In Search for an Ideal
Computer-Assisted Drawing System
yTakeo Igarashi, zSachiko Kawachiya, ySatoshi Matsuoka, yHidehiko Tanaka
yDept. of Information Engineering, zDept. of Information Science

The University of Tokyo
Hongo 7-3-1, Bunkyoku, Tokyo, Japan+81-3-3812-2111 ext 7413
ftakeo, tanakag@mtl.t.u-tokyo.ac.jp, fsachiko, matsug@is.s.u-tokyo.ac.jp

ABSTRACT Diagram drawing with conventional computer-assisted drawing(CAD) editors often tend to takeconsiderable amount of time despite their seeming ease of use. We analyzed the problems of such systems focusing on
the problem of cognitive overload, and observed that 1) the necessity of cognitive planning process in current CAD
system causes the problems and that 2) reducing the overload can lead to fundamental improvement in overall drawingeciency. We have conducted an experiment to verify these observations by comparing a typical drawing system and our
prototype drawing system called Interactive Beauti cation, which combines the ease of freehand drawing and precisionof traditional drawing editors by extracting various constraints in input strokes, and generating the desired diagrams
automatically. Results show that signi cant amount of time is spent for cognitive planning process, and reduction ofsuch planning time by Interactive Beauti cation can signi cantly improve the eciency of CAD.
KEYWORDS CAD, Interaction technique, Cognitive workload, Constraint hierarchy, Beauti cation.

1 INTRODUCTION
Computer-assisted, Object-Oriented(OO) drawing editors are now used widely, allowing precise drawings
not possible with traditional pen-on-paper drawing
techniques. Furthermore considerable research anddevelopment have been conducted on creating new
and innovative drawing systems resulting in thosewith abundance of functionalities. Despite such developments, drawing with such systems remains timeconsuming and non-trivial, especially for novice or
casual users.
The problem is that most research and development e orts on drawing systems have basically been`feature wars', adding new drawing capabilities andinteraction techniques, without deep, profound insights into their usability in practice. Instead weneed studies to nd \where does the fundamental
problem exist" based on the close analysis of drawing processes on practical drawing systems.
Based on such a motivation, we propose an abstract model of drawing to explain the problems withcomputer-assisted drawing(CAD) systems focusing
on the cognitive overload (Suchman, 87)(Norman, 86), and verify it through several experiments. In the model, we assume that the funda-

mental problem with CAD lies in considerable requirements imposed on the user's cognitive planning
process. By planning we mean the cognitive processduring drawing, where a user plans on the combination of the physical operations available in drawing
editors to obtain the desired image. In addition, we
con rm through an experiment with our prototype
system that the reduction of such cognitive planning
process can improve the eciency of CAD.
Our prototype system frees the users of the cognitive planning process by automatically generating
the desired diagram from freestrokes, but detailed
descriptions of the salient technical features of thesystem itself is not the purpose of this paper; rather,
we generalize on the e ectiveness of the approaches
to remove user's cognitive planning process from drawing editors.
This paper is organized as follows. First, we analyze the problems with CAD and propose an abstract model that captures the problems of excessive
cognitive workload. Second, we conduct an experiment to quantify the workload. Third, we introduce
a new drawing system where the objective is to reduce the workload, and conduct an experiment toquantify its e ectiveness versus traditional OO-based

drawing editors. Finally, we conclude with the future
directions of our the research.

2 A TASK MODEL OF DIAGRAM DRAWINGIn this section, the problems of excessive cognitiveplanning workload in current drawing systems areanalyzed and a task model to explain the problems
is proposed.

2.1 Problems with Current Drawing Systems
In some informal studies, the following problems havebeen observed for drawing on typical OO-based CAD
systems.

