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Programming is Writing (Revised)
Gary T. Leavens, Albert L. Baker, Vasant Honavar,
Steven M. LaValle, Gurpur Prabhu
TR #97-23a December 1997, Revised June 1998
Keywords: software, programming, writing, student ratio, teaching assistant.
1994 CR Categories: K.3.2 [Computers and Education] Computer and Information Science Education -- Computer
science education.
Submitted for publication.
© Gary T. Leavens*, Albert L. Baker, Vasant Honavar, Steven LaValle, and Gurpur Prabhu, 1997, 1998. All rights
Department of Computer Science
226 Atanasoff Hall
Iowa State University
Ames, Iowa 50011-1040, USA
* Leavens's work was supported in part by NSF grant CCR-9503168.
Programming is Writing (Revised)
Teaching a student to write computer programs well is much like teaching a
student to write English prose well. That is, although a program must be correct in every
last detail, achieving correctness is only half of the task. The other half consists of quality
factors such as clarity, organization, conciseness, maintainability, etc. Although these
factors cannot be automatically measured, they have a large economic impact, because a
major cost of software development is the time spent by other people reading programs to
validate, maintain, and enhance them.
To teach these quality factors, student programs must be read by a skilled
programmer. Furthermore, grades for programs must be partly based on these quality
factors. Completely automatic testing and grading of student programs by machine not
only ignores these quality factors, it also fosters the attitude that such factors are
unimportant. When programs are automatically tested and not read, students come to
believe that functional correctness is all that matters. They tend to write programs by
making changes in an initial attempt at a program until it “works.” The result is students
who cannot write programs well.
From this analysis and our experience, we conclude that enough human
resources, such as teaching assistants, have to be made available for programming
courses to ensure that there is adequate time for careful reading of student programs.
Computer programmers are in demand because programming is highly-skilled
work and there are too few programmers. Computer programming is a precise and
technical skill that cannot be learned without extensive training and practical experience.
This is because computers are extremely literal-minded when they carry out their
instructions (as described further below). Because employers are reluctant to hire people
who have no training, students seek formal training in programming. This creates a high
demand for computer programming courses, as evidenced by the sustained high
enrollment since the late 1970s [8].
In a typical research university, however, high demand for courses and limited
resources leads to problems. In this report we take our own university and department as
a case study of such problems, which we believe illustrate the problems facing most
computing departments. We focus on a vital resource: the course staff, including
professors, teaching assistants (TAs), and other humans (such as lab monitors) who assist
students in ways that machines cannot. (These TAs are not necessarily graduate students;
undergraduate students have been successful TAs in some introductory courses [10].
Similarly, the professors need not be primarily involved in research; dedicated
instructional staff is used in many highly-rated computing research departments [7].)
In our particular case, as at most research universities, it is the number of TAs
(and the ratio of students to TAs) that varies most rapidly when resource problems occur.
Hence, we take the ratio of students per TA as a prime indicator of the course staffing.
At a college where there are no TAs, the resource problems would show up in
the number of students per staff member. Since this is primarily a case study, however,
Programming is Writing (Revised)
we use “TA” below to mean the people who do grading in a course, regardless of whether
they are undergraduates, graduate students, instructors, or professors.
What happens when the demand for programming courses increases, or when
the number of TAs available for such courses declines? Two responses to such a dilemma
are possible:
1. one can assign more students per TA, or
2. one can limit enrollments to maintain the student to TA ratio.
Clearly, neither of these responses is ideal. However, universities change slowly,
and while demand for computer programming courses has risen dramatically in the past
few years, during this time many universities have seen flat or declining budgets. Our
own department has been fortunate to have a fairly steady budget over the past decade,
but is experiencing an increase in enrollment. Hence, like many other computing
departments, we have been faced with the dilemma described above.
In the past our department has responded to this dilemma by assigning more
students per TA in our programming courses. Whereas we had about 25 students per TA
in our courses in 1994, in Fall 1997 we had 50 students per TA. The size of lecture
sections has also increased dramatically, making it more difficult to give in-class
exercises and to have personal interactions with students.
