It is unlikely that you will ever design a general-use language like Fortran, C++, ML, or Prolog, but if you become a professional software engineer or software architect, it is highly likely that you will specialize in some problem area, like telecommunications, aviation, website management , banking, or gaming. You will become expert at building systems in your problem area, and you may well design a notation, a language, that helps you and others write solutions to problems in this area. In this case, you are a designer of a domain-specific language that is used to build domain-specific software architectures.
This chapter introduces these concepts, applying the concepts already learned.
Here is how you might want to talk, technical-style, to the robot, to tell it
how to pick up an item on the floor:
repeatedly rotate 30 degrees until you see an object on the floor.
repeatedly move forwards 6 inches until the object is within reach.
lower your arm then grasp the object then lift your arm.
return to Home Location.
These instructions aren't C code and they aren't dot-and-paren-laden method calls
to library packages for robot controllers. They are a custom language for the robot
domain, a robot-domain-specific language.
With the techniques in this chapter, you can design and implement the robot language. There are two main approaches:
Specific problem areas, e.g., flight-control or telecommunications or banking, use specific hardware architectures, and they also use specific software architectures. When a new model of airplane is designed, the hardware architecture (the airplane hardware, including its computers) is based on a hardware design that has succeeded in the past. (It is too great of a risk to start from scratch; it is also better to build on and refine what is known to work.) The software architecture for the plane will also be based on some standard layout that is known to work well.
Software architects use a collection of concepts, techniques, and patterns to build a new system in an established problem area; this collection is called a domain-specific software architecture:
The reference requirements are part of the terminology of the application domain, but they are often specially identified because they are often treated specially in the implementation methodology.
A language for discussing problems, behaviors, and solutions within a problem domain is a domain-specific language (DSL). The language's vocabulary includes concepts and notation from the domain --- the nouns, pronouns, adjectives, verbs, and adverbs of the language. The language lets participants (people and machines) discuss and implement solutions in the domain. Because its vocabulary is limited to the specific domain, a DSL is often useless for discussing and solving problems outside the domain.
A DSL uses concepts familiar to people who work in the domain. Here are two examples:
The entities can have features/attributes (``adjectives''): e.g., a word can be a label or data (in a cell). A grid has dimensions (rows and columns). A number can be data or a total value.
There are certainly operations on the entities --- inserting data into a cell, totalling the values in a row or column, printing a table.
A sequence or "script" of operations, perhaps triggered by an event, is called an action. Example action: "(event part:)"When a number is inserted into Row 9, then (operation part:) update the total for Row 9 using Equation 9 and redisplay the updated grid."
The accountant thinks in the language of the spreadsheet domain when building a spreadsheet, whether or not a computer program is helping to assemble and display the spreadsheet. But if the computer is doing the speadsheet layout, the DSL for spreadsheets becomes a programming language, e.g., Excel. (See below.)
A sample "use case" is a scenario. For example, the accountant might explain this scenario to the software architect:
Scenarios like this help the software architect implement the spreadsheet application for the accountant to use.
domains (''nouns''): sites (building, floor, hallway, room), devices (alarm, movement detector, camera, badge), people (employee, guard, police, intruder). These are the ``nouns'' of the DSL. There are also features/attributes (``adjectives'') and operations (''verbs'').
Here is a scenario:
The scenarios help you design and install an alarm system with the desired devices and behaviors. If a computer is involved in the domain, then some of the DSL about alarm systems will be a computer language that you use to tell the computer what it must do.
Compare the lingo of alarms to the lingo you write in Java --- in the latter, the ``nouns'' are numbers, arrays, objects, and variables that name numbers, arrays, objects, etc. The ``adjectives'' are data types and other declaration modifiers. The ``operations'' are arithmetic, data-structure indexing, method call, etc. ``Actions'' are commands, or groups of commands. ``Events'' can be GUI events or a call to a method to start execution. Java is a ``DSL'' for computation on numbers and arrays and objects.
A DSL lets stakeholders (the participants in a systems project) communicate their ideas (needs, suggestions, solutions, implementations, orders). The DSL is a is a modelling language that lets us discuss models, structures, and behaviors specialized to a problem domain like telecommunications, banking, transportation, gaming, algebra, typesetting, etc.
If the computer is a ``participant,'' that is, we can use some subset of the DSL to tell the computer what to do --- we can program the computer in a domain-specific programming language (DSPL).
Now, say we learn Excel (or a similar spreadsheet application). Excel understands the DSL of spreadsheets, and we "talk" (program) directly in Excel, implementing the scenarios and the spreadsheet application in a matter of hours or days. This is because Excel is a DSPL for spreadsheets --- we say what we want in the language of Excel and we have the application we want in minimal time and effort. This is the big payoff of having a DSPL.
IMPORTANT: EXCEL is actually a pair: a GUI that translates user inputs into the spreadsheet's DSPL, and a DSPL parser-interpreter. DSPLs designed for non-software engineers are usually accompanied by graphical front ends like EXCEL's.
Indeed, many sophisticated IDEs are actually GUI-DSPL pairs. And, you might argue that the input language (of mouse and keyboard events) of any application defines a crude DSPL. But the bestapplications have an input language that matches the DSL of the problem domain for which the application is intended!
But not all computational mechanisms are reactive. For example, the equational language of algebra is a DSL, and the computation underlying its equation sets are simplification laws. Yet another variation is the DSL of grammar equations, which we use to define programming languages. Another example is the box-and-arrow graphical language of UML class diagrams. There are many others --- see the section on "little languages."
In these cases, the DSL might be less``event/action oriented'', but in any case, it will be the language that the stakeholders use to discuss their problems and solutions.
One might argue that a general-purpose computer language is ``domain un-specific'' because it favors no one application domain very much over another. (A cynic would say that a general-purpose language is a ``no-domain language,'' since there is no real-life application domain that matches it!)
A user of a general-purpose language must become an expert modeller of real-life application domains in the domains of the general-purpose language --- This is core computer science: how to model real-life domains and domain-specific language within general-purpose computing machines and general-purpose computer language.