Problem 1: it takes considerable amount of time togure out how to combine the functions of editor
to draw desired diagrams.
Problem 2: it takes considerable amount of time
for users to realize that she has come to a \deadend" with the current drawing \plan", with which
the desired diagram can never be obtained.
For example, to draw a line perpendicular to aslope, one can either a) count grids, b) duplicate theslope and rotate it 90 degrees, c) draw a horizontal
and vertical lines and rotate them, etc. The user has
to identify and select at least one of the strategies,
requiring cognitive processing time. On the other
hand, if the user fails to select an appropriate strategy (for instance, to draw a slope and a line separately and adjust the line with free rotation, which
is often a wrong strategy because it is often dicultto obtain the exact desired angle with free rotation),a considerable amount of time is spent on `wasteful
operations'.
We explain these problems as the e ect of the overhead of the cognitive planning process. By planning,
we mean the translation of image in one's mind into
physical operations, where image is the concrete visual image of a diagram to draw, and physical operations are the executions of various commands(drawlines, draw rectangles, copy, ip, rotate, etc.) or
grid adjustment, etc. Problem 1 can be explained
as the planning process causing additional overhead
for each operation, and problem 2 can be explained
as failure during the planning process resulting in
wasteful operations.

2.2 An Abstract Model for Drawing Systems

To explain the problems and to clarify the structure of a drawing task, we propose a simple abstract
model of diagram drawing with a typical OO-based
editor(Figure 1). This model explains the interactions between the user's mental image of the desired

result of drawing, and physical drawing operations
the user performs during the drawing process.
Initially, the user has a visual image of the intended diagram in her mind. Then, to materialize
the image on the editor, she must translate the visual image into a sequence of commands available on
the editor(planning process)(A), and perform physical operations based on the planned command sequence. The operations are usually accompanied
by direct manipulation of visual images on the display(C). Notice that the resulting diagram on thescreen and the initial image in her mind has a visual
representation, while the command sequence has asymbolic representation. That is, the user must go
back and forth between the two representations.
For example, if a user tries to draw a tilted rectangle, the user rst plans a series of operations suchas creating a rectangle rst, and then rotating it(A).
Next, she executes the command operations physically, based on the plan by selecting the appropriate
menu items(B). Then, she directly manipulates thedrawn object to get the desired placement, size, andtilt(C). In this way, the drawing progresses repeating
the sequence (A),(B), and (C). This model does not
capture the entire aspects of diagram drawing, but
is sucient to explain the problems in CAD.
According to this abstract model, what we pointed
out in the previous section are the problems pertaining to the planning process (A), i.e., the transition
from visual representations to symbolic representations. However, many research activities in diagram
drawing systems focused on the process (B) and (C),while the planning process has not been extensively
explored. Our experiments show that large overhead
is due to such a planning process, and imply thatthe removal of the commands and planning process
dramatically improves drawing eciency.

2.3 Expert Users and Casual Users

Recently, Bhavnani,S.K. and John,B. extensively studied the improvement of CAD productivity, focusing
on the mental model of users (Bhavnani, 96). The
main observation is that CAD experts are employing
suboptimal strategies in drawing diagrams. Then,they identi ed a set of actual strategic knowledge
for using CAD e ectively and concluded that teaching these strategies to CAD users may improve theoverall productivity.
On the other hand, the basis of their cost analysis is the number of operations expert users have toperform in the editor to complete a particular task,
in a accordance with KLM (Card, 80) and GOMS

Symbolic Representation(A)
Actualizationof image[planning]command sequenceLINE RECTANGLE
COPY FLIP GROUP
GRID ROTATE ...

a)

b)

(B)
Execution ofplanned operations

Figure 2: Diagrams used in the experimentImage in the user’s mind

Visible feedback

Visual Representation

Diagram on the screen(C)directmanipulation

Figure 1: An abstract model of drawing editor(Card, 83) (Olson, 90). As a result, they do not cover
the task model where casual users may spend extra
time to nd out the appropriate operations, or time
being wasted for making and recovering from erroneous operations. Furthermore, their proposal, i.e.,
to make strategic knowledge explicit and teach it tousers, may not be applicable to casual users. We next
describe an experiment to quantitatively determine
the overhead of planning for casual users.

