Data Abstraction Concepts
H. Conrad Cunningham
15 June 2022
Browser Advisory: The HTML version of this textbook requires a browser that supports the display of MathML. A good choice as of June 2022 is a recent version of Firefox from Mozilla.
TODO: What are the goals of document/chapter?
As computing scientists and computer programmers, we should remember the maxim:
Simplicity is good; complexity is bad.
The most effective weapon that we have in the fight against complexity is abstraction. What is abstraction?
Abstraction is concentrating on the essentials and ignoring the details.
Sometimes abstraction is described as remembering the “what” and ignoring the “how”.
Large complex problems can only be made understandable by decomposing them into subproblems. Ideally, we should be able to solve each subproblem independently and then compose their solutions into a solution to the larger problem.
In programming, the subproblem solution is often represented by an abstraction expressed in a programming notation. From the outside, each abstraction should be simple and easy for programmers to use correctly. The programmers should only need to know the abstraction’s interface (i.e., some small number of assumptions necessary to use the abstraction correctly).
TODO: I am unsure about the use of the term “abstraction” in the above paragraph here and in ELIFP chapter 2.
Two kinds of abstraction are of interest to computing scientists: procedural abstraction and data abstraction.
In procedural abstraction, programmers focus primarily on the actions to be carried out and secondarily on the data to be processed.
For example, in the top-down design of a sequential algorithm, a programmer first identifies a sequence of actions to solve the problem without being overly concerned about how each action will be carried out.
If an action is simple, the programmer can code it directly using a sequence of programming language statements.
If an action is complex, the programmer can abstract the action into a subprogram (e.g., a procedure or function) in the programming language. The programmer must define the subprogram’s name, parameters, return value, effects, and assumptions—that is, define its interface. The programmer subsequently develops the subprogram using the same top-down design approach.
In data abstraction, programmers primarily focus on the problem’s data and secondarily on its actions. Programmers first identify the key data representations and develop the programs around those and the operations needed to create and update them.
TODO: Do we need to reintroduce the idea of module into the text above?
Generally we make the following distinctions among subprograms:
A procedure is (in its pure form) a subprogram that takes zero or more arguments but does not return a value. It is executed for its effects, such as changing values in a data structure within the program, modifying its reference or value-result arguments, or causing some effect outside the program (e.g., displaying text on the screen or reading from a file).
A function is (in its pure form) a subprogram that takes zero or more arguments and returns a value but that does not have other effects.
A method is a procedure or function often associated with an object or class in an object-oriented program. Some object-oriented languages use the metaphor of message-passing. A method is the feature of an object that receives a message. In an implementation, a method is typically a procedure or function associated with the (receiver) object; the object may be an implicit parameter of the method.
Of course, the features of various programming languages and usual practices for their use may not follow the above pure distinctions. For example, a language may not distinguish between procedures and functions. One term or another may be used for all subprograms. Procedures may return values. Functions may have side effects. Functions may return multiple values. The same subprogram can sometimes be called either as a function or procedure.
Nevertheless, it is good practice to maintain the distinction between functions and procedures for most cases in software design and programming.
In Haskell, the primary unit of procedural abstraction is the pure
function. Their definitions may be nested within other functions.
Haskell also groups functions and other declarations into a program unit
called a module. A
module
explicitly exports selected functions and keep others hidden.
In Java, the primary unit of procedural abstraction is the method,
which may be a procedure, a pure function, or an impure (side-effecting)
function. A method must be part of a Java class, which is,
in turn, part of a Java package.
Scala combines characteristics of Java and Haskell. It has Java-like
methods, which can be nested inside other methods. Scala also has class, object, trait, and package
constructs that include procedural abstractions.
In most languages (e.g., C), data structures are visible. A programmer can define custom data types, yet their structure and values are known to other parts of the program. These are concrete data structures.
As an example, consider a collection of records about the employees of a company. Suppose we store these records in a global C array. The array and all its elements are visible to all parts of the program. Any statement in the program can directly access and modify the elements of the array.