In this paper, we explain why increasing the number of students per TA without
limit is a mistake and a serious disservice to students. In the next Section, we explain the
quality factors that good programs should exhibit and describe their economic
importance. This section develops the analogy of writing programs to writing English
prose. In the following section, we explain why it is difficult to have TAs carefully read
student programs if the number of students per TA rises much beyond 25, at least in
introductory courses. Following that we describe our observations on how students
program when their programs are automatically graded by machine and not carefully read
by humans. Finally, we summarize this argument and offer some concluding remarks.
To the outside observer (who is not a practicing mathematician or computer
scientist), programming seems like something akin to solving a mathematical problem.
Memories of algebra or trigonometry may lead one to believe that the process of getting
the answer is not as important as the answer. Nothing could be further from the truth.
Writing a program is not at all like finding an answer to a specific instance of a
numerical problem (e.g., multiplying 3 and 4). The programmer does not solve a specific
problem instance (e.g., multiply 3 and 4 and produce 12). Instead, a programmer’s task is
to write a set of unambiguous instructions for a computer that can solve a whole class of
problem instances (such acting as a calculator). If only one instance of a problem is to be
solved, then it is not cost-effective to write a program.
Consider writing a program that acts like a calculator. It is important that the
program should work as expected. That is, it should produce the right answer (consistent
with the rules of arithmetic) for all allowable inputs. It is equally important that other
programmers should be able to read, understand, validate, modify, adapt, maintain, use
and reuse the program. Most would also argue that the programs should be concise,
elegant, and efficient. Thus, the activity of writing programs is much like writing recipes
for use by novice cooks or writing a chapter in a textbook explaining how to solve a class
of problem instances (e.g., multiplying two numbers).
This analogy between a computer program and a textbook chapter is enhanced
by a closer look at programs. A computer program is a list of detailed instructions for a
machine, together with some associated “comments.” The instructions themselves make
up an algorithm; the algorithm is like the rules for carrying out some task and formulas
that may be found in a textbook chapter. However, unlike a textbook chapter written in
English, algorithms expressed in a computer program are completely formal; that is, they
have a mathematically described syntax and semantics, and must be syntactically correct
in every detail. The comments (or “documentation”) are like the remarks in a textbook;
Programming is Writing (Revised)
they provide additional explanation and motivation, history, descriptions of purpose, or
Although programs are a means to instruct a computer, humans have to read
them as well. Indeed, in the software industry, the human readers of a program are just as
important as the machine. This is critical for several reasons:
Programs are often written in teams, and team members need to understand
each other's code and procedures.
Debugging a program (getting it to work correctly) also requires reading.
Here the reader is often the program's author; the difficulty of finding bugs
in a program [2, Chapter 13] shows the difficulty in reading programs
carefully. Even the program's own author will have difficulty in reading a
program that is unclear, poorly organized, or poorly documented.
Programs are often read during “code walkthroughs.” Here the readers are
programmers other than the program's author, who read the program
carefully to validate its correctness [2, 9].
Programs are read by “reusers,” people who wish to use or adapt the code
for another purpose.
Perhaps the most important reader of a program is the maintenance
programmer. This is, very often, a different person than the program's
author. The maintenance programmer has to understand a program to fix or
enhance its functionality. Often the author of the program is not available to
answer questions.
Because about half of the cost of a program is spent in its maintenance phase
(according to the classic reference [1, page 18]), maintenance costs have an enormous
economic impact. “Studies have shown that 30-90% of software expenditure is spent on
maintaining existing software [12, 13]” [11, page 66]. “Software engineers generally
agree that the total cost of maintenance is more than the cost of development of software
[6, page 13].
“Studies have also shown that maintenance programmers spend about half of
their time studying the code and related documentation. This has led Standish [12] to
conclude that the cost of comprehending a program is the dominant cost of a program
over its entire life cycle” [11, page 66].
Thus, training students to write a program so that it is clear, concise, well
organized, well documented, etc., is vital for the economic health of contemporary
society, which is increasingly reliant on information technology. Hence, it is necessary to
emphasize these aspects of writing programs when training students.
