J. Software Engineering & Applications, 2010, 3, 1015-1026
doi:10.4236/jsea.2010.311119 Published Online November 2010 (http://www.SciRP.org/journal/jsea)
Copyright © 2010 SciRes. JSEA
Software Project Visualization Using Task
Oriented Metaphors
Amaia Aguirregoitia1, José Javier Dolado Cosín2, Concepción Presedo1
1Department of Languages and Computer Systems, University of the Basque Country, La Casilla, Bilbao, Spain; 2
Department of
Languages and Computer Systems, University of the Basque Country, Pº Manuel de Lardizabal, Donostia, Spain.
Email: amaia.aguirregoitia@ehu.es
Received September 13th, 2010; revised September 30th, 2010; accepted October 8th, 2010.
This paper presents T-Cube and MetroMap , two new graphical represen tation models for con trolling and manag ing the
processes of software project development. They both use metaphors and visual representation techniques to address
typical project management tasks. T-Cube uses a metaphor with the Rubik-Cube whereas MetroMap uses a metaphor
with a metro map. The tools have been tested on real project data and a qualitative assessment shows the results of
testing the visualizations with users attempting several information retrieval tasks. The utility of the tools has been
positively evaluated and the article demonstrates the possibilities of visual approach es in project management.
Keywords: Project Management Information Systems, Managing Information Systems, Managing Integration
1. Introduction and Related Work
Management of software development processes in-
volves a group of complex activities to keep projects on
schedule and to ensure their quality. Managers require
different types of data such as written reports or software
metrics about productivity, quality, adherence to sche-
dule and budget. Our research focuses on the design of a
tool to assist in exploration and analysis of the required
high volumes of data. The purpose of our work is to
develop a proposal to efficiently process visual queries
on the key measures for software development manage-
The use of visual tools in the software area is not a
new issue, but prior research on software visualization
(SV) has mainly focused on the representation of the
technical implementation aspects of the project. More
specifically, it has represented source code or data
structures with the primary objective of understanding
and improving the code. Animation of program behavior,
presentation of relationships among functions and inter-
actions for better comprehension or computation visuali-
zation [1] are some other areas of activity. Source code
changes or architecture aspects are frequently repre-
sented using running time diagrams indicating function
or module calls. Some other representations include graphs,
statistics or relatively traditional diagrams representing
certain code or performance aspects [2] and most of them
have the primary objective of improving the code [3].
Augur [4], a comprehensive visualization system for
configuration management of repositories of source code,
contains multiple, dynamic views of data in an attempt to
unify views of “activities” and “artifacts” and offers
project’s source code, author network, and other develop-
ment statistics. The system StarGate [5] is an infor-
mation visualization project that uses different tech-
niques to assist the user in understanding the complex
interactions between developers and software. It visualizes
the code repository and social network of developers
associated with a software project in one integrated
One of the most frequently used graphical resources to
model modern software applications is the Unified
Modeling Language (UML) package diagram. The UML
is the de facto standard for modeling modern software
applications. It is typically used during the design phase
of the software lifecycle to graphically represent
different aspects of the system’s high-level architecture.
There are also some other widespread formal and
informal diagrams for the static view [6] such as class or
component diagrams. In addition to a static view, a
functional view can be provided through a use-case
diagram with color-coding, which offers a quick overview
of the functionalities of the present development state.
Software Project Visualization Using Task Oriented Metaphors
The functional view of status visualization is not an
alternative to the static view, but a complementary view.
Some other alternatives to visualize software archi-
tectures can be found in [7].
In contrast to SV, visualization in the software project
management area is not so much concerned with the
construction, but with the analysis of the development
process and its metrics. This area is becoming
increasingly important in software engineering activities
and much research has been recently dedicated to
designing software metrics, initiatives for software
process development improvement and decision making
[8]. However, software measurement is a complex field
and there are also a number of experiences reporting the
problems in the measurement area, such as “most
software engineers and managers did not use the
measurement program”, “the data suppliers did not
understand the definitions of metrics and measures”, “the
project indicators focused on resources and deadlines and
ignored the quality of software and few people were
capable of interpreting the presented data” [9].
