Programs

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Implementing HaRe: an example

-- This is an example

module Main where

sumSquares x y = sq x + sq y

where sq :: Int->Int

sq x = x ^ pow

pow = 2 :: Int

main = sumSquares 10 20

Is sq defined at top level, here or in importing modules; is sq imported from elsewhere?

Page 31: Refactoring Functional Programs

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Implementing HaRe: an example

-- This is an example

module Main where

sumSquares x y = sq x + sq y

where sq :: Int->Int

sq x = x ^ pow

pow = 2 :: Int

main = sumSquares 10 20

Does sq use anything defined locally to sumSquares ?

Page 32: Refactoring Functional Programs

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Implementing HaRe: an example

-- This is an example

module Main where

sumSquares x y = sq pow x + sq pow y

where sq :: Int->Int->Int

sq pow x = x ^ pow

pow = 2 :: Int

main = sumSquares 10 20

If so, generalise to add these as parameters, and change type signature.

Page 33: Refactoring Functional Programs

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Implementing HaRe: an example

-- This is an example

module Main where

sumSquares x y = sq pow x + sq pow y

where pow = 2 :: Int

sq :: Int->Int->Int

sq pow x = x ^ pow

main = sumSquares 10 20

Finally, move the definition to top level.

Page 34: Refactoring Functional Programs

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The Implementation of Hare

Informationgathering

Pre-conditionchecking

Programtransformation

Programrendering

Page 35: Refactoring Functional Programs

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Information needed

Syntax: replace the function called sq, not the variable sq …… parse tree.

Static semantics: replace this function sq, not all the sq functions …… scope information.

Module information: what is the traffic between this module and its clients …… call graph.

Type information: replace this identifier when it is used at this type …… type annotations.

Page 36: Refactoring Functional Programs

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Infrastructure

To achieve this we chose to:

• build a tool that can interoperate with emacs, vim, … yet act separately.

• leverage existing libraries for processing Haskell 98, for tree transformation, yet …

… modify them as little as possible.

• be as portable as possible, in the Haskell space.

Page 37: Refactoring Functional Programs

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The Haskell background

Libraries• parser: many• type checker: few• tree transformations: few

Difficulties• Haskell98 vs. Haskell extensions.• Libraries: proof of concept vs. distributable.• Source code regeneration.• Real project

Page 38: Refactoring Functional Programs

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Programatica

Project at OGI to build a Haskell system …

… with integral support for verification at various levels: assertion, testing, proof etc.

The Programatica project has built a Haskell front end in Haskell, supporting syntax, static, type and module analysis …

… freely available under BSD licence.

Page 39: Refactoring Functional Programs

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The Implementation of Hare

Informationgathering

Pre-conditionchecking

Programtransformation

Programrendering

Page 40: Refactoring Functional Programs

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First steps … lifting and friends

Use the Haddock parser … full Haskell given in 500 lines of data type definitions.

Work by hand over the Haskell syntax: 27 cases for expressions …

Code for finding free variables, for instance …

Page 41: Refactoring Functional Programs

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Finding free variables … 100 lines

instance FreeVbls HsExp where

freeVbls (HsVar v) = [v]

freeVbls (HsApp f e)

= freeVbls f ++ freeVbls e

freeVbls (HsLambda ps e)

= freeVbls e \\ concatMap paramNames ps

freeVbls (HsCase exp cases)

= freeVbls exp ++ concatMap freeVbls cases

freeVbls (HsTuple _ es)

= concatMap freeVbls es

… etc.

Page 42: Refactoring Functional Programs

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This approach

Boiler plate code …

… 1000 lines for 100 lines of significant code.

Error prone: significant code lost in the noise.

Want to generate the boiler plate and the tree traversals …

… DriFT: Winstanley, Wallace… Strafunski: Lämmel and Visser

Page 43: Refactoring Functional Programs

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Strafunski

Strafunski allows a user to write general (read generic), type safe, tree traversing programs …

… with ad hoc behaviour at particular points.

Traverse through the tree accumulating free variables from component parts, except in the case of lambda abstraction, local scopes, …

Strafunski allows us to work within Haskell … other options are under development.