In summary, writing computer programs is much like writing a textbook chapter
to instruct (a computer) to solve a collection of problem instances. For good economic
reasons, programs must be written so that people can easily read them.
Since writing programs is a skill, it has to be practiced to be learned. Who would
imagine that an English course in technical writing could be taught without writing
assignments? In a computer programming course, it would be equally absurd to try to
teach programming without having students write programs. Typically many programs
are assigned over the course of a semester, so that the students are more or less constantly
writing programs.
What students write must be carefully read, since feedback is necessary to
develop writing skills. In an English course, writing assignments are carefully read by
qualified instructors. To allow sufficient time for careful reading, English departments
limit the number of students that are graded by each instructor. The same should hold for
courses in computer programming.
In the following we again use our own department as a case study, and
specialize it to our own introductory (sophomore-level) undergraduate courses.
Programming is Writing (Revised)
How much time TAs have available
One way to get a handle on the amount of time available for reading student
programs is to estimate the time available for the task. In our department, TAs are usually
required to attend classes, teach a discussion section, hold office hours, and meet with the
professor. We estimate that TAs spend at least 8 hours per week, Tfixed, on such fixed
activities (see Table 1).
Week Activity
3 attending lectures
1 teaching discussion section
1 preparing for discussion section
2 office hours (meeting with students)
1 meeting with professor
8 total = Tfixed
Table 1: Hours per week for fixed activities.
The remaining time for which a TA is hired we call Tavailable. That is Tavailable is
the budgeted time, Tbudget, minus Tfixed. In our department, where Tbudget = 20, Tavailable, is
12 hours per week. Dividing Tavailable by the number of students in a class gives the
available time per student per week.
Tavailable/student = (Tbudget Tfixed) / size(class) (1)
How much time TAs spend grading
We did an informal survey of our TAs, and asked them how long they spent
grading programs. Nine TAs out of about 30 responded to the survey; 8 were TAs for
undergraduate courses, one for a graduate course. In the following we focus on the
undergraduate courses. All of these courses were introductory (sophomore-level)
programming courses. All of the undergraduate courses for which TAs responded have
about 50 students per (20 hour a week) TA.
No TA reported spending more than 10 minutes (on average) grading per
program. The average was about 7 minutes per program. The average length of such a
program was 3.25 pages. This means that TAs are currently spending about 2 minutes per
page to grade each program. This group of TAs estimated that they spent about 6 minutes
on the average reading programs, with time estimates ranging from 1 minute (for 2 page
programs) to 12.5 minutes (also for 2 page programs).
At a class size of 50 students, TAs have a maximum of about 14 minutes per
student per week to spend grading. Yet the TAs responding to the survey reported that
they only spent half of that time on the actual act of grading programs. Presumably, the
TAs spent some of their other time doing other things, such as grading non-program
problem sets, helping students outside of office hours, preparing test cases and grading
scripts, recording grades, answering student email, studying the material in the course,
helping make up test questions, preparing homework problem sets or files, and so on.
Suppose we modify the equation (1) to reflect the reality that the time spent
grading a student's program is only 1/2 of the time available. (In our experience, the
amount of time needed for other activities that consume available time is also roughly
proportional to the size of the class.) Then we get the following equation that can be used
to estimate the time TAs will spend grading programs based on class size.
Tgrading/student = ½ × (Tbudget Tfixed)/size(class) (2)
For Tbudget = 20, this equation is displayed in Figure 1.
Programming is Writing (Revised)
Figure 1: Class size vs. predicted actual time spent grading for a 20 hour per week TA.
Grading crises and possible responses
What happens when a TA has more pages of programs to read than he or she has
time to read carefully? The TA will either: do a less careful job of reading programs, or,
if required to read them carefully, will start to fall behind in grading. Both situations lead
to lack of feedback for students. Moreover, because the semester has a definite end-point,
TAs must finish grading by the end of the semester. A TA who falls behind in reading
students programs must therefore, at some point catch up. Inevitably, to catch up, the TA
must read the programs less carefully. So in either case programs will not be read
There are several responses to the crisis that happens in a course when the TAs
cannot keep up with doing a careful job of reading student programs.
The amount of programming homework can be reduced for students.
