For the evolving discipline of programming, acquisition of programming knowledge is a difficult issue. Common knowledge results from the acceptance of proven techniques based on results of formal inquiries into the nature of the programming process. This is a rather slow process. In addition, the vast body of common knowledge needs to be explicated to a low enough level of details for it to be represented in the machine processable form. It is felt that this is an impediment to the progress of automatic programming. The importance of formal approaches cannot be overstated since their contributions lead to quantum leaps in the state of the art

Amin, Ashok T.

English

NASA Technical Reports Server

ON ACQUISITION OF PROGRAMMING
Ashok T. Amin
 88-16438
KNOWLEDGE
Computer Science Department
The University of Alabama in Huntsville
Huntsville, Alabama 35899
ABSTRACT Acquisition and judicious incorporation of
programming knowledge into the programming environment
is essential to support development of correct programs
and thereby enhance the programmer productivity. A pro-
gram may be viewed as an outcome of interaction between
a programmer and his/her programming environment. The
interaction may be supported at the program generation
level, program design level, or at the program specifi-
cation level. The higher is the l_vel of interaction
supported the greater is the knowledge base required to
support this interaction, and greater are the program-
ming skills required of the programmer. Knowledge ac-
quisition techniques used range from formal-based on
mathematical properties of programs to empirical, and
from generic to application domain specific. In this
paper, we review the recent developments in this area
and suggest future directions.
I. INTRODUCTION
Knowledge based programming environments have an impor-
tant role to play in facilitating the development of correct pro-
grams. Such environments can provide for rapid development of
correct programs by guiding the programmer through the maize of
design decisions and implementation alternatives, and automating
low level programming tasks. Developments in this area may lead
ultimately to Automatic Programming Systems.
The goal of automatic programming is to automate all the
phases of the program development - namely, program specifica-
tion, design, and implementation. There are two approaches to
realization of an Automatic Programming System. In one, the sys-
tem proceeds with given correct specification of the program and
through application of an appropriate sequence of valid transfor-
mations transforms the specification into a program. The program
specification may be assumed to have been provided by the pro-
grammer or developed by the programmer through interactions with
the system. As opposed to this, the other approach is more of an
evolutionary approach, and is based on extending the automation
of programming tasks from lower levels to higher levels. In ei-
ther case, acquisition and codification of programming knowledge
is an essential task. It has been observed [3] that large body of
programming knowledge exists and that codification of this
knowledge remains most limiting factor to ultimate development of
automatic programming systems.
Knowledge acquisition and incorporation for an evolving
427
PRECEDING PAGE I_LANK NOT FII_-7_D
https://ntrs.nasa.gov/search.jsp?R=19880007056 2020-03-20T07:24:41+00:00Z
discipline is more problematic than for a mature one. One finds
that programming is variously described as art, craft, and sci-
ence. In the sense that parts of programming process are at
various stages of evolution each of the terms may reasonably be
used to describe the programming process. The evolving formali-
zation of programming process by Computer Scientists and the the
proven and empirical techniques of programming craft used by the
practitioners must be used as the sources of programming
knowledge and the knowledge from these source be suitable in-
tegrated to realize the gains in automation of programming pro-
cess.
To further complicate the matter, not only the knowledge
bases relevant to various phases of program development need to
be integrated, but knowledge bases that address the development
of correct programs must be integrated with one that addresses
the efficiency concerns in a manner that changes in the one has
minimal impact on the other [6]. Often specialization of a pro-
gramming system to a specific application opens up opportunities
for improving the programming process for that application by
judicious incorporation of domain specific knowledge [I]. An im-
portant aspect of any system is to allow for acquisition and in-
tegration of additional knowledge and refinement of existing
knowledge during its use by the programmer.
2. PROGRAMMING KNOWLEDGE
Fundamental research in Computer Science deals with for-
mal approaches to understanding of programs and the programming
process. The knowledge so gained may be used to develop explora-
tory techniques for program development. Those techniques that
gain acceptance and wide usage then become proven techniques for
program development. There are a number of issues that require
more empirical approaches for its resolution. Such issues relate
to reliability, maintainability, efficiency, human interfaces,
etc.
There are two views in relation to approaches to automat-
ic program development. One assumes that formal approaches will
ultimately permit automatic development of correct programs. The
other view accepts that writing correct programs is hard, and
therefore it is accepted that programs may contain errors and
that through iterative process of error detection and correction
one arrives at a program in which one has high degree of confi-
dence that it is correct. It may be noted that these two ap-
proaches complement each other in the sense that formal ap-
proaches are necessary if we are to build correct programs for
critical applications, and at the same time empirical approaches
are necessary if we are to develop any worthwhile program of rea-
sonably large size in timely fashion.
Formal Approaches:
Formal approaches to the understanding of the programs
and the programming process demands high degree of profficiency
428
in the creative application of mathematical skills. Formal ap-
proaches do not always yield results that may be used to develop