Another main issue is how to depict visually the
measures to supply the information for software project
management. At present, Gantt charts are predominantly
used for the mapping of projects in organizations. While
they are an effective graphical resource for planning a
project they are not effective for communication
purposes, especially when different groups are involved
Some previous work in the field has led to the
development of visualizations for software management
but most of them address specific problems and are
mainly focused on metrics of particular areas. One of
them is Tarantula [11], which is useful for finding likely
faulty sections of the code and gives the developer
information about the results of a faulty program's
execution on an entire test suite. Another example,
SVAW [12] offers visual representations for assistance
to human schedulers.
However, there are some other visualizations which
are unknown and rarely used that have been reported as
exceptionally powerful in the communication area such
as the tube map proposal in [13]. The metro map is a
well-known graph that is widely used in illustrating
transportation networks. The metro map metaphor has
also been used effectively for visualizing abstract
information such as conceptual ‘‘train of thought’’
networks, biochemical pathways involved in the spread
of cancer, networks of related book titles, and websites.
Furthermore, it has served as a tool for the organization
of learning resources and for the distribution of project
planning information within an organization. The metro
map can be used as a powerful metaphor for the
presentation and association of certain information and it
provides a coherent overview of a complex system.
Moreover, previous works consider it a useful visuali-
zation technique for non-spatial data [14]. It has been
established that a wide array of users find the metro map
intuitive and engaging when navigating abstract data and
that it is also a useful way to provide content and detail
Concerning the project management area, previous
evaluations have shown that the Metro Map visualization
can be a powerful metaphor for communicating a
complex project to different target groups. In addition,
employees like the metaphor because it provides both
overview and detailed information in one image and it
has been reported as understandable by employees
[10,13]. Here we investigate further the possibilities of
using this metaphor to display information for effective
software development management.
The remainder of this paper is organized as follows.
The next section discusses the results of some studies in
the field of Software project management and it presents
the list of key measures used in our approaches. Section
3 and Section 4 describe the two visualizations and
Section 5 compares both of them. We present the results
of a preliminary assessment in Section 6 and summarize
our conclusions in Section 7.
2. Software Project Management Metrics
2.1. Definitions
In this section, we provide definitions for some relevant
concepts that will appear in the paper. A software project
is the set of work activities, both technical and
managerial, required to satisfy the terms and conditions
of a project agreement. A software project should have
specific starting and ending dates, well-defined objectives
and constraints, established responsibilities, and a budget
and schedule [16].
Project management is a system of procedures,
practices, technologies, and know-how that provides the
planning, organizing, staffing, directing and controlling
necessary to successfully manage a project [17].
Measurement of diverse entities is fundamental to
understand, control and manage a software project. The
term entity refers to any distinguishable object in the
empirical world for which a measurement can be applied
[18]. In our research, the tasks of the software develop-
ment processes are the entities to be measured. The term
attribute refers to the property of an entity that can be
determined quantitatively, that is, for which a magnitude
can be assigned. The measurement of an attribute of an
entity is the characterization of that attribute in terms of
Copyright © 2010 SciRes. JSEA
Software Project Visualization Using Task Oriented Metaphors 1017
numbers or symbols.
2.2. Task-Based Software Project Management
Classical project management functions can be grouped
into Planning, Organizing, Staffing, Directing, Controlling
and Integrating [19]. During the planning process, the
manager must set goals and scope, identify what is to be
done (create Work Breakdown Structure), identify tasks,
estimate size, estimate effort, identify task dependencies,
assigns resources and schedule work. The project manager
often starts with the definition of a list of activities or tasks
to be planned, performed and controlled regardless of the
breakdown method used in the project (process, product,
organizational…) or the lifecycle considered (Waterfall,
“V”, Spiral…). This task definition is used for estimation,
scheduling, and provides the basis for milestone and
deliverable specification. The most widespread scheduling
techniques used in software project management as PERT,
CPM, milestone diagrams or Gantt charts are based in the
definition of tasks, activities and their dependencies [20].