Page 44: Refactoring Functional Programs

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Rename an identifier

rename:: (Term t)=>PName->HsName->t->Maybe t rename oldName newName = applyTP worker where worker = full_tdTP (idTP ‘adhocTP‘ idSite) idSite :: PName -> Maybe PName idSite v@(PN name orig) | v == oldName = return (PN newName orig) idSite pn = return pn

Page 45: Refactoring Functional Programs

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The coding effort

Transformations with Strafunski are straightforward …

… the chore is implementing conditions that guarantee that the transformation is meaning- preserving.

This is where much of our code lies.

Page 46: Refactoring Functional Programs

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The Implementation of Hare

Informationgathering

Pre-conditionchecking

Programtransformation

Programrendering

Page 47: Refactoring Functional Programs

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Program rendering example

-- This is an example

module Main where

sumSquares x y = sq x + sq y

where sq :: Int->Int

sq x = x ^ pow

pow = 2 :: Int

main = sumSquares 10 20

Promote the definition of sq to top level

Page 48: Refactoring Functional Programs

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Program rendering example

module Main where

sumSquares x y

= sq pow x + sq pow y where pow = 2 :: Int

sq :: Int->Int->Int

sq pow x = x ^ pow

main = sumSquares 10 20

Using a pretty printer: comments lost and layout quite different.

Page 49: Refactoring Functional Programs

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Program rendering example

-- This is an example

module Main where

sumSquares x y = sq x + sq y

where sq :: Int->Int

sq x = x ^ pow

pow = 2 :: Int

main = sumSquares 10 20

Promote the definition of sq to top level

Page 50: Refactoring Functional Programs

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Program rendering example

-- This is an example

module Main where

sumSquares x y = sq pow x + sq pow y

where pow = 2 :: Int

sq :: Int->Int->Int

sq pow x = x ^ pow

main = sumSquares 10 20

Layout and comments preserved.

Page 51: Refactoring Functional Programs

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Rendering: our approach

White space and comments in the token stream.

2 views of the program: token stream and AST.

Modification of the AST guides the modification of the token stream.

After a refactoring, the program source is extracted from the token stream not the AST.

Use heuristics to associate comments with semantic entities.

Page 52: Refactoring Functional Programs

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Production tool (version 0)

Programaticaparser and

type checker

Refactorusing a

Strafunskiengine

Render codefrom the

token streamand

syntax tree.

Page 53: Refactoring Functional Programs

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Production tool (version 1)

Programaticaparser and

type checker

Refactorusing a

Strafunskiengine

Render codefrom the

token streamand

syntax tree.

Pass lexical information toupdate thesyntax treeand so avoid reparsing

Page 54: Refactoring Functional Programs

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Module awareness: example

Move a top-level definition f from module A to B.

-- Is f defined at the top-level of B? -- Are the free variables in f accessible within module B? -- Will the move require recursive modules?

-- Remove the definition of f from module A. -- Add the definition to module B. -- Modify the import/export lists in module A, B and the client modules of A and B if necessary. -- Change uses of A.f to B.f or f in all affected modules. -- Resolve ambiguity.

Page 55: Refactoring Functional Programs

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What have we learned?

Emerging Haskell libraries make it a practical platform.

Efficiency issues … type checking large systems.

Limitations of IDE interactions in vim and emacs.

Reflections on Haskell itself.

Page 56: Refactoring Functional Programs

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Reflecting on Haskell

Cannot hide items in an export list (though you can on import).

The formal semantics of pattern matching is problematic.

‘Ambiguity’ vs. name clash.

‘Tab’ is a nightmare!

Correspondence principle fails …

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Correspondence

Operations on definitions and operations on expressions can be placed in correspondence

(R.D.Tennent, 1980)

Page 58: Refactoring Functional Programs

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Correspondence

Definitions

where

f x y = e

f x

| g1 = e1

| g2 = e2

Expressions

let

\x y -> e

f x = if g1 then e1 else if g2 …

Page 59: Refactoring Functional Programs

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Where do we go next?

