Telling stories about GNOME with Complicity.
ABSTRACT Traditionally, the target of software evolution research has been single software systems. However, in the recent years, researchers observed that software systems are often not developed in isolation, but within a larger context: the ecosystem level. Analyzing software evolution at the ecosystem level allows a better understanding of the evolution phenomenon, as the entire development context can be studied. Nonetheless, software ecosystem analysis is challenging because of the sheer amount of data to be processed and understood. We present Complicity, a web-based application that supports software ecosystem analysis by means of interactive visualizations. Complicity breaks down the data quantity by offering two abstraction levels: ecosystem and entity. To support a thorough exploration and analysis of ecosystem data, the tool provides a number of fixed viewpoints and the possibility of creating new viewpoints with given software metrics. We illustrate in a case study how Complicity can help to understand the GNOME ecosystem in a bottom-up approach, starting from a single project and contributor towards their impact on the ecosystem.
- SourceAvailable from: Franco-Bedoya Oscar
Conference Paper: QuESo: a Quality Model for Open Source Software Ecosystems[Show abstract] [Hide abstract]
ABSTRACT: Open source software has witnessed an exponential growth in the last two decades and it is playing an increas-ingly important role in many companies and organizations leading to the formation of open source software ecosystems. In this paper we present a quality model that will allow the evaluation of those ecosystems in terms of their relevant quality characteristics such as health or activeness. To design this quality model we started by analysing the quality measures found during the execution of a systematic literature review on open source software ecosystems and, then, we classified and reorganized the set of measures in order to build a solid quality model.ICSOFT 2014, Viena; 08/2014
Conference Paper: A historical dataset for the Gnome ecosystem[Show abstract] [Hide abstract]
ABSTRACT: We present a dataset of the open source software ecosystem Gnome from a social point of view. We have collected historical data about the contributors to all Gnome projects stored on git.gnome.org, taking into account the problem of identity matching, and associating different activity types to the contributors. This type of information is very useful to complement the traditional, source-code related information one can obtain by mining and analyzing the actual source code. The dataset can be obtained at https://bitbucket.org/mgoeminne/sgl-flossmetric-dbmerge.Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on; 01/2013
Telling Stories about GNOME with Complicity
Sylvie Neu, Michele Lanza, Lile Hattori, Marco D’Ambros
REVEAL @ Faculty of Informatics — University of Lugano, Switzerland
Abstract—Traditionally, the target of software evolution re-
search has been single software systems. However, in the recent
years, researchers observed that software systems are often not
developed in isolation, but within a larger context: the ecosystem
level. Analyzing software evolution at the ecosystem level allows
a better understanding of the evolution phenomenon, as the
entire development context can be studied. Nonetheless, software
ecosystem analysis is challenging because of the sheer amount of
data to be processed and understood.
We present Complicity, a web-based application that supports
software ecosystem analysis by means of interactive visualizations.
Complicity breaks down the data quantity by offering two
abstraction levels: ecosystem and entity. To support a thorough
exploration and analysis of ecosystem data, the tool provides
a number of fixed viewpoints and the possibility of creating
new viewpoints with given software metrics. We illustrate in a
case study how Complicity can help to understand the GNOME
ecosystem in a bottom-up approach, starting from a single project
and contributor towards their impact on the ecosystem.
Software systems change, and during this process they grow
in size and complexity, and incrementally move away from their
initial design. This phenomenon, known as software evolution
, makes it difficult to maintain a software system, which
claims a share estimated up to 90% of total software costs ,
, of which 60% is spent in understanding the system .
The high cost of software maintenance results from many
factors: Documentation is often not updated, or non existent
; because of the continuous turnover of developers, changes
to a software system are often performed by developers with
a limited knowledge of the system. An established technique
to deal with these problems and ease software maintenance
is reverse engineering. Chikofsky and Cross defined reverse
engineering as “the process of analyzing a subject system to (1)
identify the systems components and their interrelationships,
and to (2) create representations of the system in another form
or at a higher level of abstractions” .