On the other side of the coin, we can argue that a language like C is domain-specific to the domain of von Neumann machines, and a language like Java is domain-specific to heap-based object machines. Such machines are used to mimick/model other computational domains, and this is why we use C or Java to mimick/model other DSLs.
When the complexities in domain modelling become too great, the general-purpose programming language must be abandoned for a domain-specific one.
But there is another origin of DSPLs that comes totally from within the programming world: It is inconvenient to drag out a general-purpose language to code a solution to something small and simple. For example, do you code a Java program each time you do some calculator arithmetic? No --- you use a calculator language instead. It is always better to use a smaller, simpler language --- a DSPL --- that matches the problem you face.
For this reason, programmers sometimes call DSPLs little languages (e.g., ``here is a little language for drawing figures''; ``here is a little language for linking files''.) Here is a short list of ''little language'' DSPLs that have/had wide use:
In terms of domain-specific software architecture, someone might ask you,
For example, ''It would be nice to have a little language to help us lay out the wiring and sensors for a building's alarm system.''.
Or, ''It would be nice to have a little language to help us write the protocols for how the movement detectors send/receive messages to/from the other devices and people in the network.''
This kind of wishful thinking can lead to a domain-specific programming language, in particular, a top-down domain-specific programming language.
(IMPORTANT: "little languages" are used by software engineers, and there is less-or-no need for a "GUI for dummies" that acts as a translator into the little language.)
Let's review some of the languages mentioned earlier.
Here is an example from http://en.wikipedia.org/wiki/SQL:
SELECT Book.title AS Title, COUNT(*) AS Authors FROM Book JOIN Book_author ON Book.isbn = Book_author.isbn GROUP BY Book.title;Example output might resemble the following:
Title Authors ---------------------- ------- SQL Examples and Guide 4 The Joy of SQL 1 An Introduction to SQL 2 Pitfalls of SQL 1
[X,Y] = meshgrid(-10:0.25:10,-10:0.25:10); f = sinc(sqrt((X/pi).^2+(Y/pi).^2)); mesh(X,Y,f); axis([-10 10 -10 10 -0.3 1]) xlabel('{\bfx}') ylabel('{\bfy}') zlabel('{\bfsinc} ({\bfR})') hidden offThis code produces a wireframe 3D plot of the two-dimensional unnormalized sinc function:
-- import std_logic from the IEEE library library IEEE; use IEEE.std_logic_1164.all; entity ANDGATE is port ( I1 : in std_logic; I2 : in std_logic; O : out std_logic); end entity ANDGATE; architecture RTL of ANDGATE is begin O <= I1 and I2; end architecture RTL;
A program in Excel is written with the help of an IDE, and a program is in fact a sequence of menu selections, textual formulas, and data entries into cells --- a program doesn't have to be a script. (Excel's IDE translates the mouse movements into an internal DSPL.)
This diagram comes from http://en.wikipedia.org/wiki/MicrosoftExcel:
Here is a fragment of a PLY ("Python-Lex-Yacc") program:
# GRAMMAR RULE TO IMPLEMENT:
# C ::= L = E | if E : CL1 else CL2 end | print L
#
# FORMS OF OUTPUT PARSE TREES:
# CTREE ::= ["=", LTREE, ETREE] | ["if", ETREE, CLIST, CLIST] | ["print", LTREE]
#
# THE PLY CODE FOR THE ABOVE:
def pCommand1(c):
'''Command : LefthandSide '=' Expression'''
c[0] = ["=", c[1], c[3]]
def pCommand2(c):
'''Command : if Expression ':' CommandList else CommandList end '''
c[0] = ["if", c[2], c[4], c[6]]
def pCommand3(c):
'''Command : print Expression'''
c[0] = ["print", c[2]]
You can write an HTML script by hand, e.g,
<html><body>
HTML lets a user format a web document in terms of paragraphs, lists,
and fonts.
<p>
Use the <tt>View/PageSource</tt> menu option on your
web browser to see this chapter's HTML coding.
Here is a <a href="http://www.w3schools.com/htmL/">tutorial</a>.
</p></body></html>
Or, you use a web-page IDE to select, drag-and-drop, and type the components
of an HTML program. (The IDE translates your mouse and keyboard actions
into a script.) Of course, the "virtual machine" for the HTML language is a web browser!
CSS is another little language, used with HTML, to set default layouts, fonts, and colors for an HTML file.
There is one critical standard for the success of a top-down DSPL:
Upon first hearing, it sounds like top-down DSPLs are wonderful --- a language for just my problem that lets me say exactly what I want! --- but in reality, a top-down DSPL is a ``mixed bag'' of assets and drawbacks:
The starting point is this: become an expert in the problem domain. (This might take years!)
Learn the domain's vocabulary --- its nouns, verbs, and adjectives. As best as you can, define the domain's DSL. Develop many scenarios (case studies) and build lots of systems. Extract from the scenarios and code the patterns of data, features, control, and assembly linkages. Associate these with the DSL (each programming construction should be some concept from the DSL!) and extract the appropriate DSPL to implement.
There is one last question:
This is critical: the concepts expressed within the DSL must be natural and comprehensible to the language's users.
The language must be friendly towards these persons' views of the domain. If the top-down DSPL is for non-expert programmers (e.g., like Excel or HTML), then you must de-emphasize and even dispense with procedural/imperative programming notions like assignments, loops, and data-structures. You must use classic definition-style, concepts, like equations, arithmetic-style operations, and Prolog-style predicates --- these are common in other areas of science and technology.
Most non-experts have difficulty with control structures of any form --- sequencing is about the most they can handle. Repetition is often an impossible challenge to these people.
Data structures must be kept simple, resembling real-life, physical structures (a sheet of graph paper, a chest of drawers, a filing cabinet, a dictionary) or resembling the structures that are fundamental to the problem domain (hallways, buildings, wiring bundles...).
Keep this directive in mind, always:
If the DSPL's users are forced to code in notation and concepts that lie outside their problem domain, the users will get lost. (That's why non-programmers don't use Java as a DSPL for spreadsheet building!) In a serious development effort, you will design an IDE-like tool as well.