However, this leads to lack of skills on the part of the students.
Some of the assigned homework can be left ungraded. The idea here is to
not tell the students which of their homework will be left ungraded.
However, since a typical programming course has only 10 to 12 programs
that constitute the homework for a class, this is not a viable option. Leaving
some of the homework ungraded also leads to poor satisfaction levels on the
part of students, lack of feedback, and poor teaching evaluations.
Some of us have tried giving “suggested practice” problems to students, but
students are overworked and tend to just ignore anything that is not graded.
Actual grading minutes per week for a 20
hour per week TA
10 15 20 25 30 35 40 45 50 55 60 65 70
Class size
Minutes per student per week
Programming is Writing (Revised)
Group programming projects can be used instead of individual assignments.
While this is a good idea in upper-level courses, it does not always work in
introductory courses. (Some of us have tried that, but the programs in
introductory courses are typically too small for group work.) At the
introductory level, it is especially important to ensure that each student
learns basic programming skills. This is more difficult with group
projectsimagine group papers in an introductory English course.
Furthermore, group projects require more effort on the part of the staff, and
if they are scaled to the size of the group, then they tend to generate the
same amount of code per student as would be generated for individual
programs, which would not lessen the reading burden. So, while group
assignments may be good for other reasons, they do not themselves solve
the grading crisis.1
Programs can be automatically graded by running test cases. The idea is that
students electronically submit their programs, and the TA can have the
machine run test cases over these programs. The scores (how many test
cases pass), can be used to generate a grade, without a human ever reading
the program. However because machines cannot automatically grade on the
quality factors of writing programs, these factors are completely ignored.
We consider the problems caused by this at length below.
Some programs can be carefully graded, such as those early in the course,
and automatic grading can be used later in the course. This would avoid
some of the problems with automatic grading discussed below, but by
assumption it would be difficult to get students timely feedback on the hand
graded programs in the case we are considering.
Automatic grading of student homework is an attractive option to both TAs and
professors when there are more than about 25 students per TA in a class. This is because,
according to our model, the TA will be willing to spend less than 15 minutes per student
per week to spend grading. At 50 students, the TA will be willing to spend about 7
minutes per week per student grading, which does not allow time for careful reading to
grade on quality factors. Indeed, it barely allows enough time for automatic grading (at 2
minutes per page) for our average program at this level. Furthermore, the work tends to
become tedious with so many students.
Automatic grading does not seem to suffer the disadvantages of the other
responses to the grading crisis. It promises to relieve the tedium of checking programs for
correctness, and to do a more thorough job of checking for correctness than most TAs
[8]. If (some of) the test cases used in automatic grading are made available to students,
then students can also get immediate feedback on their programs, by running the test
cases themselves, which is good.
Automatic grading also seems to promise reduced costs for teaching students.
Once designed, it would seem that an automatic grading system would allow virtually
unlimited ratios of students to TAs. Unfortunately, our experience is that automated test
systems for computer programs require a great deal of effort in designing test cases.
Furthermore, errors in student programs often lead to problems in testing of their
programs; hence, in our experience, a TA must constantly attend the running of an
automated testing program. When a student's program has a problem in automated
testing, often the program must be read to assign a grade; according to our survey, this
will take as long if not longer than the automatic grading procedure for that program.
1 One alternative to group programming is a “pairwise exchange,” in which two students each write
a program, exchange the code, and edit the other person's code to make some change or
enhancement. This would tend to emphasize the ideas of readability in programming. However,
it would not solve the grading problems, as each student would still be writing each program.
Furthermore, it could easily lead to more time spent grading, because grading would have to
weigh both the original programs exchanged and the relative merits of the maintenance efforts.
Programming is Writing (Revised)
Another problem with programs that do automated grading is that they work
best with highly constrained, batch-mode programming problems. If one wishes to teach
students about how to design to incomplete requirements, larger systems, or graphical
user-interfaces, then automated grading systems become impractical.
Finally, even advocates of automated testing systems in programming courses
only advocate the use of such systems to help decide the part of a program's grade that is
based on correctness.