The use of concrete data structures is convenient, but it does not scale well and it is not robust with respect to change. As a program gets large, keeping track of the design details of many concrete data structures becomes very difficult. Also, any change in the design or implementation of a concrete data structures may require change to all code that uses it.
TODO: The following uses the term module in a general manner, so it may be necessary to introduce the concept of module above. (I had some changes above to the current version—by bringing some text back from ELIFP Chapter 2.
An abstract data structure is a module consisting of data and operations. The data are hidden within the module and can only be accessed by means of the operations. The data structure is called abstract because its name and its interface are known, but not its implementation. The operations are explicitly given; the values are only defined implicitly by means of the operations.
An abstract data structure supports information hiding. Its implementation is hidden behind an interface that remains unchanged, even if the implementation changes. The implementation detail of the module is a design decision that is kept as a secret from the other modules.
The concept of encapsulation is related to the concept of information hiding. The data and the operations that manipulate the data are all combined in one place. That is, they are encapsulated within a module.
An abstract data structure has a state that can be manipulated by the operations. The state is a value, or collection of information, held by the abstract data structure.
As an example, again consider the collection of records about the employees of a company. Suppose we impose a discipline on our program, only allowing the collection of records to be accessed through a small group of procedures (and functions). Inside this group of procedures, the array of records can be manipulated directly. However, all other parts of the program must use one of the procedures in the group to manipulate the records in the collection. The fact that the collection is implemented with an array is (according to the discipline we imposed) hidden behind the interface provided by the group of procedures. It is a secret of the module providing the procedures.
Now suppose we wish to modify our program and change the implementation from an array to a linked list or maybe to move the collection to a disk file. By approaching the design of the collection as an abstract data structure, we have limited the parts of the program that must be changed to the small group of procedures that used the array directly; other parts of the program are not affected.
As another example of an abstract data structure, consider a stack.
We provide operations like push, pop, and
empty to allow a user of the stack to access and manipulate
it. Except for the code implementing these operations, we disallow
direct access to the concrete data structure that implements the stack.
The implementation might use an array, a linked list, or some other
concrete data structure; the actual implementation is “hidden” from the
user of the stack.
We, of course, can use the available features of a particular programming language (e.g., module, package, class) to hide the implementation details of the data structure and only expose the access procedures.
There is only one instance of an abstract data structure. Often we need to create multiple instances of an abstract data structure. For example, we might need to have a collection of employee records for each different department within a large company.
We need to go a step beyond the abstract data structure and define an abstract data type (ADT).
What do we mean by type?
Consider the built-in type int in C. By
declaring a C variable to be of type int, we are
specifying that the variable has the characteristics of that type:
a value (state) drawn from some set (domain) of possible
values—in the case of int, a subset of the
mathematical set of integers
a set of operations that can be applied to those values—in the
case of int,
addition, multiplication, comparison for equality, etc.
Suppose we declare a C variable to have type int. By that
declaration, we are creating a container in the program’s memory that,
at any point in time, holds a single value drawn from the int domain. The
contents of this container can be operated upon by the int operations. In a
program, we can declare several int variables: each
variable may have a different value, yet all of them have the same set
of operations.
In the definition of a concrete data type, the values are the most prominent features. The values and their representations are explicitly prescribed; the operations on the values are often left implicit.
The opposite is the case in the definition of an abstract data type. The operations are explicitly prescribed; the values are defined implicitly in terms of the operations. A number of representations of the values may be possible.
Conceptually, an abstract data type is a set of entities whose logical behavior is defined by a domain of values and a set of operations on that domain. In the terminology we used above, an ADT is set of abstract data structures all of whom have the same domain of possible states and have the same set of operations.
We will refer to a particular abstract data structure from an ADT as an instance of the ADT.
The implementation of an ADT in a language like C is similar to that discussed above for abstract data structures. In addition to providing operations to access and manipulate the data, we need to provide operations to create and destroy instances of the ADT. All operations (except create) must have as a parameter an identifier (e.g., a pointer) for the particular instance to be operated upon.