The DSL talks about ``gates'', which have features like input connections (ports/wires) and output connections. Gates have a function (feature): AND, OR, NOT, etc. Gates are assembled into subassemblies (e.g., one can build an XOR-subsassembly), and subassemblies must be connected and grouped on a board. Gates and subassembles might be annotated with power and space requirements.
The DSL is not event-or-action driven, like an alarm system or GUI, so scenarios are more like puzzles:
IN1 IN2 | OUT1 OUT2 ---------------------- 1 1 | 1 0 1 0 | 1 1 0 1 | 1 1 0 0 | 0 0
We might design a language whose scripts handle scenarios like the ones
above, e.g., the first scenario is programmed like this:
ASSEMBLY A1 : inputs IN1, IN2; outputs OUT1.
w1 = AND(w1, w2).
w2 = OR(w1, IN2).
OUT1 = NOT(w2).
ENDASSEMBLY
The connections are coded as equations, where the left-hand side of
each equation is the name of a wire.
The second scenario might be programmed like this:
ASSEMBLE A2 : inputs IN1, IN2; outputs OUT1, OUT2.
solve for 1 1 1 0;
1 0 1 1;
0 1 1 1;
0 0 0 0.
using {NAND}
ENDASSEMBLY
The new operation, solve for _ using {_}, accepts the tabular input and generates
a solution that is named A2 that has the required functionality and behavior.
The third might go like this:
ASSEMBLY A3 : inputs IN1; outputs OUT1;
suchthat w2.voltage < (2 mv).
w1 = AND(In1, w2).
w2 = NOT(w1)
OUT1 = w2
ENDASSEMBLY
Here, the constraint on a wire's voltage is listed as part of the assembly's
output specification. The user does not code how to solve the constraint ---
the implementation knows the details.
At this point, think about what operations you might add to this little language that connects assemblies like A1 to A2 or A3 --- you will want a little linking language, and it should probably look like equations that "equate" inputs to outputs. Try it.
Of course, there are many hardware languages that can do the above and then some.
Next, consider how a parser and interpreter would be defined to read programs in this DSPL and execute them (in this case, generate circuit-diagram layouts for a board). Think about how a GUI might help a programmer "draw out" the DSPL programs, rather than typing them as script.
Indeed, what is a program? Is it
Software engineers use development tools, such as text editors, IDEs, and debuggers, for inputting their programs, interacting with the IDE, choosing menu options and completing templates until the IDE announces that a program is completed. What goes on within the IDE? Does it build a sequence of text lines? (No --- see below.)
Users of top-down DSPLs are even more ``IDE-dependent'' than software engineers. For example, an Excel user will use Excel's GUI to insert data into cells of a spreadsheet and write equations that are embedded into the spreadsheet's ``logic'' so that row-and-column totals are correctly computed. Exactly what is the ``program''? Indeed, the ''program'' is completely intertwined with the Excel development environment and its internal data structures and event handlers.
In a note at
http://martinfowler.com/articles/languageWorkbench.html Martin Fowler
coins the term, ``projecting editor'' for a DSPL that is programmed within an
IDE:
The projecting editor keeps an abstract representation
of the user's "program" that is filled in bit by bit, not necessarily sequentially,
not necessarily as script. (The "program" is data structures, event handlers,
and GUIs held within the projecting editor!)
The classic abstract representation of a program is
a parse tree whose nodes are annotated with semantic info, plus a
global symbol table, along with event handlers and support libraries.
IMPORTANT: The projecting editor must have a back-end that can interpret the abstract representation or can generate a script ("target code") that can be interpreted. (The ``storage representation'' in the diagram is some file format that archives ("pickles") the abstract representation at the end of the IDE session.)
If you are an Emacs or Eclipse or a Visual Studio user, you are using a projecting editor/DSPL for document generation.
If you are developing a top-down DSPL for non-programmer users, then you are almost certainly forced to develop a projecting editor to go with it.
There is an alternative: Maybe we can "train" the computer day-by-day, teaching it the DSL in stages. We saw this approach in the previous chapter on logic programming: Starting from the library database written in Prolog, we added definitions that teach the computer what an overdue item is, what a fine is, and so on. The librarian starts Prolog, loads the definitions, and talks to the computer by using our definitions. Over time, we include enough library-DSL definitions so that the librarian talks to the computer exclusively in terms of "library-DSL".
The general principle goes like this: an experienced programmer wants to ``extend'' a general-purpose, host language with concepts specific to a problem domain. The programmer codes the DSPL concepts in the host language as procedures, classes, macros, etc., and writes programs that are a mix of host-language code and the codings of the DSPL. Over time, more and more DSPL constructions are added; the host language is "covered over" by the DSPL; and programs are written (almost) entirely in the DSPL. We have extended the host language with the DSPL.
This is called a bottom-up (or ``internal'') DSPL.
A framework is a library of software components that help someone implement solutions in a problem domain. (Think of javax.swing or a library from .NET.) Frameworks are ``not-quite bottom-up DSPLs.'' (We will explain the remark later.)
Consider these libraries for GUI-building:
These libraries are called frameworks, because each has their own collection of components that implement nouns (''window,'' ''frame,'' ''button,'' ''layout,'' ...) and verbs (''setTitle,'' ''getText,'' ''paint,'' ...) of the GUI domain. They usually come with sample programs that suggest patterns for assembling and calling the components. But they are implemented in their host languages, and a programmer must write (lots of) code in the host language to assemble a working GUI from the GUI framework. (Often, an IDE helps generate the host-language code --- for example, use Visual Studio to drag-and-drop a GUI and then read the hundreds of lines of C# code that it generates.)
A GUI framework is an "almost-DSPL" for GUIs, because it is a library that implements GUI concepts, but there is no ``programming language'' for GUI building, only the components and some example assemblies, where the assemblies are written in host-language code.
An application is a mixture of components from the GUI-framework, calls to the GUI-framework, and coding written from scratch in the host language.