“We emphasize, however, that this testing tool does not necessarily
compute scores or grades, nor does it reduce the human judgement involved in
evaluating students' work (which includes not only the program's correctness, but
also its adherence to the principles of good design, its documentation, and perhaps
its user interface or the student's own choice of test data)” [8, page 382].
Automatic grading, as opposed to automatic testing as a supplement to careful
reading, would ignore the quality factors in programs. As we explain in the next section,
it is our experience that this causes very severe problems for student learning.
Unfortunately, these problems are not immediately apparent.
What is the response of a rational and often overworked, busy student to
automatic grading of his or her programs? First, such a student quite sensibly focuses on
doing the minimum needed to get the desired grade in the class. In a class with totally
automatic grading, no (other) human will look at the student's programs; hence the
student just focuses on the program's correctness. This focus on correctness means that
the quality factors are ignored. After all, why put effort into writing clear, concise, well-
documented programs if no one is going read them? Thus the first effect of automatic
grading is that students do not learn how to write programs for human readers; they
ignore the economically important quality factors.
The second effect of automatic grading is more subtle. Because the student does
not have to worry about clarity, organization, and documentation in his or her programs,
the student spends less time planning and organizing. The lack of up-front organization
and planning in particular is evident from our experience with students in classes where
TAs do not have time to carefully read programs and grade on the quality factors.
Typically, a student writing a program sits down in front of a computer, and begins
typing with minimal planning. Then the student tries to test the program, discovers errors
(“bugs”), and starts to try to fix them (“debugging”). The process of debugging, however,
is hampered by the lack of clarity and good organization in the program. (We have seen
this problem almost every time we try to aid students in debugging. While they are
focused on fixing minor bugs, there are major problems with the overall clarity and
organization of the program that, in addition to being the source of as yet undiscovered
errors, make debugging nearly impossible.)
As the deadline for when a program is due nears, the typical student response is
not to question their method of writing programs. On the contrary, it is often a desperate
attempt to fix the program by a process of almost random changes. We have all had the
experience with students doing this, and for many students, this is their normal way of
writing programs. The complaint “I tried everything I could think of and it still doesn't
work” means that the student has tried a large number of minor changes to the details of
the program, without trying to alter its basic structure. In essence, the student is using a
“generate and test” method of programming, where he or she generates programs, and
then tests them to see if they happen to work.2 Eventually the student gives up in
frustration, seeks help from the TA or professor, or in some cases, copies a working
program from a classmate.
2 Writing a program to generate programs by such methods is a different matter; this is similar to
genetic programming.
Programming is Writing (Revised)
While it may be possible for students to learn small nuggets of information
using the “generate and test” method of programming, continued use of this method
spells their doom as programmers. In addition to the frustration and the time needed to
use the “generate and test” method, there are two fundamental problems with it. First,
students using it never learn how to generate correct programs in the way that experts do.
Experts form a plan, and mentally check it, revising it as needed, and only then do they
write the program. When an expert finds a bug, he or she is likely to go back and question
the overall plan. This process of refining plans, and learning how to write programs
quickly and correctly is the opposite of the “generate and test” method. It engenders deep
learning of concepts, tactics, and strategies. By contrast, the “generate and test” method is
more like playing a video game, in which the student notices what happens to work.
Because they are not directly refining their mental models, students who use this method
only learn how to write programs slowly, if at all.
Second, and more importantly, the “generate and test” method does not scale;
that is, it just does not work for programs that are larger than a page or two in length.
Oversimplifying, and ignoring feedback gathered from testing small segments of code3,
we can see this as follows. Suppose that in a program of 10 statements, each may have 2
“sensible” variants; for such a program one can generate 102 (100) programs. Assuming it
takes a minute to test each generated program, and that only one of these is correct, then
it would take, on the average, about an hour (102/2 minutes) to find a correct solution.
(This estimate matches our experience with students who use this method, and complain
about how long even short programs take to write.) However, in a program of 100
statements, if each has 2 “sensible” variants, students would average about 83 hours
(1002/2 minutes) to find a correct solution. Because it does not scale, the “generate and
test” method is not practical.
