Download Manchester - University of Kent

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
format :: [String] -> String
format []
= []
format [x]
= [x]
format (x:xs)
= (x ++ "\t") : fomrat xs
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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
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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
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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
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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
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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
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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
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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
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xs
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
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xs
10
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
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xs
11
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
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xs
12
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
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xs
13
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.
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Not just programming
Paper or presentation
moving sections about; amalgamate sections; move
inline code to a figure; animation; …
Proof
introduce lemma; remove, amalgamate hypotheses, …
Program
the topic of the lecture
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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
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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.
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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 …
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Rename
f x y = …
findMaxVolume x y = …

Name may be too specific,
if the function is a
candidate for reuse.

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.
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Lift / demote
f x y = … h …
f x y = … (h y) …
where
h = …
h y = …

Hide a function which is
clearly subsidiary to f; clear
up the namespace.

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.
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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.
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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.
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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.
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Demonstration of HaRe, hosted in vim.
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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.
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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.
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Refactorings implemented in HaRe
Rename
Delete
Lift (top level / one level)
Demote
Introduce definition
Remove definition
Unfold
Generalise
Add and remove parameters
All these refactorings are module aware.
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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
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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
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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?
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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 ?
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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.
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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.
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The Implementation of Hare
Information
gathering
Pre-condition
checking
Program
transformation
Program
rendering
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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.
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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.
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The Haskell background
Libraries
• parser:
• type checker:
• tree transformations:
many
few
few
Difficulties
• Haskell98 vs. Haskell extensions.
• Libraries: proof of concept vs. distributable.
• Source code regeneration.
• Real project
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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.
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The Implementation of Hare
Information
gathering
Pre-condition
checking
Program
transformation
Program
rendering
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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 …
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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.
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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
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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.
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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
idSite
| v
idSite
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:: PName -> Maybe PName
v@(PN name orig)
== oldName = return (PN newName orig)
pn = return pn
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The coding effort
Transformations with Strafunski are
straightforward …
… the chore is implementing conditions that
guarantee that the transformation is meaningpreserving.
This is where much of our code lies.
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The Implementation of Hare
Information
gathering
Pre-condition
checking
Program
transformation
Program
rendering
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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
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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.
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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
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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.
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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.
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Production tool (version 0)
Programatica
parser and
type checker
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Refactor
using a
Strafunski
engine
Render code
from the
token stream
and
syntax tree.
52
Production tool (version 1)
Programatica
parser and
type checker
Refactor
using a
Strafunski
engine
Render code
from the
token stream
and
syntax tree.
Pass lexical
information to
update the
syntax tree
and so avoid
reparsing
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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.
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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.
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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)
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Correspondence
Definitions
Expressions
where
let
f x y = e
\x y -> e
f x
| g1
| g2
f x = if g1 then e1
else if g2 …
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=
e1
e2
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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.
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What do users want?
Find and remove duplicate code.
Argument permutations.
Data refactorings.
More traditional program transformations.
Monadification.
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Monadification (cf Erwig)
r = f e1 e2
do
v1 <- e1
v2 <- e2
r
<- f v1 v2
return r
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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.
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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
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Algebraic or abstract type?
Tr
isLeaf
data Tr a
= Leaf a |
Node a (Tr a) (Tr a)
isLeaf = …
isNode = …
isNode
flatten :: Tr a -> [a]
leaf
left
flatten t
right
| isleaf t = [leaf t]
mkLeaf
| isNode t
mkNode
= flatten (left t)
++ flatten (right t)
…
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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?).
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
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.
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Outside or inside?
Tr
isLeaf
isNode
data Tr a
= Leaf a |
Node a (Tr a) (Tr a)
isLeaf = …
…
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flatten :: Tr a -> [a]
leaf
left
flatten t
right
| isleaf t = [leaf t]
mkLeaf
| isNode t
mkNode
= flatten (left t)
++ flatten (right t)
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Outside or inside?
Tr
isLeaf
isNode
data Tr a
= Leaf a |
Node a (Tr a) (Tr a)
leaf
left
right
mkLeaf
isLeaf = …
mkNode
…
flatten
flatten = …
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Outside or inside?


If inside and the type is
reimplemented, need to
reimplement everything in
the signature, including
flatten.
If inside can modify the
implementation to memoise
values of flatten, or to give
a better implementation
using the concrete type.
The more outside the
better, therefore.
Layered types possible: put
the utilities in a privileged
zone.
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API
Refactorings
Refactoring
utilities
Strafunski
Haskell
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DSL
Combining forms
Refactorings
Refactoring
utilities
Strafunski
Haskell
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Detecting
‘bad
smells’
Work by
Chris Ryder
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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.
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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.
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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.
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www.cs.kent.ac.uk/projects/refactor-fp/