While languages like C do not directly support ADTs, the class construct
provides a direct way to define ADTs in languages like C++, Java, and
Scala.
The behavior of an ADT is defined by a set of operations that can be applied to an instance of the ADT.
Each operation of an ADT can have inputs (i.e., parameters) and outputs (i.e., results). The collection of information about the names of the operations and their inputs and outputs is the interface of the ADT.
To specify an ADT, we need to give:
There are two primary approaches for specifying the semantics of the operations:
The axiomatic (or algebraic) approach gives a set of logical rules (properties or axioms) that relate the operations to one another. The meanings of the operations are defined implicitly in terms of each other.
The constructive (or abstract model) approach describes the meaning of the operations explicitly in terms of operations on other abstract data types. The underlying model may be any well-defined mathematical model or a previously defined ADT.
In some ways, the axiomatic approach is the more elegant of the two approaches. It is based in the well-established mathematical fields of abstract algebra and category theory. Furthermore, it defines the new ADT independently of other ADTs. To understand the definition of the new ADT it is only necessary to understand its axioms, not the semantics of a model.
However, in practice, the axiomatic approach to specification becomes very difficult to apply in complex situations. The constructive approach, which builds a new ADT from existing ADTs, is the more useful methodology for most practical software development situations.
To illustrate both approaches, let us look at a well-known ADT that we studied in the introductory data structures course, the stack.
In this section we give an axiomatic specification of an unbounded stack ADT. By unbounded, we mean that there is no maximum capacity for the number of items that can be pushed onto an instance of a stack.
Remember that an ADT specification consists of the name, sets, signatures, and semantics.
Stack (of Item)
In this specification, we are defining an ADT named
Stack. The parameter Item represents the
arbitrary unspecified type for the entities stored in the stack.
Item is a formal generic parameter of the ADT
specification. Stack is itself a generic ADT; a different
ADT is specified for each possible generic argument that can be
substituted for Item.
The sets (domains) involved in the Stack ADT are the
following:
Stack:Item:boolean:{ False, True }
To specify the signatures for the operations, we use the notation for
mathematical functions. By a tuple like (Stack, Item), we
mean the Cartesian product of sets Stack and
Item, that is, the set of ordered pairs where the first
component is from Stack and the second is from
Item. The set to the right of the -> is the
return type of the function.
We categorize the operations into one of four groups depending upon their functionality:
A constructor (sometimes called a creator, factory, or producer function) constructs and initializes an instance of the ADT.
A mutator (sometimes called a modifier, command, or “setter” function) returns the instance with its state changed.
An accessor (sometimes called an observer, query, or “getter” function) returns information from the state of an instance without changing the state.
A destructor destroys an instance of the ADT.
We will normally list the operations in that order.
For now, we assume that a mutator returns a distinct new instance of the ADT with a state that is a modified version of the original instance’s state. That is, we are taking an applicative (or functional or referentially transparent) approach to ADT specifications.
Technically speaking, a destructor is not an operation of the ADT. We can represent the other types of operations as functions on the sets in the specification. However, we cannot define a destructor in that way. But destructors are of pragmatic importance in the implementation of ADTs, particularly in languages that do not have automatic storage reclamation (i.e., garbage collection).
The signatures of the Stack ADT operations are as
follows.
create: -> Stack
push: (Stack, Item) -> Stack
pop: Stack -> Stack
top: Stack -> Item
empty: Stack -> boolean
destroy: Stack ->
The operation pop may not be the same as the “pop”
operation you learned in a data structures class. The traditional “pop”
both removes the top element from the stack and returns it. In this ADT,
we have separated out the “return top” functionality into accessor
operation top and left operation pop as a pure
mutator operation that returns the modified stack.
The separation of the traditional “pop” into two functions has two advantages:
It results in an elegant, applicative stack specification whose operations fit cleanly into the mutator/accessor categorization.