We would like to think that when students are faced with the failure of the
“generate and test” method would learn better methods for writing programs. Surely
some students do learn better methods. However, teaching students better programming
methods requires that at least the following4:
Having sufficient staff with enough time available to help students in the
critical moments when they are both frustrated enough to learn a better way
of programming and not so frustrated that they give up, and
Reinforcement of the lessons of planning and organization by grading
programs partly on issues other than correctness.
In the end, it is less time consuming to teach students how to design and
organize programs clearly as a class than to do it one-on-one with each student. Teaching
these lessons one-on-one is very time consuming, because it involves reading programs
carefully, getting students to see the value of advance planning, and correcting the
problems with the quality factors in their writing.
Furthermore, for such lessons to sink in, they must be reinforced by grading,
which means that TAs have to carefully read student programs to grade on these factors.
In a class with totally automatic grading, however, there is little incentive for students to
make a fundamental change in their writing method. In such classes it is all too easy for
them to learn (and for professors and TAs to teach) a quick technical nugget of
information. This lets them generate fewer programs (by lowering the average number of
variants they have to generate and test) while keeping their “generate and test” method of
writing programs.
By contrast, when students know that their programs will be read carefully by a
TA, and graded on the quality factors that reflect advance planning (clarity, organization,
documentation, and test or verification plans) they are forced to think and plan (to some
3 Of course, students should be encouraged to get feedback from small segments of code; doing so
is the basis for good modular design of a program.
4 The are undoubtedly other factors, such as a lack of adequate training in algorithmic thinking and
problem solving skills.
Programming is Writing (Revised)
extent) before they program. In such a situation, correcting students who fall into the
“generate and test” pattern is much easier, because the grading system emphasizes and
reinforces the value of advance planning. In addition, because the students are more
involved with the writing of their programs, they learn more and increase their
understanding as the programming assignments become more complex.
In summary, the lack of planning fostered by totally automatic grading leads to
student frustration and ultimately to lack of learning how to write correct programs. Since
totally automated grading of student work causes such problems, we agree with Kay, et
al., that automatic testing should be used only as a supplement to human judgement [8].
Such human judgement, of course, comes from carefully reading programs.
As a department, we first looked for problems with our teaching of
programming when we began to see evidence that our students were not learning how to
program well. One piece of evidence was that our students seemed to be less capable
programmers when they reached our upper-division courses. Other evidence came from
selective employers, who began to question the programming skills of some of our
seniors. In investigating these problems, we have come to believe that a major reason for
these problems is that student programs are not being read carefully during grading.
Because of this, too much emphasis has been placed on correctness issues, which,
although they are of first importance, should not completely displace the other quality
factors. This seems to be the reason why so many of our students have been using the
“generate and test” method of programming. As we described, this method of
programming simply does not work.
We recommend that programming classes emphasize the quality factors (clarity,
organization, etc.) in both teaching and grading. This is especially important for
introductory programming courses. While automatic testing of programs is useful, it must
be a supplement to careful reading of programs. As we described above, heavy reliance
on automatic testing to assign grades leads to the problems we have experienced.
As teachers, we believe strongly in student learning and in maintaining our
quality of instruction. Like others [3, pages 41-42] [7], we have noted the high correlation
between quality instruction in programming and sufficient human resources. Both our
experience and the analysis described show that quality instruction suffers when the ratio
of students to TAs begins to exceed 25. (More research needs to be done to quantify
acceptable ratios of students to TAs.) When the ratio reaches the point where automatic
grading must be used student learning suffers greatly. These problems are compounded if
they are found in the introductory courses, which should lay a foundation for
programming skills [4].
As the number of students per TA increases, something has to give. With ratios
that are too large, considerations of giving a quality education to students too often take
second place to administrative tasks, such as simply assigning grades to students. As we
described above, the use of automatic grading, while it seems to solve the short term
problem of assigning grades, leads to lack of understanding, as students focus on
correctness issues only, ignore the quality factors that are important in programming, and
use the “generate and test” method. To avoid student frustration and train programmers to
produce better quality software, it is essential to keep the ratio of students to TAs low.