Most reverse engineering research is mainly concerned with
satisfying these goals using different abstraction levels (e.g.,
code level , and design level , ). The problem with
following the above definition is that it takes into account a
single software system focusing on either the project or its
contributors. However, software systems are rarely developed
in isolation, but within a same environment: an abstraction
level called ecosystem. A software ecosystem is defined as
“a collection of software projects, which are developed and
co-evolve together in the same environment” .
To analyze software ecosystems at any abstraction level,
techniques are required to cope with the huge amount of data
available about the evolution process.
Two analysis techniques have been effectively used to
convey the results to the end user: metrics , , ,
and visualization , , . The advantage of software
visualization over pure metrics is that it uses the brain’s
ability of remembering images  and extracting patterns
and anomalies from the data that are unlikely when data or
numbers are presented in tables or text . Diehl defines
software visualization as “the visualization of artifacts related
to software and its development process [...] including for
example program code, requirements and design documentation,
changes to source code, and bug reports” .
We present Complicity, a web-based interactive application
that visualizes –at different abstraction levels– the data extracted
from the Git web interface of super-repositories. Complicity
makes use of metrics and visualization to analyze software
ecosystems in a bottom-up approach. The user can start
analyzing a project or a contributor, and go to the ecosystem
level to give a higher-level context for the individual analysis.
Using the GNOME super-repository as a case study, we
demonstrate the use of Complicity to better understand how
an individual project affects the events at ecosystem level, and
follow a contributor’s involvement in different projects.
The first contribution of this paper is Complicity, a web-based
and interactive application to visualize software ecosystems.
Complicity uses information available in the Git web interface
to visualize super-repositories at different abstraction levels.
The second contribution is a bottom-up approach to study
various aspects of a software ecosystem exemplified on
a case study of the GNOME ecosystem. We demonstrate
what information is available with Complicity, and how the
visualizations can support software analysts to understand the
evolution of projects and contributors within an ecosystem.
II. RELATED WORK
Software evolution visualization research has mostly been
targeting single software systems. Seesoft  shows the change
history of files by mapping one line of code to a pixel line,
in which the color of the line represents the recency of its
change. Fisheye (http://www.atlassian.com/software/fisheye/),
a commercial web-based tool, allows one to visualize the
evolution of a single system by looking at charts and matrices
of commit activity. The Evolution Matrix  displays the
evolution of a system at class and system level, with rows
mapping different classes and columns mapping versions of the
system. Wettel et al.  exploit the city metaphor to visualize
the current state of an object-oriented system and its evolution
over different versions.
Fig. 1.Main View of Complicity visualizing the GNOME projects at ecosystem level, and the details of the Gimp project
Recent work has analyzed social aspects of a project’s evo-
lution (e.g., activity, communication structure, and knowledge
flow), which influence the changing process and is crucial to
understand the evolution of a software system.
The Ownership Map  identifies the owner of every single
file within a software system. A file is represented with a line, a
disc defines a file change, and the color of the line and the disc
defines the owner and the committer, respectively. In Maispion
, the authors analyzed the activity in the mailing list and
version control systems (VCS) of a single project to reveal
communication behavior within it. They answered questions
such as “Is there a main driver?” and “When are the developers
most active?”. Oezbek et al.  checked whether the onion
communication model is applicable to open-source systems
using mailing lists as data source. They found that the core
developers are highly interactive and tightly interconnected.
These approaches use visualization techniques to support the
comprehension of a single software system. Complicity uses
some of these visualization techniques (e.g., two dimensional
boxes to encode different metrics and visualizes contributors’
activity) to support the understanding of software ecosystems.
Software ecosystems analysis is an under-researched area.
FLOSSMole  aims at mining free, libre, and open-source
software (FLOSS) super-repositories (e.g., sourceforge) and
making general information on these projects publicly available.
The data from the VCS or any other data source is not
mined. L´ opez-Fern´ andez et al. apply social network analysis
to FLOSS projects, such as KDE, and Apache . They
found out that committers of the GNOME and KDE are
more tightly connected than the ones of the Apache, because
of the GNOME’s and KDE’s projects technical proximity.