It results in a simpler, cleaner abstraction in which the set of operations is “atomic”. No operation in the ADT’s interface can be decomposed into other operations also in the interface.
Also note that operation destroy does not return a
value. As we pointed out above, the destroy operation is
not really a part of the formal ADT specification.
We can specify the semantics of the Stack ADT with the
following axioms. Each axiom must hold for all instances s
of type Stack and all entities x of type
Item.
top(push(s,x)) = xpop(push(s,x)) = sempty(create()) = Trueempty(push(s,x)) = FalseThe axioms are logical assertions that must always be true. Thus we can write Axioms 3 and 4 more simply as:
empty(create())not empty(push(s,x))The first two axioms express the last-in-first-out (LIFO) property of stacks. Axiom 1 tells us that the top element of the stack is the last element pushed. Axiom 2 tells us that removal of the top element returns the stack to the state it had before the last push.
Moreover, axioms 1 and 2 specify the LIFO property of stacks in purely mathematical terms; there was no need to use the properties of any representation or use any time-based (i.e., imperative) reasoning.
The last two axioms define when a stack is empty and when it not. Axiom 3 tells us that a newly created stack is empty. Axiom 4 tells us that pushing an entity on a stack results in a nonempty stack.
But what about the sequences of operations top(create())
and pop(create())?
Clearly we do not want to allow either top or
pop to be applied to an empty stack. That is,
top and pop are undefined when their arguments
are empty stacks.
Functions may be either total or partial.
A total function A -> B is defined for
all elements of A.
For example, the multiplication operation on the set of real numbers
R is a total function (R,R) -> R.
A partial function A -> B is undefined
for one or more elements of A.
For example, the division operation on the set of real numbers
R is a partial function because it is undefined when the
divisor is 0.
In software development (and, hence, in specification of ADTs), partial functions are common. To avoid errors in execution of such functions, we need to specify the actual domain of the partial functions precisely.
In an axiomatic specification of an ADT, we restrict operations to their domains by using preconditions. The precondition of an operation is a logical assertion that specifies the assumptions about and the restrictions upon the values of the arguments of the operation.
If the precondition of an operation is false, then the operation cannot be safely applied. If any operation is called with its precondition false, then the program is incorrect.
In the axiomatic specification of the stack, we introduce two preconditions as follows.
pop(Stack S):not empty(S)
top(Stack S)not empty(S)
Note that we have not given the semantics of the destructor operation
destroy. This operation cannot be handled in the simple
framework we have established.
Operation destroy is really an operation on the
“environment” that contains the stack. By introducing, the “environment”
explicitly into our specification, we could specify its behavior more
precisely. Of course, the semantics of create would also
need to be extended to modify the environment and the other operations
would likely require preconditions to ensure that the stack has been
created in the environment.
Another simplification that we have made in this ADT specification is that we did not impose a bound on the capacity of the stack instance. We could specify this, but it would also complicate the axioms the specification.
In this section, we give a constructive specification of a bounded stack ADT. By bounded, we mean that there is a maximum capacity for the number of items that can be pushed onto an instance of a stack.
StackB (of Item)
In this specification of bounded stacks, we have one additional set involved, the set of integers.
StackB:Item:boolean:integer:{ ..., -2, -1, 0, 1, 2, ... }
In this specification of unbounded stacks, we define the
create operation to take the maximum capacity as its
parameter.
create: integer -> StackB
push: (StackB, Item) -> StackB
pop: StackB -> StackB
In this specification, we add operation full to detect
whether or not the stack instance has reached its maximum capacity.
top: StackB -> Item
empty: StackB -> boolean
full: StackB -> boolean
destroy: StackB ->
In the constructive approach, we give the semantics of each operation by associating both a precondition and a postcondition with the operation.
As before, the precondition is a logical assertion that specifies the required characteristics of the values of the arguments.
A postcondition is a logical assertion that specifies the characteristics of the result computed by the operation with respect to the values of the arguments.
