Lambda the Ultimate

In a G+ request, Norman Ramsey says he wants spice up his PL survey course with a little bit of "cultural enrichment" to show my students the "propositions as types" approach to proof. He's looking for source materials, and, I take it, experiences from people who have tried to teach similar things.

A feature I lifted from ksh93, I thought I would share with LTU. Active variables in Common Lisp; variables with callbacks on reading and writing. Possibly a useful feature that I rarely see in modern programming languages.

github/cl-active-variables

Today I learned a new term from a blog post:

An interesting phenomena I've observed over my years as a programmer is the occurrence of "Peak Abstraction" within a young programmer - and their subsequent improvement.

There is a common pattern. It typically occurs after 3-4 years of professional practice. A programmer is growing in his confidence. He begins to tackle more complex, challenging problems. And, he reflects those problems with complex, challenging data structures. He has not yet learnt the fine art of simplicity.

Every member of every data structure is a pointer or a reference. There are no simple data types. ... Those around them become increasingly desperate. They can see the code becoming inflated, inefficient, unmaintainable.

And then it happens. The programmer realises the error of their ways. They realise that they are adding neither design value nor computational value. They realise they are adding overhead and maintenance trouble. ... And thus the recovery begins. Data structures become simple, expressive, and maintainable.

The complete blog post rags mostly on perf issues, but I'm more interested in the complexity implications. I felt myself go through this before, and to be honest the more powerful the language, the worse my peak abstraction got. It was only when moving to a less expressive language (C# rather than Scala) that I had incentive to keep it simple.

Has anyone else found themselves in an abstraction trap and come down as they grew as a programmer? What can we do in PL design to avoid, or at least discourage, overuse of abstraction? Is this a case of where less might be more?

I have been thinking recently about what makes the mainstream language a mainstream. And I think that the mainstream programming language paradigms generally evolve using the following evolution pattern:

1. Complexity Pain: complexity of reasoning about some aspects of the programs growth fastest with program growth. At some time amount of efforts required to reason about some piece of code becomes unreasonable.
2. Virtual Structure: Organizing program according to virtual structure that makes reasoning about that aspect of the program easier.
3. Explicit Structure: Develop language, that makes virtual structure explicit by introducing additional meta-level constructs, that organize constructs of the previous levels into the structure.

In paradigm for mainsteam languages we could obviously see the chain when the following level organizes the previous one.

1. [Conditional] jumps, memory assignments,and register arithmetics (assemblers).
2. Statements (FORTRAN).
3. Blocks, procedures, and data types (C, Pascal).
4. Classes (abstraction over procedures and data types) (C++)
5. Components (abstraction over classes and interfaces that differenitates techinical and intential aspects) (Java, C#, VB.NET, VB+COM, JavaScript[only halve way here])

As we see here, the next generation always abstracted previous level, and it was exactly one level abstraction. The functional languages so far do not fit into this picture.

The LISP was not one step jump, they are many step jump. Many features of LISP got into mainstream (the latest were garbage collection and lambda expressions), but there are some things to integerate (like internal DSLs). So LIPS was a kind of peak experience for programming language paradigms.

To me Haskell and ML did not offer appropriate one-step abstraction over structured programming. ML modules were only half-step abstraction over data and procedures, since data and functions were still different concepts. There were also halfway abstraction of data and behavior in the form of lambda expressions, but it was a single-method object, but GUI toolkits (current complexity challlenge at that time) required many-methods objects for appropriate abstraction.

If we look to the sequence, the next step should be some abstraction over components, and I think it would be component systems. We could see the early component systems as libraries now (Spring, GUI toolkits, EJB3, Plugins in Eclipse and IDEA, UML2, etc).

I have written more details why I think so in this document. It also contains an initial list of requirements for the possible next generation mainstream language. I think it will be a component system programming langauge (CSPL). So the document is named CSPL Challenge. I think typing and and type-checking component systems would be a quite interesting research topic. And maybe there are already some results out there, that fit requirement exactly.