A GUI framework is often ``married'' to its host language by a visual editor, e.g., Visual Basic, Visual C++, Visual Studio, and Eclipse. The visual editor tries to fill the gap between the framework and host language. This usually isn't enough, because there is no language for GUI building, only bits and pieces.
If you build GUIs for a living, you will not be satisfied with just a GUI framework and an IDE --- you will develop and use design patterns, macros, templates, custom components, and shortcut-code that reduce your time and effort. You are developing your own little language, your own bottom-up DSPL, for GUI building.
Experienced programmers naturally become bottom-up DSPL designers, because over time they assemble a library of that express domain concepts that they use over and over to solve problems.
Eventually, the programs these people write consist almost totally of code from their own library. The host programming language --- when it is used at all --- acts merely as minimal ``glue code'' for assembling components and templates and patterns.
At this point, the host language plus the library of custom constructions is a bottom-up DSPL, because the library has become ``more important'' to the problem solving than the host language itself. What has happened is this:
The custom-written library is coded in the host language, and it is oriented towards encoding ``domain-concepts-as-code'' (nouns as data-structure patterns, verbs as operations/control-structure patterns, features as attributes, sentences and paragraphs as assembly/design patterns) so that scenarios discussed in the DSL are readily converted to code. Experienced programmers have good instincts for coding domain concepts as code and saving them as libraries. It is almost a matter of survival --- there is never enough time to build a new solution completely from scratch!
Many ideas from object-oriented design and design patterns apply to bottom-up DSPL development: classes, methods, templates, and design patterns implement domain concepts. You code them, save them, reuse them --- you have a language.
Languages like Scheme (via lambda abstraction and hygienic macros) and Smalltalk and Ruby (via blocks and macros) let a programmer easily define design patterns as custom templates directly in source-code syntax. The Scala language even lets you alter its own parser so that it can be extended to parse your new syntax patterns! These are ways of extending the host language upwards towards the application domain.
But any general-purpose language can serve as a host language. Usually the host language is whatever language in which the starting libraries and frameworks are written.
A bottom-up DSPL has its strengths and weaknesses also:
You also have several completely coded software implementations of networked alarm systems. But each time you design and install a new system for a new customer, you must study earlier implementations and copy-and-paste code and write new code.
At this point, you have a framework for alarm systems --- lots of useful pieces, several completed assemblies, but no "language" for talking about the "big picture", namely, no computer language for mapping a design into a complete system of multiple hardware and software devices.
You must "computerize" more of the work that you are currently doing and redoing by hand. To do this, you look at the systems you've already built and study how your designs were hand-coded into the implementations. You look for standard patterns (design patterns!) and you look for variations (parameters) in the patterns. You extract code templates that correspond to the patterns and you name them.
You also study the specifications you developed when you met with your clients and you convert as many of the specifications' nouns and verbs of the Domain Language (DSL) into computer code for those nouns and verbs --- that is, you are training the computer to understand the same DSL that you and the client understand.
You implement the design patterns, the templates, the Domain Language, with macroprocessors, with custom components that use eval/exec, and with simple parser-translator tools. (These techniques are described in the remainder of this chapter.) You extend/lift-up the host language to include as much of the Domain Language as possible.
Later in the chapter, there are two small examples that attempt to illustrate this approach.
Experienced programmers are the natural users of a bottom-up DSPL because they design it themselves, over time, as a library of implemented components and patterns, meant to represent how the computer understands the nouns and verbs and features of the DSL. Eventually the host programming language acts merely as ``glue'' for linking/assembling the library's templates and patterns and components --- The programmer has extended the host language ``upwards'' towards the problems to be solved.
Bottom-up design might go like this:
Your goal is to make the bottom-up-DSPL library ``stand-alone,'' so that you write programs just with your library and with almost zero new code from the host language. This means you use the host language only as a minimal glue language for your library, and as as an ``interface language'' to contact external components that you have not written, and in rare cases to ``escape'' from the DSPL to execute host-language code.
Implicit in the previous paragraphs are the notions of framework and product line from mainstream Software Engineering:
The programmer can modify an appropriate skeleton and fill in the gaps with a mix of framework code and custom-written code. GUI libraries, client-server libraries, and protocol libraries are almost always organized as frameworks. You can find some Python-coded frameworks for mail servers and networking at http://effbot.org/librarybook/.
For example, consider a product line of cars all based on the same engine-chassis assembly. Now, consider the control software for the varieties of engine installed in the cars. The software is basically the same for all the engine variations, and each engine controller is generated from a standard program that is customized by the software ``features'' for the specific engine.
Another example is Notepad/Wordpad/Word, which are based on the same word-processor structure but have different degrees of customizations/features for font choices, formatting, and file formats.
A product line of software is built from a library and one single program skeleton; the gaps in the skeleton are filled by pre-coded features from the library. This is a kind of bottom-up DSPL, where the programs and their feature selections are tightly restricted.
When you implement template or design patterns as new constructions, you want to have a nice syntax to call them. Ruby, has a built-in "macro processor" for defining new syntax patterns that extend Ruby's syntax. Scala has an ML-like pattern language that lets you update the Scala parser to handle new syntax constructions. Other languages are less helpful, and you might be forced to write a parser-translator that translates your nice syntax of patterns into host-language code.
Here are some possible combinations to use:
Host language | Pattern language that links to host |
Java | Scala (provides ML-like front end) or Groovy (provides Python-like front end) |
C# | F# (provides ML-like front end) |
Python | Python's re module (provides macroprocessor) |
C | GPP or m4 (macroprocessors) |
Unlike "little languages" (top-down DSPLs), bottom-up DSPLs are "big", because they start with a host language and a framework and get bigger and bigger with extensions until the DSPL library is completed. So, it is difficult to give simple examples of bottom-up DSPLs. But we will try.
Say that you use Tkinter to program lots of Python GUIs that are grids. The grids always turn out to be matrices of buttons that look and behave the same. (Spreadsheets and game boards work like this!) This means you are copying-and-pasting many patterns of definition, assembly, and control from existing applications to new ones. It would be much better to code the patterns as classes, functions, templates, and macros that are inserted into your Tkinter programs. The resulting programs would be easier to code and read and would work reliably (because your patterns are implemented correctly once and for all).