Ohloh (http://www.ohloh.net/) is an online directory of FLOSS
projects and its developers. It retrieves data from different VCS
and uses different metrics (e.g., number of commits, number of
lines of code) to provide some visualizations that show various
aspects of the projects’ evolution. Lungu focuses his work on
reverse engineering software ecosystems . He created SPO,
an interactive tool that can be used to analyze the evolution of
software ecosystems. SPO differentiates between two aspects,
project and developer, for which the ecosystem plays one of
two different roles: focus, to better understand the ecosystem; or
context, to understand a single entity of the ecosystem. Seichter
et al. introduced an approach of knowledge management using
a social network of software artifacts in which the knowledge
is attached to an artifact rather than a contributor . The
advantage is that if a developer leaves a software project, the
knowledge remains within the project. Goeminne and Mens
provide a framework to mine VCS, mailing lists and bug
tracking databases, to analyze and visualize mainly the mailing,
and commit activity of FLOSS ecosystems . They define
an ecosystem as “the source code together with the user and
developer communities surrounding the software”.
In our work we combine software visualization with software
metrics, by focusing not only on a single entity or the ecosystem
level, but enabling the user to switch between the ecosystem
and entity level, and between project and contributor views.
BirthFirst Release Maintenance Major Release
of lines added (green) vs. number of lines removed (red), d. difference between number of lines added and removed, e. number of files changed))
Project Detail Page (here: Activity per day over the entire lifetime of the Nautilus project (a. number of commits, b. number of contributors, c. number
Complicity (http://complicity.inf.usi.ch) is a web-based vi-
sualization tool that allows the user to interactively explore,
analyze and understand the evolution of super-repositories at
two different abstraction levels: entity (single project or con-
tributor), and ecosystem (a group of projects or contributors).
User Interface. We kept the user interface simple to avoid
distracting the user from the analysis tasks, whereas the shapes
in the visualizations are colored to attract their attention.
The main page (see Figure 1) is divided into three main
parts: (1) the control panel on the left, (2) the main graphs
of the ecosystem in the center of the page, (3) a quick view
panel of a project’s or contributor’s details on the right, which
appears by clicking on a shape in the graph.
Control Panel. It gives the user the possibility to (a) analyze
different super-repositories, (b) choose between two entity types
(project and committer) for the visualization at ecosystem level,
(c) navigate through predefined viewpoints or change their
settings, and (d) search for projects or contributors of interest
by project type, project name or contributor name.
Graphical View. It visualizes the available data for the
selected super-repository and the selected entity type as scatter
plots. Every box represents either a single project or contributor,
depending on the entity type selected, and reflects up to five
different metrics: position on x and y axis, width, height and
color. By clicking on a shape a detail panel on the right appears.
Entity Details Panel. It provides general information about
the selected entity, which goes from name and date of the
first and last commit, up to number of commits, number of
contributors or projects, etc. From this panel the user has the
possibility to get more details and further analyze the selected
project or contributor in a new window.
The detail page of a project or contributor (see Figure 2)
has a similar layout as the main page, with the exception that
the control panel on the left is replaced with an overview of
the selected project’s or contributor’s details.
In the center of the page, the user can choose between
two views: (a) activity diagrams and (b) projects/contributors
involvement distribution. The activity diagrams view allows the
user to compare the activity of the selected entity in terms of
number of commits, number of contributors/projects, number
of lines added versus number of lines removed, and number of
files changed. In the involvement distribution view, the analyst
gets an idea about how many contributors have been involved,
when, and for how long, or –in case of a selected contributor–
how many projects he worked on and what his speciality is.
Architecture. Figure 3 shows the architecture and the
backend of Complicity. To fetch the relevant data from the Git
web interface into our database, we developed a number of
Java programs. The crawler stores a copy of the web pages
from the web interface of the Git super-repositories locally
before the parser extracts the data, and stores it in a database.