Hello all!

I'm building a lisp in F# using FParsec. The language is called AML. I have a reader and an interpreter running, and I just now built a 'desugaring' function to rewrite AML code. For example, the expression a.b desugars to (access :m:b a). This is all well and good, but there is a problem. Once the desugarer outputs the transformed code text, it will be passed to the reader. As mentioned, the reader uses FParsec to create the AST. When FParsec encounters an unparsable character sequence, it propagates some nice error position information, a somewhat useful error message, and a description of the where the code text cannot be parsed. The issue is that this information will not be very meaningful to the user once the reader gets its input from the desugarer rather than the original user's code.

How do interpreters typically preserve this error information across transformed code so that the user can get error messages in terms of his original code?

Any practical or research material is nice. For run-time performance and implement-ability, I prefer a simple algorithm to a more sophisticated one. In truth, I am a language development newbie in many respects.

Thank you greatly!

LastCalc is a very experimental web-based tool that started out as a kind of Google Calculator but with a "REPL"-style user interface that permits variable assignment.

Like Google Calculator it supports basic math, and conversions between different measurement types like miles or kilograms (relying on the Java JScience library for this functionality), all with a relatively natural language syntax.

Along the way we added the ability to define parameterized functions, and then pattern matching on datastructures like lists and maps (inspired by ML), including recursively defined functions, all in a friendly AJAX-based web user interface.

LastCalc's parser is extremely robust, doing a reasonably efficient best-first search with backtracking to interpret its input. Anything it can't interpret is ignored.

Soon people will be able to share the parameterized functions they've designed, ultimately allowing people to collaboratively "teach" LastCalc how to interpret things that it currently cannot.

The link above provides a quick walk through of the system along with a link to it so that you can try it for yourself.

Link to the original blog entry.

In the course of this blog I am going to use a variant of Eiffel called
"Modern Eiffel". This blog entry gives a short outline of the language
elements. Although "Modern Eiffel" is a full blown language with all
sorts of imperative elements, mutable reference type objects, multiple
inheritance, dynamic bind etc. this blog entry concentrates only on a
small subset: immutable types and inductive types. The reason:
immutable/inductive types are a very important concept to verify
statically the correctness of a program. More advanced features will be
introduced if necessary.

Skeleton of a class

Modern Eiffel is an object oriented language. All types are defined by
classes (as in other object oriented languages like C++, C#, java,
scala, etc.). The skeleton of a class looks like

   class
      MY_CLASS   -- class name
   create
      creator1(a:A)  ...
      creator2  ...
   feature
      some_function(b:B): RT   ...

      some_command(c:C)  ...

      all(i:INT) some_property(i)
         proof
            ...
         end

      CHAR = CHARACTER   -- type alias
   invariant
      ...   -- consistency conditions
   end

Comments begin with "--" and span to the end of the line.
Classes and types are always written in uppercase. Each class can have
creators by which objects of the corresponding type can be constructed.
The features of a class are either functions (i.e. routines which return
a value) or commands (i.e. routines which change the state of the
object) or assertions or type aliases.

Basic types

There are some basic types. These are types with built-in functions and
built-in value representation. The basic types are BOOLEAN, NAT
(unsigned integers of a certain size), INT (signed integers of a certain
size). Beside their built-in functions and value representation the
basic types are nothing special. They are defined with classes as well.

   immutable class
      BOOLEAN
   feature
      implication alias "=>" (o:BOOLEAN): BOOLEAN
         external "built_in" end

      negated alias "not": BOOLEAN
         external "built_in" end

      conjuncted alias "and" (o:BOOLEAN): BOOLEAN
         external "built_in" end

      disjuncted alias "or" (o:BOOLEAN): BOOLEAN
         external "built_in" end

      ...
   end

Functions can have operator aliases for unary and binary operators. I.e.
having variables a,b,c of type BOOLEAN the expressions

   not a            -- highest precedence

   a and b

   a or b

   a => b           -- lowest precedence

have the expected meaning. The most important boolean operator is the
implication "=>". It is right associative i.e. "a => b => c" is parsed
as "a => (b => c)". The binary operators "and" and "or" are left
associative. All binary boolean operators are not strict (short
circuited) i.e. the second operand is only evaluated if necessary to
determine the truth value of the expression.