There isn't time or space here to present lots of grid-GUIs,
but here's one, a game board, where some common grid-GUI coding patterns
are marked by #n ****.
===================================================
#Game board for "Pente" game:
from Tkinter import *
import PenteBoard # the model subassembly --- holds the gameboard data
### the CONTROLLER module --- this should be placed in a separate file.
#1 ***********************************
def makeHandler(myrow, mycolumn) :
"""makeHandler constructs a handler function for a new button.
parameters:
myrow - an int, the row coordinate where the new button lives
mycolumn - an int, the column coordinate where the new button lives
returns: the handler function customized for a new button
"""
def handleButtonPress() :
"""handleButtonPress is the constructed handler function.
It makes the move for the human who pressed
this button (which is at position myrow,mycolumn).
The updated board is then painted.
"""
if PenteBoard.game_on() :
PenteBoard.makeMove(myrow, mycolumn)
repaintGUI()
return handleButtonPress
# END ***********************************
### the VIEW module starts here:
def repaintGUI() :
"""repaintGUI repaints the foreground text of all the buttons on the GUI,
it also updates the displayed count of captures, and if there is a
winner, it prints a message as to who won.
"""
global buttons, label1
#2 ****************************
for i in range(size) :
for j in range(size) :
buttons[i][j].configure(text = PenteBoard.contents(i,j))
buttons[i][j].configure(bg = "white")
#END *****************************
label1.configure(text = "Your captures " + " = " \
+ str(PenteBoard.getCaptures()))
#3 *********************************
window = Tk()
window.title("Pente")
size = PenteBoard.size
window.geometry(str(50 * size) + "x" + str(50 * (size)))
frame = Frame(window)
frame.grid()
#END *********************************
label1 = Label(frame,
text = "Captures " + " = " \
+ str(PenteBoard.getCaptures()),
font=("Arial", 12, "bold") )
label1.grid(row = 0, column = 0, columnspan = 5)
#4 **********************************
buttons = [] # a nested list that remembers addresses of all button objects
for i in range(size) :
button_row = []
for j in range(size) :
button = Button(frame,
font = ("Arial", 14, "bold"), fg = "blue", bg = "white",
width = 2, height = 1)
button.configure(text = PenteBoard.contents(i,j))
button.configure(command = makeHandler(i, j))
button.grid(row = i+2, column = j)
button_row = button_row + [button]
buttons.append(button_row)
#END*********************************
window.mainloop() # activate GUI
===================================================
There's a lot of ugly code here, and an IDE will not help you avoid
the tedious coding of the nested loops for initializing and resetting
the button grids. The event handlers must also be
manually coded. The numbered patterns seen above are simple Domain concepts:
Say we use a macroprocessor to define nice syntax names for the four
patterns.
The above code is simplified to the following, where the
macros are prefixed by @-signs:
===================================================
from Tkinter import *
import PenteBoard # the model subassembly
def repaintGUI() :
"""repaintGUI repaints the foreground text of all the buttons on the GUI,
it also updates the displayed count of captures.
"""
global label1
@repaintGrid from (PenteBoard)
label1.configure(text = "Your captures " + " = " \
+ str(PenteBoard.getCaptures()))
window, frame = @initializeFrame("Pente", PenteBoard)
label1 = Label(frame,
text = "Captures " + " = " \
+ str(PenteBoard.getCaptures()),
font=("Arial", 12, "bold") )
label1.grid(row = 0, column = 0, columnspan = 5)
@configureGrid from (PenteBoard, frame)
handler (lambda(myrow, mycolumn) =>
if PenteBoard.game_on() :
PenteBoard.makeMove(myrow, mycolumn);
repaintGUI() ) # THE HANDLER IS DEFINED AS CLOSURE CODE
and (font = ("Arial", 14, "bold"),
fg = "blue", bg = "white",
width = 2, height = 1)
window.mainloop()
===================================================
We have a more readable mix of GUI-domain concepts and host-language code.
Here are the macro patterns that were used above:
global buttons size = MODEL.getSize() for i in range(size) : for j in range(size) : buttons[i][j].configure(text = MODEL.contents(i,j)) buttons[i][j].configure(bg = "white")
window = Tk() window.title(TITLE) size = MODEL.getSize() window.geometry(str(50 * size) + "x" + str(50 * (size))) frame = Frame(window) frame.grid()
buttons = [] size = MODEL.getSize() for i in range(size) : button_row = [] for j in range(size) : button = Button(frame, ATTRIBUTES) button.configure(text = MODEL.contents(i,j)) button.configure(command = HANDLER(i, j)) button.grid(row = i+2, column = j) button_row = button_row + [button] buttons.append(button_row)
Over time, more and more components and patterns will be named, saved, and reusued. The GUI programs will call more and more of the saved DSPL concepts and less and less of new code. A bottom-up DSPL evolves. We will learn in a future section how to use a macroprocessor to declare and call the above macro patterns.
Programmers who use object languages use design patterns that help them assemble systems faster. Say that you develop a lot of blackboard-style systems where the central database (``model'' or ``entity object'') must be monitored and/or displayed by actors or GUI widgets (``observers'') and contacted each time the model is updated. The Observer Design Pattern is a well-known design pattern for this situation. If you use it a lot, then it is a concept in your DSL, and you should define it as a "template" and add it to your DSPL library.
Here is one version of the Observer Design Pattern, used in Java programming:
Rather than recode the assembly each time, we define this syntax,
a named macro pattern, @observed, that returns a handle to the Observable object
that anchors the design pattern:
Observable omodel = @observed (SUBJECT) by (OBSERVER LIST);
The macro expands to code that declares the Observer-related event,
allocates the Observable wrapper object,
registers the OBSERVER LIST, and returns the handle that the rest of the
system uses for contacting the
SUBJECT:
===================================================
# declare a new event type, subjectUpdated, and bind it to its event handler:
public Observer event/delegate subjectUpdated;
# code for constructing wrapper object:
class Observable {
ConcreteSubject model;
Observer[] registered;
public Observable(m, olist) {
model = m; registered = olist
foreach (obs in registered) { obs.setSubject(model);
subjectUpdated.register(ob.handle); }
}
public setState(...) { model.SetState(...); signal subjectUpdated; }
public getState() { return model.getState(); }
}
# return handle to observable object:
return new Observable(SUBJECT, OBSERVER LIST);
===================================================
The above code is simplified a bit (it isn't strict Java or C#), but the idea is clear.