The task-based definition of the project stated at the
planning stage establishes the foundation not only for
schedule and cost monitoring but also for the overall
project control. The two visualizations in this paper share
the task-oriented perspective to depict the information.
2.3. The Key Measures
The information to be presented using the metaphor
came out as a result of the analysis of data from several
works and surveys in the software project management
area [21,22]. A list of measures for each task involved in
the development process is presented in Table 1. The
visualizations described below present all these measures
for each task and put them into visual form in such a way
as to promote a deeper level of understanding and insight
into the data and amplify cognition. Diverse task arrange-
ment and visualization techniques are used to gain
understanding on the aforementioned measures.
3. The Treemap Hypercube Metaphor
3.1. Treemaps and Metaphors
Metaphors are an important tool in information visuali-
zations as they provide familiar cognitive models to help
users to browse unfamiliar information spaces [23]. Six
advantages of Visual Metaphors have been described in
previous works: 1) to motivate people, 2) to present new
perspectives, 3) to increase remembrance, 4) to support
the process of learning, 5) to focus attention and support
concentration of the viewer, 6) to structure and coordinate
communication [24].
The T-Cube proposal applies the benefits of metaphors
as well as a visualization technique known as Treemaps.
Table 1. List of measures.
Task effort
Estimated task effort
Task cost
Planned task cost
Number of Requirement failures
Number of Design failures
Number of Code failures
Number of Documentation failures
Number of other type failures
Number of total failures
Number of failures detected by the client
Number of failures detected by the developers
Requirement failure detection effort
Design failure detection effort
Code failure detection effort
Documentation failure detection effort
Other type failure detection effort
Failure detection total effort
Number of Requirement reviews
Number of Design reviews
Number of Code reviews
Number of Documentation reviews
Number of other type reviews
Number of total reviews
Requirement failure correction effort
Design failure correction effort
Code failure correction effort
Documentation failure correction effort
Other type failure correction effort
Failure correction total effort
Number of changes required
Number of changes rejected
Number of changes implemented
Number of changes pending
Number of deliverables planned
Number of deliverables rejected by the client
Number of deliverables accepted by the client
Number of pending deliverables
Number of detected risks ( with description and type)
Effort deviation
Cost deviation
Risk detection effort
A Treemap is a space-constrained visualization of hierar-
chical structures. It is very effective in showing attributes
of leaf nodes using size and colour coding. Treemap
Copyright © 2010 SciRes. JSEA
Software Project Visualization Using Task Oriented Metaphors
Copyright © 2010 SciRes. JSEA
enables users to compare nodes and sub-trees even at
varying depth in the tree, and help them spot patterns and
exceptions. The Treemap was first designed by Ben
Shneiderman during the 1990s. It is extremely efficient
to represent extensive attributes (sizes, costs, value) of
elements arranged in a hierarchy. In a treemap each node
has a name and an associated size. The size of the leaves
may represent, for instance, the effort of individual tasks;
the size of non-leave nodes is the sum of the sizes of its
children. The treemap is constructed via recursive
subdivision of the initial rectangle. The size of each sub-
rectangle is proportional to the size of the node. The
direction of the subdivision alternates per level: first
horizontally, next vertically, and so on. As a result, the
initial rectangle is partitioned into smaller rectangles,
such that the size of each rectangle reflects the size of the
leaf. The structure of the tree is also reflected in the
treemap, as a result of its construction. Colour and
annotation can be used to give extra information about
the leaves [25]. This technique has been applied to a
wide variety of domains: to present large number of
images grouped by directory, to analyze file systems,
financial analysis [26] or sports reporting [27].