Default inheritance and equality

A class can inherit features from other classes. For this blog entry
inheritance is not important. I just mention the fact that each class
has some implicit inheritance. A class T with no inheritance clause is
treated as if it were defined as

   class
      T
   inherit
      -> ANY
      COMPARABLE
   ...
   end

The symbol "->" stands for conforming inheritance. For the following
the class ANY is not important. But the parent class COMPARABLE defines
equality. It has the outline

   class
      COMPARABLE
   feature
      is_equal(o:like Current): BOOLEAN
         external "built_in"
         ensure
            all(x:like Current) x=x
            all(x,y: like Current) x=y => y=x
            all(x,y,z: like Current) x=y => y=z => x=z
            -- "=" has higher precedence than "=>"
         end
   end

The feature "is_equal" is used in Modern Eiffel to define equality. It
is a built-in function which guarantees properties which are expected
from an equivalence relation. Reflexivity (every entity is equal to
itself), symmetry and transitivity. In Modern Eiffel the postconditions
of built-in routines are treated as axioms, i.e. properties which do not
need any proof. Note the transitivity law written as

  x=y => y=z => x=z

Usually one would expect the transitivity law written as

   x=y and y=z => x=z

Both forms are equivalent. This will be proved later. If possible we
write all properties with the implication operator, because implication
is more powerful and practical for proofs.

Each class inheriting from COMPARABLE can redefine (i.e. overwrite) the
feature "is_equal". The postconditions are inherited but in case of
redefinition they are no longer treated as axioms; they have to be
proved to be true.

Inductive types Simple inductive types

People with experience in imperative languages like C++, C#, Java, etc.
are not acustomed to use inductive types. People having experience with
functional languages like Haskell, ML, OCAML, COQ, Isabelle know
inductive types well. This chapter is written for people not knowing
inductive types. The others might just be interested to learn how
inductive types are presented in Eiffel syntax.

Inductive data types are best explained with some examples. The simplest
inductive type is similar to an enumeration type in other languages. We
can define a class COLOR like

  case class
    COLOR
  create
    red
    green
    blue
  end

The keyword "case" introduces an inductive class. The class COLOR has
just three creators to define objects of type color. E.g. we can use
this class to define variable of type COLOR.

    r: COLOR := red
    g: COLOR := green
    ...

The assigment operator ":=" can be used to initialize variables.

In a similar fashion we can define a class WEEKDAY

  case class
    WEEKDAY
  create
    monday
    thuesday
    wednesday
    ...
    sunday
  feature
    next: WEEKDAY
      ...
  end

The class WEEKDAY has a feature "next" in order to calculate the next
weekday. The definition of "next" looks like

  next: WEEKDAY =
    inspect Current
    case monday   then thuesday
    case thuesday then wednesday
    ...
    case sunday   then monday
    end

Inductive types allow case expression to make distinctions on how the
object has been constructed. The keyword "Current" returns the current
object. For each creator a cause clause is given. The meaning should be
quite clear. It is like C's select statement.

The above form is the short form of a function definition. The function
"next" does not have any precondition. Therefore the short form is
appropriate. In the case that a function has preconditions, the long
form must be used.

  next: WEEKDAY
    require
       ...   -- precondition(s)
    ensure
      Result =
        inspect Current
        case monday    then thuesday
        ...
        end
    end

The keyword "Result" indicates the return value of the function. The
postcondition "Result = ... " specifies the return value of the function.

Inductive types with stored values

Objects of the very simple inductive types like COLOR and WEEKDAY just
store as its value the way they have been constructed. But objects of
inductive type can store other values as well.