In the specification in this subsection, we are a bit informal about the nature of the underlying model. Although the presentation here is informal, we try to be precise in the statement of the pre- and postconditions.
Note: We can formalize the model using an ordered pair of type
(integer max, sequence stkseq), in which max
is the upper bound on the stack size and stkseq is a
sequence that represents the current sequence elements of elements in
the stack. This, more formal alternative, is presented in the next
subsection.
create(integer size) -> StackB S'
size >= 0
S' is a valid new instance of StackB
&&
S' has the capacity to store size items
&&
empty(S')
push(StackB S, Item I) -> StackB S'
S is a valid StackB instance &&
not full(S)
S' is a valid StackB instance &&
S' = S with I added as the new top.
pop(StackB S) -> StackB S'
S is a valid StackB instance &&
not empty(S)
S' is a valid StackB instance &&
S' = S with the top item deleted
top(StackB S) -> Item I
S is a valid StackB instance &&
not empty(S)
I = the top item on S
S is not modified by this operation.)
empty(StackB S) -> boolean e
S is a valid StackB instance
e is true if and only if S
contains no elements (i.e., is empty)
S is not modified by this operation.)
full(StackB S) -> boolean f
S is a valid StackB instance
f is true if and only if S
contains no space for additional items (i.e., is full)
S is not modified by this operation.)
destroy(StackB S) ->
S is a valid StackB instance
StackB S no longer exists
Note that each operation except the constructor (create)
has a StackB instance as an input; the constructor and each
of the mutators also has a StackB instance as an output.
This parameter identifies the particular instance that the operation is
manipulating.
Also note that all of these StackB instances are
required to be “valid” in all preconditions and postconditions, except
the precondition of the constructor and the postcondition of the
destructor. By valid we mean that the state of the instance is within
the acceptable domain of values; it has not become corrupted or
inconsistent. What is specifically mean by “valid” will differ from one
implementation of a stack to another.
Suppose we implement the mutator operations as imperative commands
rather then applicative functions. That is, we implement mutators so
that they directly modify the state of an instance instead of returning
a modified copy. (S and S' are implemented as
different states of the same physical instance.)
Then, in some sense, the above “validity” property is invariant for an instance of the ADT; the constructor makes the property true, all mutators and accessors preserve its truth, and the destructor makes it false.
An invariant property must hold between operations on the instance; it might not hold during the execution of an operation. (For this discussion, we assume that only one thread has access to the ADT implementation.)
Aside: An invariant on an ADT instance is similar in concept to an invariant for a while-loop. A loop invariant holds before and after each execution of the loop.
As a convenience in specification we will sometimes state the invariants of the ADT separately from the pre- and postconditions of the methods. We sometimes will divide the invariants into two groups.
The interface invariants are part of the public interface of the ADT. They only deal with the state of an instance in terms of the abstract model for the ADT.
The implementation invariants are part of the hidden state of an instance; in some cases, they define the meaning of the abstract properties stated in the interface invariants in terms of hidden values in the implementation.
Let the bounded stack StackB be represented by an
ordered pair of type (integer max, sequence stkseq), in
which max is the upper bound on the stack size and
stkseq is a sequence that represents the current sequence
elements of elements in the stack.