Most of us played with a Rubik Cube when we were
children, so we use this metaphor as a way to leverage its
familiarity to enable users to better understand the tool
and how it organizes tasks. In a Rubik cube, the six faces
are covered by nine stickers of six solid colours. A pivot
mechanism enables each face to turn independently, thus
scrambling the colors. For the puzzle to be solved, each
face must be a solid color.
The purpose of using the metaphor with the Rubik
cube is to assist in the comprehension of the task
structure using a rotation mechanism in the visualization.
The Rubik cube is composed of smaller cubes and in
order to solve it one needs to do two things: rotate the
cube to see the different sides one by one and analyze
colors. In the visualization, the project is divided into
tasks (comparable to the small pieces or squares of the
cube) which are organized into different facets (com-
parable to the faces of the cube) and only one of the sides
is visible at a time while the rest are hidden. The tool
utilizes rotation to allow access to the different sides and
within a side, the size and color of each square (as color
in Rubik cube pieces) is defined by the attributes of the
corresponding task. There are two main variables that
affect the T-Cube visualization: the criteria selected to
arrange tasks on one or another side of the cube
(different categories of tasks) and an attribute to be used
to determine the colour and size of the task.
3.2. The Treemap Hypercube Metaphor for
Software Management
There are a lot of tasks and measures about project tasks
that we would like to be able to arrange, group, and
move as with a Rubik Cube. Furthermore, a Rubik Cube
inherently hides information when you focus on one face
of the cube, which is an interesting feature when
analysing only a cluster of tasks. The overall layout of
how the metaphor is applied is presented in Figure 1.
Figure 1. Overview of T-Cube.
Software Project Visualization Using Task Oriented Metaphors 1019
The first step of the analysis is to define what
characteristic of the tasks will define the clusters or
facets of the hypercube. The criterion selected defines
the number of sides of the hypercube and therefore, the
list of available faces in the upper right part of the screen.
In the example in Figure 1, when “Project phase” is
selected, the system uses the previously defined phases
to put together in a face the tasks corresponding to
“Design” phase, in another side the ones related to
“Testing” phase and so on.
The tasks in the example can also be arranged on faces
using any of the following characteristics or criteria:
workgroup, type of task, module or time. As an example,
if the selection is workgroup, each face would contain all
the tasks performed by the same workgroup and the
hypercube would have as many faces as workgroups.
The lower right part of Figure 1 presents the radio
buttons with the different criteria for defining the sides of
the hypercube, while the upper part shows the side
selection and rotation method.
The information in the main view is presented using
the treemap space-filling technique. In a typical treemap
a square can represent either a leaf node or a group of
items and the user moves from a group level to its leaves
and vice versa by clicking on the square. In T-Cube,
squares represent only the leaf nodes of the facet
currently in the main view and the user is forced to make
radio button selections to modify the grouping criteria or
to change from one group to another. However, in a
typical treemap, clicking on the squares makes all these
operations possible. This design, which is consistent with
the metaphor, has the objective of assisting the user in
structuring the information into criteria and groups and
keeping this structure in mind.
Once the cube has been defined, the user chooses what
face is to be displayed. The user selects from the list one
of the facets, clicks on “Redraw cube,” and the cube
rotates, giving access to the side the user wants to
analyze, what we call “Rotate to main view”. The
visualization presents only one facet at a time but the
user can select the “Show All” option for an overall view,
which presents the information of all the tasks in a single
view. It is worth noting that also a time based cluster
creation and visualization is available. The flexibility for
task arrangement of the tool is extremely useful since it
enables to assess software from very different per-
spectives. The cube changes when a different criterion is
selected for task arrangement and the active view
changes when the cube rotates as a different facet is
It is worth noting that any of the indicators described
in Table 1 can be selected to define the size of the
presented tasks in the treemap. A very complete set of
data is available for analysis and therefore, the tool is
valuable for studying problems in the development
process of different nature and complexity. Planning
deviations tracking, analysis of defect detection and
correction patterns, overall assessment of change
management policies, evaluation of deliverable status, or
product quality estimation are some of the problems that
can be addressed by selecting different measures.