3 AN EXPERIMENT TO ESTIMATE THE
AMOUNT OF COGNITIVE WORKLOAD
3.1 Experimental Set-Up
In this experiment, we observe and video-record theusers drawing a given set of sample pictures using atypical OO-based drawing editor.
Systems. The machine used is an Apple Powerbook, and the drawing software is CANVAS 3.5 byDeneba Software.
Subjects. 14 casual CAD users who are accustomed to typical window-based GUI, and have useddrawing tools occasionally. They vary in their pro ciency in using CANVAS.
Task. Each user is rst given a brief tutorial of
CANVAS and conducts a series of short exercises
several times. Then the users are instructed in thefollowing manner:
 To draw as rapidly and clearly as possible,
 To draw in a natural way, as he would with drawing programs he is accustomed to.
 There is no need to reproduce perfectly the given
diagram, if the task appears to be too dicult.
Then the users draw the two given diagrams(Figure 2),verbally speaking out the task he intends to perform.
No time limits or feature restrictions are imposed so
as to simulate standard drawing activities.

We analyze the video recording of each subject,dividing and categorizing time spent for the following behaviors into three categories: the time spent
not pressing the mouse button(NON), the time spent
during which the objects are directly operated uponwith a mouse(OBJ), and the time during which themenus are selected with a mouse(MENU). Next, we
add explanations of the user's activities to each unit
by referring to the video recordings. For example,
to draw a line, a user rst moves the cursor to atool menu (NON), and selects the `line' palette(MENU),
After the selection, the cursor moves to the starting
point (NON), and then to the end point(OBJ).

3.2 Results

Based on the basic data collected as described above,
we perform detailed analysis of the diagram drawing.
The analysis consists of two parts. One is local level
analysis, where the time consumed during each individual operation is calculated without considering
its respective meaning or role in the total drawing sequence. From this analysis, we can examine detailed
information on how quickly subjects performed eachstep. The other is global level analysis, where theunderlying meaning of each step is analyzed with respect to its role in the total drawing sequence. This
analysis is expected to reveal the nature of trial anderror sequences during the drawing process.

Local Level Analysis

Figure 4 shows the histogram of the categorized operation steps classi ed by the time they required.
First, we observe that some operation steps consume
considerable amount of time. If each step consists ofa simple physical operation which does not requirecognitive processing, the time consumed during eachstep should stay relatively constant. However, thehistogram shows that the time for each step varies
widely, where there are some steps that exceed 6-8
seconds, indicating that extra time is consumed forcognitive processing.
We investigated such time-consuming steps by analyzing the videotaped verbal protocols and observedsome common phenomena: A user fails to obtain his desired result because of
inappropriate grid settings(Figure 3); and further-±­Drawing Line°­¯²

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Figure 3: An example of time consuming operation: the user wants to draw a perpendicular line,
but it is impossible in this grid setting. Further,it takes time for him/her to realize it.

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then others, which can be seen as the result ofe ect of cognitive processing.

more fails to realize this fact and repetitive futile
attempts are made to draw.
 The mouse pointer makes aimless movements and/or
momentarily pauses, failing to make any meaningful contributions to the intended drawing. Rather,time is seemingly spent on planning, e.g., wondering whether to draw the slope rst or the box rst
in Figure 2a.
We regard these phenomena as the manifestationof cognitive workload because these time losses are
not directly relevant to physical operations or the nal result. In the following, we calculate an estimateof the percentage of the time spent on cognitive processing during the entire drawing process.
In order to obtain the basis of the estimate, we
performed a series of experiments, where subjects
are speci cally instructed to perform a certain operation, such as to select a rectangle in the tool menu
and draw a rectangle, or to select an object and ` ip'
it along the x-axis by selecting the ip command inìãÌíâïÞñæìëð

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Ñæêâ¥Ðâà¦Figure 5: The number of operations categorized
by the time they consumed in a task where noplanning is necessary: all operations terminate
within 2-3 seconds.

the menu bar, etc., thereby eliminating their cognitive planning workload. The results are shownin Figure 5, which show that time spent for suchphysical operations is short and uniform, taking approximately 2-3 seconds each. This result is in accordance with the results reported in previous work
(Card, 83)(Olson, 90).
Based on this result, the time spent for cognitiveplanning process is calculated by basically subtracting the physical operation time from the total drawing time. The actual calculation is based on the following equation:

P[Eq.1] cognitive processing time
= ( max(0 , t(x) - threshold) )[x 2 all steps],
wheret(x) = time spent for step x,
threshold = assumed average time required foreach physical operation.
In the equation, we make a simpli cation that theassumed average time for each physical operation remain constant. This is because with the current experiments, the distinction between the physical operation versus cognitive planning cannot be clearly
determined. So instead, we assume a constant average threshold for physical operations, and any timespent for each operation exceeding this threshold isassumed to be spent for cognitive planning process.
Based on the baseline experiments in Figure 5, we
varied the threshold value between 2-4 seconds. The
result is shown in Table 1 | here, we estimate thatroughly 20%  40% of the time is spent for cognitiveplanning process.

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In this analysis, the role of individual operations in
the context of the entire drawing processes is examined. Then the `wasteful' operations, whose e ects
are not re ected in the nal results, are extracted,
and the total time spent for such wasteful operations
is calculated.
For example, a subject in the experiment tried to
draw Figure 2a by drawing a rectangle and a triangle
separately, and then rotating the rectangle so that itsedge will be parallel to the slope of the triangle. However, he could not do so because it is not possible tospecify a rotation angle explicitly in CANVAS otherthan integer values (e.g., if the necessary rotation angle was 35.5 degrees while it is only possible for theuser to specify either 35 or 36 degrees.) We observedthat it took considerable amount of time for the subject to become aware of the nature of the problem.
After numerous futile attempts, the subject gave up
and deleted the rectangle. Here the time used for theoperations from the generation to the deletion of therectangle is calculated as `wasteful' time.
Figure 2b posed even more diculties for mostusers. For example, it was dicult to draw the legs,
and achieve proper symmetry; either the limbs would
not properly join, the half-circle signifying the footcould not be merged, or the leg lines would end up
not being parallel, depending on the user's initialplan. This seemed to surprise and frustrate the users,
because of the apparent simplicity of the gure, plus
the fact that minor local adjustments usually failed.
Figure 6 shows the results. The amount of wasteful time varies among subjects, but the existence ofwasteful operations, and its e ects are clearly observed. (One may notice that there is not a strong
correlation between time for g 2a and g2b. This isbecause the amount of wasting time greatly changes
depending on accidental failures rather than subjectspeci c reasons which are constantly observed.) Thus,
we claim that, it is not only essential for the user interface of drawing systems to help in decreasing theÓÞéæáÌíâïÞñæìëð
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Table 1: The ratio of time required for cognitive
processing to the total drawing time: this gure is
the result of the calculation of cognitive processing
time based on [Eq.1].

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Figure 2b

Assumed threshold (Sec)
4.0
3.0
2.0
22.50% 30.90% 45.10%
17.6% 26.7% 41.5%

Figure 6: The time used in the wasteful operations: numbers 1a-14a indicate the drawing of
Figure 2a by the 14 subjects and numbers 1b-14bthat of Figure 2b accordingly: this gure showsthat some subjects spend considerable time onwasteful operations.

amount of time required for an optimal drawing procedure, but also to prevent users from making a plan
which would actually not lead to the desired results.

4 REQUIREMENTS FOR AN IDEAL DRAWING SYSTEMSThe experiments showed that considerable overhead
is due to the cognitive planning process for casual
users of OO-drawing systems. Thus, alleviating theoverhead of planning process can fundamentally reduce the overhead. In other words, helping users totranslate images to operations and freeing user of thistranslation are some of the most essential approachto improve drawing systems.
Based on the proposed abstract model of Figure 1,
this means that improvement of the process (A) is
critical for improvement of the overall drawing systems. However, most research focused mainly on
process (B) and (C), whereas only few has explicitly intended to improve (A).