Now, the controllers that contact the model do so by calling
omodel.setState(...), which triggers the update to the SUBJECT
and an event broadcast that activates the handle method
of each observer.
The macro code above is more than one method or class --- it is components, declarations, and executed code; it is a subassembly pattern, a template, an extension of what Java/C# provides; it is a coded, reusable DSPL concept.
We might say that a DSPL is ``mostly top-down'' if it is designed to express DSL scenarios-in-code and has its own parser (or IDE editor) and interpreter/translator.
A mostly-top-down DSPL can appear like this: You use some framework or component library to build systems, and you develop insight about what the "dream language" (the DSL language!) truly is for writing the algorithms you regularly implement with host-language code and library calls.
So, you design the dream language: You write grammar rules for the syntax and you write semantic equations that map syntax into host language code and library calls. You build the translator. In this way, you have built a bridge from the DSL, at the top of your thinking, mostly downwards to the frameworks below.
A danger of any top-down DSPL is that it is isolated from other systems and implementations. Your mostly-top-down DSPL should let you call library components and execute code written in the implementation language. To do this, add a ``trap door'' to the DSPL so that the execution of the DSPL program can be paused and the implementation-language code can be executed instead. Many scripting languages provide such a trap door, in the guise of an eval operation, which takes as its argument a string that holds executable code --- eval runs the code. Here are three useful forms of trap door in Python:
Here is a program that builds a string and runs it:
x = 2; y = 3; z = 5
invar = raw_input("Type name of variable (x, y, or z) to zero out: ")
if invar in ("x", "y", "z") :
code = invar + " = 0"
else :
code = "pass"
exec(code)
The exec command can also read and execute the contents of an opened text file:
handleToCodefile = open("MyPythonProgram.py", "r") # open a readable file
exec(handleToCodefile) # execute its contents
import os cwd = os.getcwd() # get current working directory if os.path.basename(cwd) == "MyPictures": # is the lowest-level dir "MyPictures" ? # then, move up one level to parent directory: os.chdir(os.pardir) print "Current path is ", os.getcwd() os.system("ls -a") # ANY OS command can be supplied as a string arg
# run an external program from within Python code: import subprocess # general format: subprocess.call(["program-name", "param1", "param2", ...]) subprocess.call(["C:/Python26/Python.exe", "MyPythonPgm.py"])
A DSPL is ``mostly bottom-up'' if it is developed as layers of host-language-coded components and macro-coded patterns that help model the problem domain. Perhaps the layers of components do not express directly and immediately the DSL --- there is still a "gap" between the code solutions and the solutions described in scenarios.
To close the gap, we add customized control- or linking-patterns that express the missing domain concepts, so that the concepts look like they are built into the host language. (In particular, we want to avoid writing ugly dot-notation, like packageName.objectName.methodName(arg1, arg2, ...), each time we use a custom-coded domain concept/pattern.)
A good host language will give you a technique to add custom patterns. Here is a simple example:
Say that your problem domain has lots of solutions that use the
phrase, ``repeat ACTION until CONDITION holds'' so that
this pattern should be added to the DSPL
library. Some languages let you define higher-order functions
(functions that take code/closures as parameters) in mix-fix
keyword notation, like this:
def repeat(action)until(condition)holds :
"""executes the command, action, until expression, condition, is true"""
action() # do the action step
if condition(): # finished ?
return
else: # do it again:
repeat(action)until(condition)holds
This defines a function named, repeat..action..until.
The function is used in a program like this:
...
repeat([x = x - 1])until([x == 0])end
...
The brackets, [..], are quoting the code .., that is,
constructing a closure holding the code.
Functional languages, like Scheme and Haskell, support this approach,
as do Ruby and Smalltalk to a lesser degree.
For older programming languages, the traditional way to add custom control structures is with a macro processor (``preprocessor''). A macro processor is a program that reads as input a program in the host language that has the custom structures mixed into the code. The macro processor locates the occurrences of the custom structures and replaces them with the instructions in the host language that perform the intended operations.
C's preprocessor is a standard but not-too-exciting example. A segment of C code like this,
#define PI 3.14159
#define Double(x) (x + x)
// now, PI and Double act like they are built-in C functions:
y = Double(PI * 5) ;
defines two macros, PI and double, which look like functions and
can be called like functions.
When the above code
is input to C's preprocessor, this text is the output:
y = (3.14159 * 5 + 3.14159 * 5) ;
The macro definitions are removed, and the calls are replaced by
C-text, giving a program in pure C.
When a macro is called, its arguments are text and not computed values! At a macro call, the text argument is bound to the parameter and the text is inserted for occurrences of the parameter in the macro body. The text computed by the macro's body is copied back in place ofthe macro call. In the example, y = Double(PI * 5) is rewritten to y = (PI * 5 + PI * 5), which is rewritten to y = (3.14159 * 5 + PI * 5), which is rewritten to y = (3.14159 * 5 + 3.14159 * 5). The example shows why the macro processor must be a separate program, run first, before the parser, interpreter or translator. There is a preprocessor, called GPP, that can be used stand-alone to process any program that contains C-like macros. Like C's preprocessor, GPP requires that a macro call look like a function call, of the form, MACRONAME(ARG1, ... ARGn). The m4 macroprocessor lets its user write macro definitions whose calls look somewhat like the mix-fix notation seen in the previous repeat..until..holds example.
In a future section, we will see how to use a language's regular-expression library to code a simple but useful macro processor.