Thanks to Harry Brearley, Mary Jo Brearley, Don Heller, Janet Leavens, Clyde
Ruby, and Akhilesh Tyagi for their ideas and comments on an earlier draft. Thanks also
to the anonymous referees for their suggestions.
Programming is Writing (Revised)
[1] B. W. Boehm. Software Engineering Economics. Prentice-Hall, Inc., Englewood
Cliffs, N.J., 1981.
[2] F. P. Brooks, Jr. The Mythical Man-Month. Addison-Wesley Publishing Co., Reading,
Mass., 1975.
[3] R. Dawson and R. Newsham. Introducing software engineers to the real world. IEEE
Software, 14(6):37-43, Nov/Dec 1997.
[4] N. E. Gibbs. The SEI education program: The challenge of teaching future software
engineers. Communications of the ACM, 32(5):594-605, May 1989.
[5] D. R. Hofstadter. Gödel, Escher, Bach: an Eternal Golden Braid. Basic Books, New
York, N.Y., 1979.
[6] P. Jalote. An Integrated Approach to Software Engineering. Springer Verlag, New
York, N.Y., 1991.
[7] D. G. Kay, J. Carrasquel, M. J. Clancy, E. Roberts, and J. Zachary. Managing large
introductory courses. SIGSE Bulletin: The Proceedings of the 28th SIGCSE Technical
Symposium on Computer Science Education, 29(1):386- 387, Mar. 1997.
[8] D. G. Kay, P. Isaacson, T. Scott, and K. A. Reek. Automated grading assistance for
student programs. SIGSE Bulletin: The Papers of the Twenty-Fifth SIGCSE Technical
Symposium on Computer Science Education, 26(1):381-382, Mar. 1994.
[9] H. D. Mills, M. Dyer, and R. Linger. Cleanroom software engineering. IEEE
Software, 4(5):19-25, Sept. 1987.
[10] E. Roberts, J. Lilly, and B. Rollins. Using undergraduates as teaching assistants in
introductory courses: An update on the Stanford experience. SIGSE Bulletin: Papers
of the 26th SIGCSE Technical Symposium on Computer Science Education, 27(1):48-
52, Mar. 1995.
[11] S. Shum and C. Cook. Using literate programming to teach good programming
practices. SIGSE Bulletin: The Papers of the Twenty-Fifth SIGCSE Technical
Symposium on Computer Science Education, 26(1):66-70, Mar. 1995.
[12] T. Standish. An essay on software reuse. IEEE Transactions on Software
Engineering, SE-10(5):494-497, Sept. 1984.
[13] Y. Wu and T. Baker. A source code documentation system for Ada. ACM Ada
Letters, 9(5):84-88, Jul/Aug 1989.
... Some computer science educators have expressed serious concerns about the teaching and practicing of intangible program qualities, such as those reflected by internal program documentation [10]. Without focus on such qualities the authors claim that many students will use a generate and test method of programming, which is characterized by ad hoc methods, minimal planning, and a "trial and error" approach to problem solving. ...
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It is difficult and challenging to comprehend the internal aspects of a program. The internal aspects are seen as contrasts to end user aspects and interface aspects. Internal program documentation is relevant for almost any kind of software. The internal program documentation represents the original as well as the accumulated understanding of the program, which is very difficult to extract from the source program and its modifications over time. Elucidative Programming is a documentation technique that originally is inspired by Literate Programming. As an important difference between the two, Elucidative Programming does not call for any reorganization of the source programs, as required by Literate Programming tools. Elucidative Programming provides for mutual navigation in between program source files and sections of the documentation. The navigation takes place in an Internet browser applying a two-framed layout. In this paper we investigate the applicability of Elucidative Programming in a number of areas related to internal program documentation. It is concluded that Elucidative Programming can solve a number of concrete problems in the areas of program tutorials, frameworks, and program reviews. In addition we see positive impacts of Elucidative Programming in the area of programming education.
Conference Paper
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Conference Paper
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Conference Paper
Computer professionals face dilemmas which demand both technical knowledge and an understanding of ethical principles and skills, but how to best teach these principles and necessary skills to students? ACM's Curriculum '91 and the problems inherent ...