Let us consider the following class

  case class
    OPTIONAL[G]
  create
    none
    value(value: G)
  end

This class is generic. A generic class is a type constructor. It can
construct the types OPTIONAL[INT], OPTIONAL[BOOLEAN], OPTIONAL[COLOR],
OPTIONAL[OPTIONAL[CHARACTER]], ...

The class has just two creators: "none" and "value". The object

  n: OPTIONAL[INT] := none

has no value and the object

  i7: OPTIONAL[INT] := value(7)

has the value 7. The name "value" is used for the creator and for an
attribute. The attribute "value" is an optional attribute. It is
available only if the object has been created with the "value" creator.

The access of the attribute "value" is illegal, if the corresponding
object has not been created with the "value" creator. In many case the
attribute is not accessed directly. Usually the attribute is accessed
better through pattern matching expressions.

In order to introduce pattern matching we use a contrived example of a
function.

  value_plus_100(o:OPTIONAL[INT]) =
    inspect o
    case none     then  -1
    case value(i) then  i+100
    end

The function "value_plus_100 is contrived, because one would not write
such a function in Modern Eiffel. The inspect expression distinguishes
the two possible creators for an object of type OPTIONAL[INT].

The first case clause is for objects created with the "none" creator.
Since the "none" creator has no arguments, no value can be extracted in
that case.

The second case clause is for objects which have been created with the
"value" creator. This creator has one argument which can be retrieved
and attached to a fresh local variable with name "i". This variable is
availalbe only on the right hand side of this case clause to calculate
the return value.

The function "value_plus_100" can be written in a form which accesses
the attribute "value" directly.

  value_plus_100(o:OPTIONAL[INT]) =
    inspect o
    case none      then -1
    case value(_)  then o.value + 100
    end

The wild card "_" is used to supress the creations of a fresh local
variable. But the compiler derives the fact that in this case clause the
object "o" has been created with the "value" creator. Therefore the
access to the attribute "value" is valid within this clause.

Inductive types with recursive structure

The full expressive power of inductive types unfolds with recursively
defined inductive types. We can use an inductive type to define natural
numbers.

  case class
    UNARY_NATURAL
  create
    zero
    succ(pred: UNARY_NATURAL)
  feature
    plus alias  "+" (o:UNARY_NATURAL): UNARY_NATURAL = ...
    times alias "*" (o:UNARY_NATURAL): UNARY_NATURAL = ...
    power alias "^" (o:UNARY_NATURAL): UNARY_NATURAL = ...
    ...
  end

The class UNARY_NATURAL is highly inefficient to do arithmetics. But it
is a good vehicle to model natural numbers and use this model to prove
properties of very efficient implementations of natural numbers.

The class UNARY_NATURAL has tow creators. One to create the number 0 and
one to create the successor of an already created number.

The recursive structure of the type can be used to define the functions
"plus", "times" and "power" recursively.

  plus alias "+" (o:UNARY_NATURAL): UNARY_NATURAL =
    inspect Current
    case zero then o
    case succ(p) then succ(p+o)
    end

  times alias "*" (o:UNARY_NATURAL): UNARY_NATURAL =
    inspect Current
    case zero then zero
    case succ(p) then o + p*o
    end

  power alias "^" (o:UNARY_NATURAL): UNARY_NATURAL =
    inspect o
    case zero then succ(zero)
    case succ(p) then Current * Current^p
    end

Recursive functions for inductive types defined in this fashion have the
nice property that the compiler can verify the termination of the
recursion. In order to see this let us look at the recursive branch of
the definition of "+"

   case succ(p) then succ(p+o)

This branch is entered only if the current object has been created with
the "succ" creator using an already existing number, the predecessor of
the current object. The left hand side of the case expression attaches
the predecessor of the current object to the fresh local variable "p".
The right hand side

   succ(p+o)

uses the predecessor to make the recursive call "p+o", i.e. it calls the
function "plus" recursively. This recursive call will finally terminate
i.e. end up in a branch "case zero", because the very first creator of
any UNARY_NATURAL number must have been the "zero" creator. This is the
only way to create a UNARY_NATURAL if no other UNARY_NATURAL is available.