create(integer size) -> StackB S'
size >= 0
S' == (size,[])
[] represents an empty sequence. The value of a
variable occurring in the postcondition is the same as that variable’s
value in the precondition.push(StackB S, Item I) -> StackB S'
S == (m,ss) && m >= 0 && length(ss) < m
S' == (m,[I]++ss)
++ denotes the concatenation of its left and right
operand sequences. The result sequence has all the values from the left
operand sequence, in the same order, followed by all the values from the
right operand sequence, in the same order. Also the notation
[I] represents a sequence consisting a single element with
the value I.pop(StackB S) -> StackB S'
S == (m,ss) && m >= 0 && length(ss) > 0
S' == (m,tail(ss))
tail is a function that returns the sequence
remaining after removing the first element of its nonempty sequence
argument. Similarly, the function head (used below) returns
the first element of its nonempty sequence argument.top(StackB S) -> Item I
S == (m,ss) && m >= 0 && length(ss) > 0
I = head(ss) && S' == S
empty(StackB S) -> boolean e
S == (m,ss) && m >= 0 && length(ss) <= m
e == (length(ss) == 0) && S' == S
full(StackB S) -> boolean f
S == (m,ss) && m >= 0 && length(ss) <= m
f == (length(ss) == m) && S' == S
destroy(StackB S) ->
S == (m,ss) && m >= 0 && length(ss) <= m
StackB S no longer exists
Using this abstract model, we can state an interface invariant:
For a
StackB S, there exists an integermand sequence ofItemelementslsuch thatS == (m,ss) && m >= 0 && length(ss) <= m
For discussion of implementing ADTs as Java classes, see the chapter Data Abstraction in Java. A Java implementation of the StackB ADT appears in that chaper.
Consider an ADT for storing and manipulating calendar dates. We call
the ADT Day to avoid confusion with the Date class in the
Java API. This ADT is based on the Day class defined in Chapter 4 of the
Horstmann and Cornell [5].
Logically, a calendar date consists of three pieces of information: a
year designator, a month designator, and a
day of the month designator. A secondary piece of information
is the day of the week. In this ADT interface definition, we use
integers (e.g., Java int) to designate
these pieces of information.
Caveat: The discussion of Java in these notes does not use generic type parameters.
create(integer y, integer m, integer d) -> Day D'
y != 0 && 1 <= m <= 12 && 1 <= d <= #days in month m
&&(y,m,d) does not fall in the gap formed by the change to
the modern (Gregorian) calendar
D' is a valid new instance of Day with year
y, month m, and day d
setDay(Day D, integer y, integer m, integer d) -> Day D'
D is a valid instance of Day &&
y != 0 && 1 <= m <= 12 && 1 <= d <= #days in month
&&
(y,m,d) does not fall in the gap formed by the change to
the modern (Gregorian) calendar
D' is a valid instance of Day &&
D'= D except with year y, month
m, and day d
setDay, setMonth,
and setYear operations? What problems might arise?advance(Day D, integer n) -> Day D'
D is a valid instance of Day
D' is a valid instance of Day &&
D' = D with the date moved n days later
(Negative n moves to an earlier date.)
getDay(Day D) -> integer d
D is a valid instance of Day
d is day of the month from D, where
1 <= d <= #days in month getMonth(D)
D is unchanged.)
getMonth(Day D) -> integer m
D is a valid instance of Day
m is the month from D, where
1 <= m <= 12
D is unchanged.)
getYear(Day D) -> integer y
Precondition: : D is a valid instance of
Day
y is the year from D, where
y != 0
D is unchanged.)
getWeekday(Day D) -> integer wd
D is a valid instance of Day
wd is the day of the week upon which D falls:
0 = Sunday, 1 = Monday, …, 6 = Saturday
D is unchanged.)
equals(Day D, Day D1) -> boolean eq
D and D' are valid instances of
Day
eq is true if and only if D and
D' denote the same calendar date
D and D' are unchanged.)
daysBetween(Day D, Day D1) -> integer d
D and D' are valid instances of
Day
d is the number of calendar days from D1 to
D, i.e., equals(D,advance(D1,d)) would be true
D is unchanged.)
toString(Day D) -> String s
D is a valid instance of Day
s is the date D expressed in the format
“Day[getYear(D),getMonth(D),getDay(D)]”.
D is unchanged.)
Note: This method is a “standard” method that should be defined for most Java classes so that they fit well into the Java language framework.
destroy(Day D) ->
Precondition: D is a valid instance of
Day
Postcondition: D no longer exists
A Java implementation of the Day ADT appears in the supplementary notes.
The design and implementation of ADTs (i.e., classes) must be approached from two points of view simultaneously:
The client-supplier relationship is as represented in the following diagram:
________________ ________________
| | | |
| Client |===USES===>| Supplier |
|________________| |________________|
(ADT user) (ADT) TODO: Replace the above diagram with a better graphic and make it a Figure.