practical techniques. Often techniques based on formal ap-
proaches are time consuming particularly when it is to be carried
out manually. Further, since these techniques require special-
ized skills, they are subject to subtle and hence hard to detect
errors. For example, it may be very difficult to detect error in
an erroneous proof of program correctness. None the less
knowledge gained from formal inquiries have yielded many useful
results.
Laws of programming [5] and formal program design methods
[4] are examples of formal approaches to the understanding of
programs and the programming process, respectively. It is inves-
tigations of these types that will allow us to resolve the ques-
tions of program equivalence by suitable representation of pro-
grams in a canonical form. And allow development of programs
through better understanding of issues that are important at
various stages of program design and to the development of
languages with suitable degrees of freedom to represent a program
through various stages of development.
Exploratory approaches to program development are typi-
cally based on some break through in the formal understanding of
programs and the programming process. Formal representation of
program design information in Programmer's Apprentice represents
an exploratory technique [7] for use in semi-automatic program
development. Acceptance and wide usage of these techniques leads
to proven techniques which then become common knowledge.
Common Knowledge:
Vast body of programming knowledge exists in the form of
textbooks and reference books, especially related to algorithms,
data structures, and structured programming. The impact of the
developments in these areas from formal inquiries in 50's and
60's is now visible in development of programming languages and
programmer training. While programming language can not enforce
structured programming it can and have facilitated development of
structured programs by providing suitable constructs and support
for abstract data types. It is believed that advances in pro-
gramming will require development of languages that support the
major paradigms of their user communities [2].
The paradigms for algorithm development, such as divide-
and-conquer, provide a systematic approach to development of al-
gorithmic solution to a problem. A unified view of application of
this paradigm for the development of algorithms and its implemen-
tations for a class of problems, say sorting, is needed to extri-
cate the technique in details enough for the knowledge to be
represented in machine processable form [3,8]. To this end, one
can recognize that merge sort, quick sort, insertion sort, and
selection sort are examples of divide-and-conquer paradigm with
differences in the methods of partitioning of the problem and the
composition of the solution from the partial solutions. In addi-
tion, a number of paradigms need to be developed for the tech-
429
niques of implementation to be suitably represented,
recursion-to-iteration transformation, etc.
such as
Empirical Approaches:
There are a number of issues of practical importance that
defy formal approaches and in fact may not be properly be sub-
jects of formal inquiry. For example, when do we stop testing a
program? A practical answer is when the resources allocated for
the testing have been exhausted. None the less, formal inquiries
on this questions have yielded results that are awfully inade-
quate. Other issues involve human interfaces, or measures of
complexity. It has been remarked that programming deals with
managing complexity. There are rules, based on psychological
studies, that say a human can deal with seven things at a time. A
number of software methodologies use this as a guide to help
manage complexity. How large a problem should be for a technique
to more efficient than a simpler one? A number of implementation
issues have this characteristics. In such cases it much better
to arrive at a resolution based on interaction with the user.
The studies involving observations of expert programmer at work
also yield useful information involving 'good' programming prac-
tices [9].
Acquisition and Incorporation:
The approach used for knowledge acquisition and incor-
poration is important. Knowledge bases that addresses specific
aspects of programming must be integrated in a manner that allows
common interface without making it more complex to use or update.
The knowledge must be represented so that its refinement and ad-
dition can be accommodated gracefully. Lastly, means must be pro-
vided so that user may add and modify the programming knowledge.
3. CONCLUSION
For the evolving discipline of programming, acquisition
of programming knowledge is a difficult issue. Common knowledge
results from the acceptance of proven techniques based on results
of formal inquiries into the nature of the programming process.
This is a rather slow process. In addition, the vast body of
common knowledge needs to explicated to the low enough level of
details for it to be represented in the machine processable
form. It is felt that this currently impediment to the progress
of automatic programming. Importance of formal approaches can
not be overestimated since its contributions lead to quantum jump
in the state of the art.
REFERENCES
[I] D. Barstow, "Domain-Specific Automatic Programming,"
IEEE-TSE, 11.11(1985)1321-1336.
[2] R.W. Floyd, "The Paradigms of Programming," Communications
430
[3]
[4]
[51
[6]
[7]
[8]
[9]
of ACM, 22.8(1979) 455-460.
C. Green and D. Barstow, "On Program Synthesis Knowledge,"
Artificial Intelligence, 10(1978) 241-279.
C. A. R. Hoare, "An Overview of Some Formal Methods for
Program Design," COMPUTER, 20.9(1987) 85-91.
C. A. R. Hoare, I. J. Hayes, et.al, "Laws of Programming,"
CACM, 30.8 (1987 672-686.
E. Kant, "Efficiency in Program Synthesis" UMI Research
Press, 1981.
C. Rich, "A Formal Representation For Plans In The
Programmer's Apprentice," Proc. of Seventh International
Conference on Artificial Intelligence, 1981.
D. R. Smith, "Top-Down Synthesis of Divide-and-Conquer
Algorithms," Artificial Intelligence, 27(1985) 43-96.
E. Soloway and K. Ehrlich, "Empirical Studies of Programming
Knowledge," IEEE Trans. of Software Engineering, 10.5(1984)
595-609.
431


On acquisition of programming knowledge

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server