Software Visualization
Space Filling Approach &
Semantic Zooming
Siva Venkatachalam
03/23/2004
Articles covered
► Marla
J. Baker, Stephen G. Eick, Space-Filling
Software Visualization, Journal of Visual
Languages and Computing, 6(2), 1995, p119133.
► K.L. Summers, T.E.Goldsmith, S.Kubica,
T.P.Caudell, An Experimental Evaluation of
Continuous Semantic Zooming in Program
Visualization, IEEE Symposium on
Information Visualization, 2003.
Software Visualization
►
Software visualization is the use of
computer graphics and animation to help
illustrate and present computer programs,
processes, and algorithms.
Space filling approach
►
►
The space filling approach used in this
paper is based on the generalized idea of
treemaps developed by Shneirderman,
which aids in showing hierarchical data.
The hierarchy for a software system is
Software system  subsystems 
directories  files.
Motivation
The main motivation for such a system is
to gather information on:





Subsystem information
Directory information
Error-prone code
Recurring problems
System evolution
Typical software system
information
►
►
►
►
►
Non-commentary source lines (NCSL)
Software complexity metrics
Number and scope of modifications
Number of programmers making
modifications
Number and type of bugs
Approach
The approach adopted in this paper is very
similar to that of a treemap approach.
► A rectangular space is partitioned into
subsystems depending on its total NCSL.
► A subsystem is in-turn divided into
directories, which in-turn contain files.
► The software visualization technique
developed is implemented in a system called
SeeSys.
►
Space Filling Approach
• Three subsystems X, Y, Z
• X subsystem has 5
• Filling may indicate the
directories
amount of new NCSL
• In terms of %, directory 5
has the largest change in
NCSL.
• Subsystem Y with its
internal directories and
files
• The leftmost directory
contains 5 files and the
shading may be indicative
of new NCSL.
1. Subsystem information
►
Questions:
 Which subsystems are the largest?
 Where is the new development activity?
►
Procedure:
 The outermost rectangle represents the
total size of the system.
 The individual rectangles denote the size
of the subsystems in NCSL.
 Color coding is used to encode the size of
the individual subsystems.
Subsystem Information
Three for
NCSL
of the
subsystems
t during
largest
various
with
subsystems
the
releases
most (visually)
development activity Color codes
2. Directory information
► Questions:
 Where are the large directories?
 Is there even distribution of
►Large
and small directories
►New development between directories
 Which directories are stable?
 Which directories have the most activity?
Directory level detail
Back
Almost no
development
Subsystems n
have the
no large
directories
e and D
Z have
largest
directories
Max development
Zoomed view of subsystem t
3. Errors in code
► Questions:
 Which subsystems and directories have
the most errors (bugs)?
 How much of the development activity is
apportioned to.
►Fixing bugs.
►Adding new functionality.
Bug rates by subsystem and directory
Max bug fix rate
Mostsize
The
development
of the subsystems
activity are based
Lesser
onbug
the fix
new
rate
NCSL
compared
during the development of the system
to new development
4. Recurring error problems
►
Questions:
 Are the bugs really fixed or are they a recurring
problem?
 Are there any components that would need
complete restructuring or reconstruction?
►
Fix-on-fix rate
 A fix-on-fix bug is a software bug correction
that modifies an earlier bug fix.
Fix-on-fix rates
The area representing
each subsystem
and directory
Subsystems
i and K have
high fix-on-fix
rates is proportional
to the number of bugs. The fill area represents the fix-on-fix rates.
5. System Evolution
► Questions:
 Which were the major software releases?
 Have any subsystems shrunk or
disappeared during releases?
 What is the rate of growth for subsystems
and directories?
 Where has the development work been
done historically?
System Evolution
► SeeSys
animates the display over the
evolution the code.
► The bounding rectangle represents the
maximum size of the subsystem across all
releases. The filled portion is the amount of
development during that particular release.
► A set of frame sliders control the display
frame.
Code growth animation – frame 1
Frame sliders
Code growth animation – frame 2
Code growth animation – frame 3
Evolution changes
►
►
►
►
Subsystem O has disappeared.
Subsystems F and J have shrunk.
Subsystem D has a slow growth at first, but
a faster growth during later releases.
Subsystems t, k and Z are the fastest
growing subsystems.