In the lower part of the screen there is a text to inform
the user about the currently visible data. The user can see
the project name, the measure selected from the list, and
the criteria and group currently in the view.
The values of the selected measure for each task in the
main view are presented using a table. This table is
accessible from the “Cube/Detail” tab as shown in
Figure 1. The table complements the visual information
and it also presents the sum of all the tasks for the
selected variable. This information is really valuable
since the Treemap offers individual data as a percentage
of the group. The use of colour and size lets the user
identify and focus on troublesome areas easily and the
treemap allows the user to apply visual filters from the
selected side.
The system has been implemented using Java and the
JTreeMap library for coding and MySQL for data storage.
The system presents all the data in a couple of seconds
and after any interaction with the interface the
visualization is refreshed in about two seconds. Figure 1
shows one of the views of an EIS (Executive Information
System) development and implementation project used
as an example to evaluate the representation with phases
and projected dates of a real project. The view in Figure
1 presents the cost deviation for the design tasks. Some
of the measures have been estimated for evaluation
purposes since not every measure by phase had been
previously recorded and, therefore, no data was available
for some of the measures.
4. The MetroMap Metaphor
The second approach, MetroMap uses a metaphor with a
metro map along with various interactive techniques to
represent information concerning the software develop-
ment process. Figure 2 shows how MetroMap applies
the metaphor. The metro stations, which stand for the
different tasks defined in the development process, are
organized into lines and the configuration of the lines is
defined by the selected criteria for grouping. Therefore,
the first step is to select the criterion or hierarchy that
will define the organization of the tasks and related
In the example, the system presents the lines using the
previously defined types of tasks and it aligns the tasks
related to design together in one line, those related
Copyright © 2010 SciRes. JSEA
Software Project Visualization Using Task Oriented Metaphors
Figure 2. Overview of MetroMap when “Type of task” is selected.
to testing in another, and so on. Another possibility in the
example is grouping the tasks in lines based on the
different workgroups that performed the work or based
on the different modules that integrate the whole
software application. It is worth noting that a time based
cluster creation and visualization is also available. The
flexibility of the presentation is advantageous because it
allows the user to assess the software development
process from multiple and distinct perspectives. Figure 1
presents the different criteria for arranging lines available
from the “Set line criteria” option.
The “Show/Hide lines” tab displays the different
“lines” or groups of tasks that result from applying the
criterion selected for line arrangement. The users can
arrange tasks into lines according to “Project phase” and
then, they can choose to hide or show each line by
selecting the desired project phase. The same process is
possible for “workgroups”, “type of task”, “time scales”
and “modules”.
The distance between one station and the next can
represent variables such as real effort, estimated effort,
real cost or budgeted cost, or it can be a fixed distance.
The users can choose an option from the list labeled
“Scale” in the “Scale/Filter by value” tab. For an overall
picture, visibility can be enhanced by presenting
equidistant stations.
Summarized information about the group of tasks in
each line of the graphic is presented at the end of the line.
The visualization shows the deviation from the planned
effort and the deviation from the budgeted cost in both
decimal and percentage form.
The system lets the user identify the tasks that have
been performed on time by a black dot and the rest of the
tasks will be represented by a white dot. The unplanned
tasks, having not been identified in the estimation phase,
can be easily detected because they appear highlighted in
a different shade. The tool offers filtering options,
including filter by value based on a wide range of
measures, thereby producing visualizations that display
only the set of tasks currently of interest. As an example,
the system can filter those tasks that have an error
correction effort, or number of deliverables rejected
greater than a given value. To further filter, the
“Show/Hide lines” option can be used to examine only
tasks related to programming, or only those performed
by the workgroup three, or only those belonging to a
certain module. This combination of multiple hierarchies
and indicators in the filter options creates many
possibilities for analysis and helps the user to identify
and focus on problematic areas.
For each individual task, the most relevant measures
are presented alongside numbers and their associated
metaphorical icons as shown in Figure 3 and Figure 4.