4.1 Previous E orts on Improving the Drawing Systems

Previous constraint-based layout approaches such as(Bier, 86), (Bouma, 95), (Nelson, 85), (Borning, 81),
(Sutherland, 63), etc. allow drawing precise diagrams
without wasteful operations during (C), but most
systems required the users to state necessary constraints explicitly, which can increase the user's cognitive burden during (A). Adding new features andfunctionalities, which are often the strategies with

commercial editors, can decrease the number of required operation steps in (B) or possibility of mistakes in (C), but the added complexity also can increase the cognitive burden in (A). Programming-byexample approaches (Kurlander, 92) (Lieberman, 92)(Maulsby, 89) could be regarded as decreasing theburden of (B) and (C), but in practice could impose
considerable overload when the user has to carefullyplan what are the `right' set of examples to present
to the system. Gesture based systems (Apte, 93)(Zao, 93) (Rubine, 92) (Gross, 96) can free the usersof menu selection in (B), but gestures in these systems correspond to the individual commands in OObased systems, and the problem of planning remains.
Beauti cation systems (Pavlidis, 85) intend to freeusers of ne placement of objects in (C), but the initial drawing process would be basically the same as
in OO-based systems.
The work of Saund et al.(Saund, 94) can be viewed
as helping the users during process (A). Their system, PerSketch, allows the system to understand theperceived image of the drawing in the user's mind
and translate it into a system representation. However, it only handles the movement of sketch objects,
and does not deal with precise drawings.

5 INTERACTIVE BEAUTIFICATION: an
Experimental System to Reduce the Planning Overhead

We are currently developing an experimental drawing system called Pegasus, which assists the planning
process (A) of user drawings. We call the interaction technique Interactive Beauti cation, which reduces the overload of the planning process by removing editing commands entirely from diagram drawing
process. Here, we investigate how the system could
improve the drawing task. 1
We rst give an overview of the interactive beauti cation in Pegasus. First, the user draws an approximate shape of his desired diagram with freestroke (Figure 7(A)) using a pen or a mouse. Then,the drawing system infers the desired precise diagram based on the perceptual constraints between
the input stroke and surrounding context, and displays the multiple possible beauti ed candidates on
the screen (Figure 7(B)). In this diagram generation,
the system automatically detects higher level constraints such as similarity or symmetry. Among theseveral possible candidates, the system returns the
1 As we have mentioned, the evaluation of the system itself
is not the purpose of this section, but con rmation of thevalidity of our proposal is the current objective.

A) User draws an approximate
shape with a free stroke.
B) System generates multiple
candidates based on theextracted constraints.
C) User selects the desired
diagram among the displayed
candidates.

Figure 7: Interaction of the proposed system

Figure 8: Sample image of the prototype system
on Windows 95: the user is selecting a desired
candidate using a slider.

most con dent one as the primary candidate. If theuser is satis ed with the candidate, he can proceed
on to the next stroke without any additional operations(Figure 7(C)). Otherwise, the user can selectthe desirable one using a slider that appears near the
cursor(Figure 8). During the selection, the system
displays the constraints satis ed by the currently selected candidate visually, which makes rapid and precise interaction possible. Overall, users can draw a
precise diagram without abstract manipulations suchas gridding or rotation.
Interactive Beauti cation resolves the ambiguityof input strokes and allows the usage of complex
constraints that were not easily speci able in previous OO-based drawing systems. We construct a
constraint hierarchy consisting of a set of weighed
constraints extracted from the input stroke and surrounding context. Multiple candidates are generated
by changing the strength of the constraints in theconstraint hierarchy (Sannella, 94). The use of a constraint hierarchy is superior to other ad hoc strategies in that it e ectively generates plausible candidates by separating constraint solving and constraint
abstraction. (Detailed description of this algorithm
and prototype system will be given in another paper.)At a glance, the system may seem similar to ex-5.1 Implementation

The prototype system is being developed with Microsoft Visual Basic and Visual C++ on Windows
95. Sample drawings are shown in Figure 8. The
currently implemented constraints are coincidence ofedge points, horizontal / vertical alignment of edges,
equivalence of segment length, parallelism / perpendicularity among segments, and horizontal and vertical symmetry. Time required for generating multiple candidates is about 100 msec (IBM PC/AT,
Pentium 75MHz), sucient for interactive drawing.
Constraints for curves are currently being worked on.