Link
User interaction
SeeSys uses mouse movements to interact with
the system. The active component at any
particular time is indicated by a red highlighted
boundary.
► The available statistics are displayed on the
lower left corner.
► There are five buttons that can be used to
change the appearance on the screen.
► There are sliders that control the number of
rows to be displayed and the animation.
►
Display principles
► The
visualization principles used in SeeSys
 The individual components can be assembled to
form the whole.
 Pairs of components can be compared to see
how they differ.
 The components can be disassembled into
smaller components namely subsystems and
directories.
SeeSys properties
► Screen
real estate
 The total screen space (100%) is utilized as the
rectangles are placed next to each other.
 To view the smaller components in greater detail, the
zoom feature can be used.
► Spatial
Relationship
 Example: Comparing the new development activity by
subsystem involves making all subsystems of the same
size.
► Color
 Color could be used to encode various attributes like
NCSL, age, complexity, activity, number of programmers
etc of the software system.
New development by subsystem
Continuous Semantic
Zooming
An Experimental Evaluation
Zooming
► Geometric
Zooming
 This kind of zooming simply provides a blowup
of graph content. (E.g. Zooming a picture)
► Semantic
Zooming
 This kind of zooming means that the
information content changes and more details
are shown when approaching a particular area
of the graph. (E.g. plots in Matlab)
 Issues
►Providing
the appropriate level of detail is a
challenge.
Examples of Visualization tools
Flat representation of
all the data items on
the available screen
space
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Objective
► Study
the effect of viewing visual programs
on the users’ understanding.
► Comparison of three methods
 Flat zooming
 Semantic zooming
 Continuous semantic zooming
Semantic Zooming
► Details
in the zoom area become more
distinct.
► Changes in the representation as the result
of the zoom.
► Example: Procedural programming
► Drawback:
 When viewing the top-level program, the
contents of the procedures are hidden and viceversa.
Programs without procedures
Encapsulated procedure and details
Continuous Semantic Zooming (CSZ)
► CSZ
uses the concept of semantic zooming
with blending and proximity.
► This allows the user to view both the
procedure details and the details of the
higher levels (focus + context).
► The smooth transition between levels helps
maintain the mental map of the
representations.
Continuous Semantic Zooming
Zooming with top-level details hidden
Evaluating CSZ
► The
evaluation of the CSZ method is done
by comparing it with flat and semantic
zooming.
 Flat representation contains no procedures.
 Semantic representations have procedures.
Zooming
Hierarchical view
with procedures
Flat representation with all procedures exploded
Evaluation Study
► The
evaluation consisted of two pilot studies
and one main study.
► Metrics
 Time to find the elements.
 Accuracy
► Pilot
studies were mainly done to calibrate
the metrics.
 Compares only the flat and semantic
representations.
Example program
►
Available polygons
 Lines, triangles,
rectangles & ovals.
►
►
The polygon’s
relationship between
one another is
determined by the
connections between
program elements.
SGPL – Simple
Graphics Programming
Language.
Pilot Study 1 – Program output
Pilot study 1 - Flat representation
Pilot study 1 - Hierarchical representation
Pilot Study 2 - Program output
Pilot studies - Observations
► The
pilot studies were conducted to
corroborate that the measures were
sensitive enough to detect differences in
time and accuracy.
► Observations
 Pattern matching was used for simple polygon
identification.
 Remedies
►Add
more similar features
►Add complexity
Main study
► Comparisons
between flat, SZ and CSZ
representations.
► 60 subjects, 20 per method
► 14 tasks
► The picture contained 164 polygons and the
corresponding program contained around
200 elements.
Picture used for study
Results
Scatter plot
Results (cont’d)
Results
Z scores
The more negative
the Z score is the
higher the value.
Conclusions
► CSZ
is a better method for visualizing
complex programs when compared to flat
and SZ representations.
► Distinct advantages
 The context is not lost (multi-level views
possible)
 Smoother transition between views
 The more complex the program gets, the better
the method is over the others.
Program Screen
Future work
► The
user’s understanding of textual programs
could be tested using this procedure.
► The reasons for the difference between SZ and
CSZ need to be ascertained and also whether the
subjects made use of all the information given to
them (these would provide new venues for
research).
► Extension of CSZ to real world problems
(visualizing parallel programs).