This visual and numerical information includes effort and
Copyright © 2010 SciRes. JSEA
Software Project Visualization Using Task Oriented Metaphors 1021
Figure 3. Steps required to analyze the values of different
measures for a task.
Figure 4. Steps required to analyze the values of different
measures for a group of tasks.
cost, dependencies among tasks, deliverable completion
and approval status, number of detected failures, failure
correction effort and scope management. The numerical
labels associated to each icon present the data for the
corresponding measure. As an example, the warning
icons indicate the phases of the project where errors have
been detected and the numerical label for each icon
shows the number of total errors for that phase.
The user can hide each icon individually by clicking
on the corresponding item to make the representation
more comprehensible. For example, in order to analyze
the status of the deliverables, the user can select only the
traffic light icons and hide the rest. Moreover, two
graphs present global data about error detection and
correction effort, which can be useful to detect the most
detrimental type of error. The user can visually study the
total number of changes required, and their status
(pending, implemented and rejected) to examine the
scope management. The area at the top of the screen
shows information about risk identification and
prioritization. The most relevant risks can be easily
identified by the “Give way” icon. That icon indicates
that the risk has been assigned a mitigation action.
The system has been implemented using the same
tools as in T-Cube, JavaFx for coding and MySQL for
data storage. The graphics in Figure 2 shows the
representation of an EIS (Executive Information System)
development and implementation project as an example
to evaluate the representation with real phases and
projected dates.
5. Comparison of the Two Visualizations
5.1. Differences and Similarities between the
Two Approaches
We will now analyze the similarities and differences
between the two visualization proposals. The similarities
can be summarized as follows:
They are two-dimensional visualizations.
Both show the same data and both are task-based
In addition, both use the same task hierarchy structures
and both present only one hierarchy at a time.
They offer the possibility of arranging clusters of
tasks and allow for visualization of only a certain
subset of tasks.
They include the same measures for analysis (except
for task dependencies which are only available in
MetroMap) and allow filtering the information
according to the value of a selected measure.
Both incorporate time-based analysis.
Both include graphical and textual information to
complement the graph.
Both comprise a high interaction level (multiple
selections, mouse functions, pop-up labels…) and
include similar features as different possibilities for
task arrangement for a general view and additional
information for a task when mouse hovers.
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Software Project Visualization Using Task Oriented Metaphors
Copyright © 2010 SciRes. JSEA
They concentrate on detecting problematic areas by
focusing on nonstandard or irregular values.
However, there are many differences between them:
MetroMap presents multiple indicators in a view
while T-Cube shows only one indicator at a time.
The capability of showing multiple measures at a
time is a central feature because it lets the user
analyze, detect patterns and draw a conclusion from
information of multiple variables. The amount of
information in a view can be bigger with MetroMap
and this richness can be useful for inference.
MetroMap can be recommended when the analysis
includes different measures simultaneously and T-
Cube when groups of tasks are to be examined using
one measure at a time.
MetroMap includes the “Show hide lines” option for
visualizing a subset of tasks, which allows
visualizing multiple groups of tasks simultaneously.
In T-Cube the group of tasks to be displayed is
defined by the main view and a unique side is
presented at a time. MetroMap can be recommended
when the analysis is focused in comparing different
groups of tasks or for pattern detection and T-Cube
when a group of tasks is the object of the study.
Furthermore, MetroMap can make the structure
clearer because all the groups can be visible
The visualizations include total values, but they do
not display the same added information. In Metro-
Map, the only measures calculated for a group of
tasks (line) are always total effort and cost deviation
per line, whereas T-Cube always calculates and
displays the added value per each face for the
currently selected measure.
In MetroMap the numerical value of a measure for
each individual task is presented. T-Cube includes
this numerical value in the data table and the value
as a percentage of the face is shown in the Treemap.
In MetroMap the user has access to the description
and some other individual information for all the
tasks in the visualization simultaneously, while in T-
Cube this information is only displayed for the
selected task.