• Larger-scale examples: ADTs, monads, …

• An API for do-it-yourself refactorings, or …

• … a language for composing refactorings

• Detecting ‘bad smells’

• Evolving the evidence: GC6.

Page 60: Refactoring Functional Programs

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What do users want?

Find and remove duplicate code.

Argument permutations.

Data refactorings.

More traditional program transformations.

Monadification.

Page 61: Refactoring Functional Programs

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Monadification (cf Erwig)

r = f e1 e2 do v1 <- e1

v2 <- e2

r <- f v1 v2

return r

Page 62: Refactoring Functional Programs

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Larger-scale examples

More complex examples in the functional domain; often link with data types.

Dawning realisation that can some refactorings are pretty powerful.

Bidirectional … no right answer.

Page 63: Refactoring Functional Programs

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Algebraic or abstract type?

data Tr a

= Leaf a |

Node a (Tr a) (Tr a)

Tr

Leaf

Node

flatten :: Tr a -> [a]

flatten (Leaf x) = [x]

flatten (Node s t)

= flatten s ++

flatten t

Page 64: Refactoring Functional Programs

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Algebraic or abstract type?

data Tr a

= Leaf a |

Node a (Tr a) (Tr

a)

isLeaf = …

isNode = …

Tr

isLeaf

isNode

leaf

left

right

mkLeaf

mkNode

flatten :: Tr a -> [a]

flatten t

| isleaf t = [leaf t]

| isNode t

= flatten (left t)

++ flatten (right t)

Page 65: Refactoring Functional Programs

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Algebraic or abstract type?

Pattern matching syntax is more direct …

… but can achieve a considerable amount with field names.

Other reasons? Simplicity (due to other refactoring steps?).

Allows changes in the implementation type without affecting the client: e.g. might memoise

Problematic with a primitive type as carrier.

Allows an invariant to be preserved.

Page 66: Refactoring Functional Programs

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Outside or inside?

data Tr a

= Leaf a |

Node a (Tr a) (Tr

a)

isLeaf = …

Tr

isLeaf

isNode

leaf

left

right

mkLeaf

mkNode

flatten :: Tr a -> [a]

flatten t

| isleaf t = [leaf t]

| isNode t

= flatten (left t)

++ flatten (right t)

Page 67: Refactoring Functional Programs

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Outside or inside?

data Tr a

= Leaf a |

Node a (Tr a) (Tr

a)

isLeaf = …

flatten = …

Tr

isLeaf

isNode

leaf

left

right

mkLeaf

mkNode

flatten

Page 68: Refactoring Functional Programs

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Outside or inside?

If inside and the type is reimplemented, need to reimplement everything in the signature, including flatten.

The more outside the better, therefore.

If inside can modify the implementation to memoise values of flatten, or to give a better implementation using the concrete type.

Layered types possible: put the utilities in a privileged zone.

Page 69: Refactoring Functional Programs

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API

Refactorings

Refactoringutilities

Strafunski

Haskell

Page 70: Refactoring Functional Programs

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DSL

Refactorings

Refactoringutilities

Strafunski

Haskell

Combining forms

Page 71: Refactoring Functional Programs

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Detecting ‘bad smells’

Work byChris Ryder

Page 72: Refactoring Functional Programs

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Evolving the evidence

Dependable System Evolution is the software engineering grand challenge.

Build systems with evidence of their dependability …

… but this begs the question of how to evolve the evidence in line with the system.

Refactoring proofs, test coverage data etc.

Page 73: Refactoring Functional Programs

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Teaching and learning design

Exciting prospect of using a refactoring tool as an integral part of an elementary programming course.

Learning a language: learn how you could modify the programs that you have written …

… appreciate the design space, and

… the features of the language.

Page 74: Refactoring Functional Programs

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Conclusions

Refactoring + functional programming: good fit.

Practical tool … not ‘yet another type tweak’.

Leverage from available libraries … with work.

We have begun to use the tool in building itself!

Much more to do than we have time for.

Martin Fowler’s ‘Rubicon’: ‘extract definition’ … in HaRe version 1 … fp productivity.

Page 75: Refactoring Functional Programs

www.cs.kent.ac.uk/projects/refactor-fp/


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