Here are some references for existing macro processors:
Ruby supports a ``block'' construction (the [..] syntax) that makes it possible to code simple customized control structures directly in Ruby. There are some Ruby-implementation approaches at http://weblog.jamisbuck.org/2006/4/20/writing-domain-specific-languages
If the hardware language is not expressive enough, or it is limited in space and speed, you must protoype the top-down DSPL interpreter in a different language and then convert the interpreter into a compiler that translates into the hardware language. Do this as a last resort, since compiler development and maintenance are expensive tasks.
In the case of a bottom-up DSPL, you should select a host language that either (i) is directly understood by the hardware or (ii) has an efficient compiler from the host language to the hardware language. In all cases, the chosen host language must support components and libraries, so that you can extend the host language bottom up.
Although it is almost never done, it is an excellent project to implement a DSPL one way and then use the acquired knowledge to implement it the ``inverse way.'' That is, if you designed a DSPL top-down, try to extract from its interpreter the parts that become components for a bottom-up implementation. Dually, if you first built a bottom-up DSPL, then next use the components as the ``logic'' within a top-down, interpreter implementation. The second version of the language might be the one that you prefer!
If you have designed a top-down DSPL, you should add a ``trap door'' so that code in the implementation language can be embedded in the programs you write. The simplest way to do this is to use an implementation language that has an eval/exec operation.
Here is a small example. Perhaps you have designed a game-app for
a cell phone, where a child can tell birds to eat bugs. The game
has a GUI front-end, but the mouse moves and clicks on the GUI generate
code in this syntax:
===================================================
CL : CommandList A : Atom
C : Command S : String
CL ::= C | C . CL
C ::= A1 eats A2 | do S
A ::= bird | bug
S is a quoted string
===================================================
An example program that the GUI might generate is
bird eats bug.
bird eats bug.
bug eats bird
The game has limited functionality (haha), but notice the
do command, which is a trap door that lets a programmer
insert Python code that directly manipulates the language's interpreter,
say, like this:
bird eats bug.
do "census['cat'] = 1\ncensus['bird'] = 0\nprint 'uh oh!'"
The string holds Python code:
census['cat'] = 1
census['bird'] = 0
print 'uh oh!'
Here is the interpreter for the bird-cage language:
===================================================
"""Interpreter for mini top-down DSL for bird-cage domain of birds and bugs.
Includes trap-door operation, do S, for embedding Python source code.
Source language syntax to be parsed:
CL : CommandList A : Atom
C : Command S : String
CL ::= C | C . CL
C ::= A1 eats A2 | do S
A ::= bird | bug
S is a quoted string
Operator-tree structures resulting from the parser:
CLIST ::= [ C* ]
CTREE ::= ["eat", A1, A2 ] | ["do", S]
A ::= "bird" | "bug"
S ::= a quoted string
"""
# Global variable: remembers count of entities in bird cage:
census = {"bird": 9, "bug": 99}
def interpretCLIST(p) :
"""interprets CLIST p"""
for command in p :
interpretCTREE(command)
def interpretCTREE(c) :
"""interprets CTREE c"""
operator = c[0]
if operator == "eat" :
eater = c[1]
lunch = c[2]
if census[eater] > 0 and census[lunch] > 0 :
census[lunch] = census[lunch] - 1
elif operator == "do" : # trap-door ``eval'' operation ---
exec(c[1]) # executes c[1] as python code. Can affect census,
# add new global variables to interpreter's namespace,
# print trace information, etc.
else :
crash("invalid command")
def crash(message) :
print message + "! crash! core dump: ", census
raise Exception
def main(program) :
"""interprets the operator tree, program"""
interpretCLIST(program)
print "final census =", census
===================================================
Here are some sample uses of the interpreter:
python -i top.py
>>> main([["eat", "bird", "bug"]])
final census = {'bird': 9, 'bug': 98}
>>> main([["eat", "bird", "bug"], ["do", "census['cat'] = 1\ncensus['bird'] = 0\nprint 'uh oh!'"]])
uh oh!
final census = {'bird': 0, 'bug': 97, 'cat': 1}
The do command lets a programmer escape from the limited functionality
of the DSPL and use the operations of the implementation language.
If you have developed a bottom-up DSPL, you should also define control-structure patterns and linking patterns for your DSPL library. It is always best to use host-language facilities to do this.
Some host languages (e.g., Scheme and C) come with their own macro processors. Others (e.g., Smalltalk and Ruby) have flexible procedure-call syntax for defining new patterns. Others (e.g., Perl, PHP, Python, Ruby) supply regular-expression libraries that have powerful pattern-matching operations that you can use to write your own macro processor.
Here is an example of using regular-expression string matching in Python. We use Python's
regular-expression module, re, to define a pattern, match
the pattern in a string, and replace it.
The comments in the code explain how this operates:
===================================================
import re # re is the module of regular-expression operations
# Here is a pattern that matches strings of form,
# @DOUBLE alpha END
# where alpha is some substring that holds no occurrences of @ :
# "(\\s*)@DOUBLE\\b([^@]*?)\\bEND\\b"
# where
# \\s means a whitespace character
# \\b means a word boundary
# E* means match E zero or more times as much as possible for success
# E*? means match E zero or more times as little as possible for success
# [^c] means match any character that is NOT character c
# The parens mark _groups_ that are used below.
# p is a string-matching object compiled from the pattern string:
p = re.compile("(\\s*)@DOUBLE\\b([^@]*?)\\bEND\\b")
# try this multi-line example:
source = """
x = 0
x = @DOUBLE x END
print x
"""
print "source text ="
print source
# search for compiled pattern p in source:
m = p.search(source)
print
# if the match succeeds, m is an object; else m = None
print "match result =", m
# m holds a list of substrings that matched parenthesized groups in the pattern:
print "matched groups =", m.groups()
# m also holds the start and end indexes of the matched string:
print "span of matched text =", m.span()
# the start and end indexes can be referenced individually, too:
print "matched text =", source[m.start() : m.end()]
print
# let's replace the matched string by something else:
matches = m.groups()
source = source[:m.start()] \
+ matches[0] + "(2 * " + matches[1] + ")" \
+ source[m.end():]
print "updated text ="
print source
# We have completed a simple macro-expansion of "!DOUBLE alpha END"
# into "(2 * alpha )", preserving any leading spacing
===================================================
Here is the output from the above script:
source text =
x = 0
x = @DOUBLE x END
print x
match result = <_sre.SRE_Match object at 0x7ff3d6e0>
matched groups = (' ', ' x ')
span of matched text = (10, 25)
matched text = @DOUBLE x END
updated text =
x = 0
x = (2 * x )
print x
The example shows that patterns can be complex. There is a tutorial
on writing patterns at
http://docs.python.org/howto/regex.html
and there is a mostly complete listing of pattern options at
http://docs.python.org/library/re.html.