They apply mapping to visual metaphors in a very
different way. Metro map uses multiple objects
(stations, traffic light, give way icons...) and different
attributes (color, position…) to represent the values
of a set of metrics. In T-Cube only the metaphor
with the cubes and faces is used and the only
attributes are color and size.
MetroMap uses a variety of icons and texts while T-
Cube uses a more classical visualization (treemaps).
The second one has a shorter learning curve since
with MetroMap the user needs to familiarize with
the icons code and semantics.
MetroMap uses of variety of colors and shapes
while T-Cube uses only squares and a scale of
shades from green to red. T-Cube can be easier to
MetroMap includes different filtering options
whereas treemaps make automatic filtering. A
treemap presents only those tasks that have a value
for the selected measure and eliminates the rest.
MetroMap has zoom and drag options and T-Cube
shows a unique view for the current selection. The
capability of controlling and directing the focus
offered by MetroMap is valuable when overview
and detail analysis is required.
MetroMap presents image and text in the same area
while T-Cube presents a graphical representation
and a supplementary table with text data. Presenting
the text spatially contiguous it is supposed to reduce
required cognitive load.
MetroMap is a new design with unique characteristics
while T-Cube includes treemaps, which have pre-
viously been used and proven.
Table 2. Best tool for each activity.
Activity Cube MetroMap
Analyze several measures simultaneously for a task
Analyze one measure for different groups of tasks
Comparing different group of tasks for pattern detection
Analyze applying different task structures easily
Analyze totals for different groups of tasks
Analyze numerical and percentage values.
Access to detailed information of multiple tasks
Access to information with a lower learning curve and with a easier to comprehend tool
Control focus, use drag and zoom facilities
Quickly change from global to a grouped view
Software Project Visualization Using Task Oriented Metaphors 1023
Figure 5. Representation of the total number of errors for the coding phase.
Table 2 indicates the most suitable tool for each type
of analysis. Figure 5 shows how the number of errors for
the coding phase is displayed in both visualizations and
allows visual comparison of both systems with the same
information being displayed.
5.2. Analysis Process and Iteration Using the
The features described in the previous sections are valuable
to the extent that they are useful to perform analysis tasks
An interesting approach to study the actual utility of
these characteristics is by comparing the minimum steps
or the steps of the “shortest path” to perform a specific
analysis task using different tools. As an example, this
section analyzes the use of MetroMap and Cube for the
following purposes:
1) Find a task with the highest, lowest or a certain
value for a measure.
2) Find the group of tasks with the highest, lowest
value for a measure.
3) Analyze the values of different measures for a task.
4) Analyze the values of different measures for a
group of tasks.
5) Select the tasks that are within a range of values for
a measure.
6) Analyze the value of a measure for a sub-group of
tasks being this sub-group defined by a range of
values for a different measure.
The interaction required to complete the analysis
process consists of several steps. Figures 3, 4 and 6-9
illustrate the paces to perform each of the above-
mentioned tasks. Steps indicated in italics are optional
and they can be useful depending on the amount of
information being analyzed.
Figure 6 and Figure 7 show that for inspecting the
values of a single measure, the steps are similar using
both tools. However, Figure 3 and Figure 4 show that
studying different measures for a task involves several
selections and redrawings when using Cube. Figure 8
illustrates the analysis of different groups of tasks, and
indicates that Cube requires several rotations and
repetitions of a sequence of steps. Figure 9 points out
that when using Cube, the user requires multiple selections,
redrawings and that he needs to recall information while
Figure 6. Steps required to find the task with the highest,
lowest or a certain value for a measure.
Figure 7. Steps required to select the tasks that are within a
range of values for a measure.
Copyright © 2010 SciRes. JSEA
Software Project Visualization Using Task Oriented Metaphors
Figure 8. Steps required to find the group of tasks with the
highest, lowest value for a measure.
Figure 9. Steps required to analyze the value of a measure
for a sub-group of tasks being this sub-group defined by a
range of values for a different measure.
with MetroMap he only needs to apply a filter and
evaluate one view.