The supplier’s concerns include:
The clients’ concerns include:
As we have noted previously, the interface of an ADT is the set of features (i.e., public operations) provided by a supplier to clients.
A precise description of a supplier’s interface forms a contract between clients and supplier.
The client-supplier contract:
gives the responsibilities of the client. These are the conditions under which the supplier must deliver results — when the preconditions of the operations are satisfied (i.e., the operations are called correctly).
gives the responsibilities of the supplier. These are the benefits the supplier must deliver — make the postconditions hold at the end of the operation (i.e., the operations deliver the correct results).
The contract
protects the client by specifying how much must be done by the supplier.
protects the supplier by specifying how little is acceptable to the client.
If we are both the clients and suppliers in a design situation, we should consciously attempt to separate the two different areas of concern, switching back and forth between our supplier and client “hats”.
We can use the following design criteria for evaluating ADT interfaces. Of course, some of these criteria conflict with one another; a designer must carefully balance the criteria to achieve a good interface design.
In object-oriented languages, these criteria also apply to class interfaces.
Cohesion: All operations must logically fit together to support a single, coherent purpose. The ADT should describe a single abstraction.
Simplicity: Avoid needless features. The smaller the interface the easier it is to use the ADT (class).
No redundancy: Avoid offering the same service in more than one way; eliminate redundant features.
Atomicity: Do not combine several operations if they are needed individually. Keep independent features separate. All operations should be primitive, that is, not be decomposable into other operations also in the public interface.
Completeness: All primitive operations that make sense for the abstraction should be supported by the ADT (class).
Consistency: Provide a set of operations that are internally consistent in
Avoid surprises and misunderstandings. Consistent interfaces make it easier to understand the rest of a system if part of it is already known.
Reusability: Do not customize ADTs (classes) to specific clients, but make them general enough to be reusable in other contexts.
Robustness with respect to modifications: Design the interface of an ADT (class) so that it remains stable even if the implementation of the ADT changes.
Convenience: Where appropriate, provide additional operations (e.g., beyond the complete primitive set) for the convenience of users of the ADT (class). Add convenience operations only for frequently used combinations after careful study.
TODO
TODO
In Spring 2017 I adapted these lecture notes from my previous notes on this topic. The material here is based on my study of a variety of sources (e.g., Bird and Wadler [1], Dale [2], Gries [3]; Horstmann [4,5], Liskov [6], Meyer [7], Mossenbock [8], Parnas [9], and Thomas [10]).
I wrote the first version of these lecture notes to use in the first Java-based version of CSci 211 (then titled File Systems) during Fall 1996. I revised the notes incrementally over the next decade for use in my Java-based courses on object-orientation and software architecture. I partially revised the notes for use in my Scala-based classes beginning in Fall 2008.
In Fall 2013 I updated these notes to better support my courses that used non-JVM languages such as Lua, Elixir, and Haskell. I moved the extensive Java-based content to a separate document and developed separate case studies for the other languages.
In Summer 2017, I adapted the notes to use Pandoc. I continued to revise the structure and text in minor ways in 2017, 2018, and 2019.
I incorporated quite a bit of this material into Chapters 2, 6, and 7 of the 2018 draft of the textbook Exploring Languages with Interpreters and Functional Programming (ELIFP).
I retired from the full-time faculty in May 2019. As one of my post-retirement projects, I am continuing work on possible textbooks based on the course materials I had developed during my three decades as a faculty member. In January 2022, I began refining the existing content, integrating separately developed materials together, reformatting the documents, constructing a unified bibliography (e.g., using citeproc), and improving my build workflow and use of Pandoc.
TODO: 2022 updates
I maintain this chapter as text in Pandoc’s dialect of Markdown using embedded LaTeX markup for the mathematical formulas and then translate the document to HTML, PDF, and other forms as needed.
TODO