5.2 Experiment

Using the prototype system described above, we performed an experiment to investigate whether reduction of planning process could really reduce the cognitive workload and improve eciency of drawing
processes.
In the experiment, the same 14 subjects as theprevious experiment were instructed to draw the diagram shown in gure 1a, and the drawing processes
were recorded and analyzed as had been with the previous experiment. Subjects were given a brief explanation of the prototype system, and practiced withthe system for approximately one minute.
Figure 9 shows the time required for each subject
to complete the drawings. On the average, drawing time for our prototype system was approximately
43% of that for the OO-based system. This indicates

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Ñæêâ¥Ðâà¦isting sketch-based interfaces including commercial
ones such as Apple Newton, GO's Penpoint, andother freestroke mode in drawing softwares (SmartSkech,
Corel Draw, etc.). These systems accept freestroke
input and convert them into vector segments. They
also automatically connect nearby segments, straightenlines, and recognize geometric primitives (circle, rectangle, etc.). However, Pegasus is di erent in that
if handles complex, global constraints such as parallelism, symmetry, or similarity, which requires global
analysis of the drawing, besides basic constraints sucha connecting adjacent points. Considering such higherlevel constraints is essential in freeing the users of
the planning process. For example, if one wants todraw a symmetric diagram precisely in existing systems, he must draw one half of the desired diagram,and use explicit commands (copy, ip, and move) tocomplete the drawing. If any editing occurs, suchcommand actions have to be repeated every time. In
contrast, desired symmetry could be automaticallyaccomplished in our system without little or no additional operations.

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Figure 9: Time required to draw gure 2a in
CANVAS and in the prototype system: per average, drawing in the prototype system requires
only 43% of the drawing time in CANVAS.

Figure 2aAssumed threshold (Sec)
4.0
3.0
2.0
11.8% 19.3% 34.3%

Table 2: The ratio of time required for cognitive
processing to the total drawing time in the prototype system.

that the time required in our system is shortened
and more stable. The result of local level analysis is shown in Figure 10 and Table 2. The ratioof cognitive processing time is reduced to 12% 
34% compared with 17%  45% for that of CANVAS. Investigation of Figure 10 shows that SELECT
operations (selection of a candidate by a slider), is
the sole operation that takes some time to complete,and other operations (NON and STROKE) nish quickly.
Furthermore, future improvements of both the interface for SELECT and the associated constraint solving
algorithm may improve the time for SELECT. Lastly,
we observed that the wasteful operations due to themisunderstanding of the system behavior and wrong
planning are greatly reduced. The quality of thedrawings were not quanti ed in these experiments,
but no conclusive di erences in quality between thetwo experiments were observed.
The result shows that reduction of planning timeachieved in the prototype system improved the efciency of diagram drawing; it remains to be seen,
however, whether our proposed Interactive Beauti cation is applicable to a wider range of applications.

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Figure 10: The number of operations categorized
by the time they consumed in the prototype system: STROKE means the free stroke input, and
SELECTION means the candidate selection by
the slider.

6 CONCLUSION

We have conjectured that, for casual users, the necessity of cognitive planning process results in major
overhead in current OO-based drawing systems, andinteraction techniques to fundamentally reduce planning overhead improve the overall drawing process.
Our experiments to verify the conjecture showedthat planning process consumed about 17%  45%of the total drawing time, and that wasteful operations consumed up to 78% of total drawing time. By
contrast, our prototype Pegasus drawing system with
Interactive Beauti cation, exhibited signi cantly reduced planning process time, or approximately 12% 34% of the total drawing time. Furthermore, due
to the reduction of wasteful operations, the overalldrawing time is reduced to 43% of the drawing timewith conventional OO-based drawing system.
As a future work, we must perform a more detailed and comprehensive comparative studies among
other types of drawing systems, using a more extensive set of drawing samples. We also need to makeour drawing system more robust by incorporatingother kinds of constraints and their solvers especiallycurves, and furthermore improve the SELECTion interface for multiple candidates.

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