From the study of the interactions, it can be concluded
that the path is in general more straightforward and that
the actions taken are more basic using MetroMap.
6. Assessment Overview and Results
Eighteen computer science students at the UPV/EHU
(University of the Basque Country) participated in the
assessment that was designed to examine the utility of
the tools. The students were reasonably familiar with the
notions of projects, tasks and measures and we selected
for the evaluation of the tools typical information
retrieval tasks of project management, which included
plan tracking and deviation, error tracking, change
management, deliverable management and some other
tasks that required a more complex analysis with
multiple filters and measures. These information retrieval
tasks were organized into 15 questions that the users had
to answer using the Moodle platform (a free course
management application) using personal computers in
both cases. After performing these tasks the users were
asked to answer some subjective assessment questions.
In this subjective assessment we presented nine questions,
eight of them to evaluate the utility of the tool for
particular tasks and a last one to evaluate the utility
The values in Figure 10 correspond to the percentage
of students that assessed the tool as a function of the mark
(1 indicates that the tool is not judged very useful for that
task and 5 is the highest positive assessment).
The figure shows that in general, users seem to have a
better perception of MetroMap than that of the T-Cube. In
summary, users evaluated T-Cube quite positively. The
results of the final assertion show that 37.5% of the users
evaluate the utility of T-Cube as 5, the maximum
possible, and 50 % of the rest assess it at 3. The average
values seem to indicate that the user liked the tool, since
26.56% of the answers are 5 and 26.69% of them are 4.
The questions where the users indicated slightest
agreement (questions 4 and 6) are those that include
comparing groups that were on different sides of the
cube and that required the user to consider information
not in the current view.
Concerning MetroMap, it can be stated that users
evaluated it very positively. The results of the final
question show that 50% of the users evaluate the utility
of MetroMap as 5, the maximum possible, and 37.5 % of
the rest assess it as 4. The average values seem to
indicate that the user liked the tool, since 50% of the
answers are 5 and 37.5% of them are 4. The question
where users indicated the least agreement (number 12)
requires the user to consider more than one variable in
the analysis, which is intrinsically a more complex task.
7. Conclusions
The two approaches presented in this paper offer a
general overview of the software project development
process, which is depicted as a set of related tasks that
can be analyzed from different perspectives. These
perspectives define the structure of the information of the
project and allow the user to focus on a group of tasks.
Both tools include a wide set of measures with
information about effort and cost deviation, deliverable
tracking, error types, effort distribution, and scope
management. The user can analyze diverse aspects of the
development process using only one tool and choosing
from a list of measures. In this sense, we consider these
tools more powerful and effective than other tools such
as the Gantt diagrams. The wide range of measures
presented and the flexibility in the organization of the
Copyright © 2010 SciRes. JSEA
Software Project Visualization Using Task Oriented Metaphors 1025
1. Get an overview of costs and efforts of the tasks.
2. Find a task with a cost deviation higher than a given value.
3. Find the phase of the project with the highest nbr. of requested modifications.
4. Find the task with the greatest deal of code correction effort and the nbr. of errors associated.
5. Identify the task with the highest number of modifications rejected.
6. Compare a set of modules and identify the one with the maximum number of pending deliverables.
7. Identify the type of task with the highest number of deliverables rejected.
8. Make a complex analysis requiring multiple filters and visual analysis.
9. In general, the tool has been helpful to answer the questions.
Figure 10. Assessment of the efficiency of the tools per task as a function of the mark (Percentage of students).
tasks offer a high number of possibilities for analysis.
The representations depict a large amount of related
data simultaneously and offer analysis possibilities which
are hardly feasible unless visual tools are used for project
management. As a result of the qualitative assessment, it
can be concluded that users have expressed a positive
judgment on the presented visualizations, which is also
important since the success of an information visuali-
zation tool depends partly on users' subjective opinions
of the tool's interface and utility. More formal experi-
ments can be designed to analyse other benefits of the
current proposal.
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