The metrics calculator takes the extracted data, prepares and
stores it in such a way that Complicity can easily retrieve the
data necessary for the visualization without having to calculate
the metrics on the fly.
Fig. 3. Architecture of Complicity and the backend
PHP scripts retrieve the data from the database and convert
it into JSON objects, the format required for the visualization.
The graphical visualization of the data is done by an external
toolkit, called Protovis1. The user interface of Complicity is
for user interface components and interactions.
Data Model. Figure 4 illustrates Complicity’s data model.
Fig. 4. Data model behind the Complicity visualizations
Each project is attached to a super-repository and to a project
type. The project table contains the general information, e.g.,
name, and description, but also the pre-calculated metrics,
such as number of commits (numCommits), number of files
(numFiles). The contributor table contains general information
as well as pre-calculated metrics. A project can have many
contributors and a developer can work on multiple projects.
The tables Periodic and Fileiodic contain information about
the changes done to the project. The difference is that Periodic
contains the general changes based on the metrics (e.g., number
of commits, number of files), whereas Fileiodic contains the
data necessary to visualize the specialty of a contributor based
on the number of files she has changed with specific extension.
IV. STORIES OF THE GNOME PROJECT
GNOME is a desktop environment for GNU/Linux/Unix
composed of many free and open-source software systems. It
was created in 1997 by two students, and since then it has grown
in popularity. We show how Complicity can support software
analysis at ecosystem level. Using a bottom-up approach,
we first analyze a single project regarding its activity and
community support before moving to the ecosystem level and
trying to reveal some patterns in the contributors’ affection to
either translation or development work. In a second example, we
analyze a contributor’s activity and expertise before examining
at ecosystem level how he affected the GNOME project.
This approach is of interest to analysts who want to
understand the evolution process of a project and its impact on
GNOME, or to analyze the role of contributors on individual
projects and at ecosystem level. Several maintenance needs may
lead analysts to use visual exploration of software ecosystems,
e.g., to identify critical parts of the system to prioritize
maintenance; identify the main contributors –who retain the
knowledge– of a project; and to identify dependencies among
projects to be aware of ripple effect of a project’s release
on depending projects. The approach is also of interest to
developers who want to get involved in the projects, and need to
understand their dynamics before becoming active contributors.
We focus on Nautilus, GNOME’s default file manager.
A. The Nautilus project and its impact on the ecosystem
Project Activity Diagrams are a good starting point to
get a first idea of Nautilus’ evolution beyond the basic
information available in the project’s details. They illustrate
the project’s daily activity comparing six different metrics:
number of commits, number of contributors, number of lines
added and removed, difference between number of lines added
and removed, and number of files changed. We use an area
chart for all activity metrics, except the line-difference metric,
which is drawn as a line chart. All of them have the time
on the x-axis and one of the six metrics on the y-axis.
The number of lines added (in green) and removed (in red)
is drawn in a same diagram for better comparison. These
diagrams allow us to identify different phases of a project.
In Figure 2 we observe that Nautilus was created in 1997
(birth), when also GNOME was created. Between 2000/2001
its popularity increased in terms of contributors, in number
of commits per day and in number of lines added/removed
(first major release). Afterwards the number of commits and
contributors decreased and stabilized. Also the number of lines
added/removed equalized (maintenance). In 2010, the number
of commits increased again until Feb. 2011 (new major release).
Beside the different phases, we observe that there is
continuous development taking place almost every day, which
is an indicator for an active development team. The next step
is to get familiar with its contributors: Who worked, when, and
for how long on the project? This information is presented by
the contributors involvement distribution view in Figure 5.