We now use the ideas in the example to write
a macroprocessor in Python that searches for macro-call
patterns and replaces them with expansions. Here are the two
macro calls the processor will perform:
@REPEAT Code FOR Expr TIMES ===> newvar = Expr
while newvar > 0 :
Code
newvar = newvar - 1
@DOUBLE Expr END ===> ((Expr) * 2)
Each macro call on the left is coded as a pattern string,
and each translation is done by a Python-coded handler function.
The macro processor's main data structure is a list of
(compiled-pattern, handler-function) pairs.
Here is the macro processor:
===================================================
"""Simplistic macroprocessor based on regular expressions.
main data structure:
macrotable : list of (COMPILED_PATTERN, HANDLER) pairs
Example:
macrotable = [ (re.compile("(\\s*)@REPEAT\\b(\\s*)([^@]*?)\\bFOR\\b([^@]*?)\\bTIMES\\b")
translateREPEAT),
(re.compile("@DOUBLE\\b([^@]*?)\\bEND\\b"), translateDOUBLE) ]
holds these two macro definitions:
indent1 @REPEAT indent2 alpha FOR beta TIMES
=> translateREPEAT(indent1,indent2,alpha,beta)
@DOUBLE alpha END => translateDOUBLE(alpha,)
Compiled patterns, as written above, match macro-call symbol, @,
followed by keywords (which are required to be separate words by \\b )
such that included text arguments do not include any call symbols, @.
Note that E*? denotes the minimal match of E* such that
the overall pattern match succeeds. Thus, the macro processor computes
inside-out processing of macro calls so that nested calls are never confused.
The pattern for @REPEAT also records the amount of indentations via (\\s*).
Macro-processor algorithm:
read source
repeat until no more macro matches:
search source for each compiled pattern in macrotable
if successful match,
then call accompanying handler function,
which assembles appropriate translation
insert translation in place of matched pattern in source
write source
"""
### This portion should be a separate module that holds the translation
### functions. It is embedded here for simplicity.
#GENSYM function:
var_count = 0 # count of new names generated for expanded macros
def genNewVar() :
"""genNewVar is a gensym function, generating unique new names
returns: a string of form, "_varN", where N is a unique nonneg int
"""
global var_count
newvar = "_var" + str(var_count)
var_count = var_count + 1
return newvar
def translateREPEAT(args) :
"""translateREPEAT expands this macro call:
indent1 @REPEAT
indent2 Code
FOR Expr TIMES =into=> indent1 newvar = Expr
indent1 while newvar > 0 :
indent2 Code
indent2 newvar = newvar - 1
where indent1 = args[0] and indent2 = args[1]
Code = args[2] and Expr = args[3]
(indent1 and indent2 are leading white-space)
returns: ans, a string holding the macro-expanded call
"""
indent1 = args[0]
indent2 = args[1]
bodycode = args[2]
exprcode = args[3]
# the call to REPEAT is replaced by this python code, as documented above:
newvar = genNewVar()
ans = indent1 + newvar + " = " + exprcode \
+ indent1 + "while " + newvar + " > 0:" \
+ indent2 + bodycode \
+ indent2 + newvar + " = " + newvar + " - 1"
return ans
def translateDOUBLE(arg) :
"""translates @DOUBLE(arg,) =into=> '((arg) * 2)' """
ans = "((" + arg[0] + ") * 2)"
return ans
### END OF HANDLER-FUNCTION MODULE
### MACRO PROCESSOR CONTROL ALGORITHM:
import re # import regular-expression module
# initialize macrotable:
macrotable = [ (re.compile("(\\s*)@REPEAT\\b(\\s*)([^@]*?)\\bFOR\\b([^@]*?)\\bTIMES\\b"),
translateREPEAT),
(re.compile("@DOUBLE\\b([^@]*?)\\bEND\\b"), translateDOUBLE)
]
# read source:
import sys
if len(sys.argv) < 2 :
inputfilename = raw_input("Type input file to copy: ")
else :
inputfilename = sys.argv[1]
input = open(inputfilename, "r")
source = input.read()
input.close()
# replace all macro calls:
still_matching = True
while still_matching :
still_matching = False
for (pattern, handler) in macrotable :
match = pattern.search(source)
if match : # != None
replacement = handler(match.groups())
source = source[:match.start()] + replacement + source[match.end():]
still_matching = True
# write source:
index = inputfilename.find(".py")
outputfilename = inputfilename[:index] + "out" + ".py"
output = open(outputfilename, "w")
output.write(source)
output.close()
print
print "Contents of " + outputfilename + ":"
print source
===================================================
Say we have this file, test.py, whose contents are:
x = 0
@REPEAT
x = @DOUBLE x + 1 END
@REPEAT
pass
FOR 2 TIMES
FOR 3 TIMES
print x
When we use the macroprocessor to rewrite the file (python macrop.py test.py), we get this report:
Contents of testout.py:
x = 0
_var1 = 3
while _var1 > 0:
x = (( x + 1 ) * 2)
_var0 = 2
while _var0 > 0:
pass
_var0 = _var0 - 1
_var1 = _var1 - 1
print x
All macro calls are expanded.
The book shows how to apply macro/pattern/meta-programming tricks in Groovy (a Python/Ruby extension of Java) and Scala (an ML/Haskell extension of Java) to develop bottom-up DSLs. It also uses the ANTLR parser generator to help implement top-down DSLs.