Another classic inductive type is defined by the class LIST.

  case class
    LIST[G]
  create
    nil
    cons alias "::" (first:G; tail:LIST[G])
  feature
    ...
  end

A list is either empty or is an element (the first element) followed by
another list (the tail of the newly created list).

If a creator has two arguments, a binary operator can be used as an
alias. All binary operators ending with "::" are right associative.
Therefore the following initialization of variables is well defined

  lst: LIST[INT] :=   1::2::3::nil
  -- parsed as 1::(2::(3::nil))
  -- equivalent to cons(1,cons(2,(cons(3,nil)))

Since lists are very expressive on its own and can serve as models an
extra blog entry will be dedicated to lists.

Summary

This blog has introduced some basic language elements of Modern Eiffel.
Some next blog entries will concetrate more on lists and on how to prove
assertions within Modern Eiffel.

Keep in mind that all sorts of mutable structures and imperative
elements are possible in Modern Eiffel. But in order to verify SW
immutable types and especially inductive types are very important.
Therefore this blog will first scrutinize immutable types.

The recent mention of the Nile programming language reminded me to post about the STEPS 2011 Progress Report.

The overall goal of STEPS is to make a working model of as much personal computing phenomena and user experience as possible in a very small number of lines of code (and using only our code). Our total lines of code‑count target for the entire system “from end‑user down to the metal” is 20,000, which—if we can do a lot within this limit—we think will be a very useful model and substantiate one part of our thesis: that systems which use millions to hundreds of millions of lines of code to do comparable things are much larger than they need to be.

...

Our general approach is to pick an area whose parts seem to be related—for example: 2.5D anti‑aliased alphaed computer graphics—try to find the mathematical relations that cover the desired phenomena, design a “problem oriented language” (POL) that is a “runnable math” version of the mathematics, implement that language, then use it to write the program code for the target area.

...

STEPS is now to the point where it is more fun to use and demonstrate than to talk and write about. Quite a bit of the original proposal is now working well enough to give all our presentations and write this entire report for the NSF and Viewpoints Research Institute websites. In the STEPS system, this document is live: the examples are actually runnable demos; the code shown is changeable and testable, etc.

A large part of the work over the last year has gone into making the test versions more real and usable.

Previous discussion of this project on LtU includes:

• Steps Toward The Reinvention of Programming The NSF grant proposal (2006)
• STEPS Toward The Reinvention of Programming: First Year Progress Report
• Programming and Scaling, a lecture by Alan Kay

Greetings,

Ehrhard and Regnier described a functional programming language -- the differential lambda calculus -- which added to the lambda calculus a commutative monoid structure and a differential operation. One feature of this calculus is that application is linear in the first argument.

I am studying a weaker system that adds only the commutative monoid structure, and I insist that application preserves addition in the first argument (which is implied in E.R.'s system). This means:
(g+h)*m = g*m + h*m
0*m = 0
(where * is application)

The above has the feel of non-determinism to it. Given 'g' or 'h' applied to 'm' the result is 'g' applied to 'm' or 'h' applied to 'm.'

However, in general:
g*(m+n) =/= g*m + g*n

So it is not as if 'g' makes a non-deterministic choice of 'm' or 'n.' While some have called this non-determinism, others have called parallelism.

Any thoughts on what the correct word describing such an application might be?

From the it-had-to-happen department, Announcing Visual Studio Achievements Beta:

Impress your friends! Earn achievements while you code! Code while you earn achievements!

What's troubling is that the list of achievements in fact includes various anti-achievements such as Go To Hell (for using goto), Potty Mouth ("Use 5 different curse words in a file"), Turtles All The Way Down ("Write a class with ten levels of inheritance") etc.

In the grim future of gamified PLT, you'll not only be tyrannised by your BDFL, your aberrances from the One Microsoft Way will be broadcast to your peers in realtime.

Also: XCode achievements on Twitter.