Contributors Involvement. This view gives information
about the number of commits, but it does not allow a
comparison of different metrics at the same time. Instead, it
provides a more detailed view on the people who contributed to
the project over time, so that the drivers of the different phases
can be identified. This information is crucial to understand the
flow of knowledge (Is there a center of power? Are there smooth
Number of Commits
Fig. 5.Contributors Involvement View of the Nautilus project
takeovers?), which is a good basis for a project to evolve well
and have a long lifetime. For this visualization, we use a stacked
area chart using colors to identify different contributors. The
size, the density and the position of the contributor’s name are
based on the maximum number of commits a person has done
at once. Clicking on a single contributor hides all the others,
so that the contribution timeline of a single person can be
better analyzed. This view shows the evolution on a monthly
basis. In Nautilus’ first major release phase (see Figure 5),
Ramiro Estrugo (gray), as well as Andy Heitzfeld (yellow),
Darin Adler (green), and John Sullivan (yellow) are the main
initiators of the project. Darin Adler remains the main driver at
the beginning of the maintenance phase until the beginning of
2002, when Alex Larsson (blue) takes over. He remained the
driver for almost the whole maintenance period. Only by 2008
a new actor enters the scene, Cosimo Cecchi (light pink), who
takes over the project. The above diagrams contain information
about a single project, how it and its community evolve over
time. As Nautilus is only one software system of the GNOME
project, it is important to analyze how it impacts the ecosystem.
Affectional Bond - Ecosystem Diagram. This view shows
the contributor distribution at ecosystem level, with number of
commits on the x-axis, and number of project on the y-axis and
as color metric. The width and height of each shape is defined
by a contributor’s lifetime in days. This view can be used to
visualize all contributors of the entire ecosystem or filters can
be applied to show only the contributors of a specific project.
This visualization presents a piece of information that cannot
be revealed at project level from any of the two views described
above: the contributors’ general affectional bond to either
development or translation work. In this graph the contributors
are split into these two groups under the assumption that
people who contributed a lot but only to a relatively small
number of projects are likely to be developers. Conversely,
people who committed less often but to more projects are likely
to be translators. This results into the following distribution:
people located under a logarithmic-like curve are defined as
developers and the ones placed above an exponential-like curve
are considered to be translators.
Number of Projects
Number of Commits
committer, where the x position maps the number of commits, the y position
and the color the number of projects, and the size the lifetime in number of
days (T: translators, D: developers, O: outlier, N: no man’s land)
Affectional Bond view at ecosystem level. Each square represents a
Figure 6 illustrates the contributor distribution of the Nautilus
project. It reveals that many people have been working
on the project over time and that the distribution between
developers (marked as D) and translators (marked as T) is
roughly equalized. The contributors located within the high-
density bottom left part (marked as N) might not be clearly
differentiable. It seems that developers need a longer lifetime
(larger boxes) in order to be clearly differentiable from the
translators, who have a more varied lifetime (collection of large
and small boxes). The outlier on the top right (marked as O)
did a huge amount of changes (positioned to the right) and
contributed to many projects (positioned to the top) over a
long lifetime (relatively large box). O is Kjartan Maraas, both
a developer and a translator within the GNOME project.
B. Impact of the contributor Kjartan Maraas on the ecosystem
As a second example we have chosen Kjartan Maraas as
the contributor for the bottom-up analysis. Since 1998 he has
been working on 499 different projects, having done more than
15’000 changes by February 2011. During 4,689 days (ca. 13
years) he had an average rate of three commits per day.
We know when a contributor did his first and last commit
and we can calculate his average commit rate. However it is
not possible to know whether he was active all the time or there
have been some gaps of inactivity during his lifetime. This
information can be extracted from developer activity diagrams.
# Lines Added/Removed
Diff Changed Lines
Number of Commits
Number of Projects
Fig. 7. Activity Diagrams of Kjartan Maraas
Developer Activity Diagrams give an overview of the
contributor’s daily activity within an ecosystem. They are
constructed the same way as the activity diagrams at project
level with the only difference that the metric number of
contributors is replaced by number of projects. In Figure 7
we observe that Kjartan Maraas has been continuously active
with only one exception of few months. Particularly interesting
in his past activities is a short period of time (one day), in
2006, during which he added or removed up to 200’000 lines,
performed about 100 commits and changed up to 350 files.
A piece of information missing in this view is on which
projects Kjartan Maraas did these changes. To this aim, we
have introduced the project involvement view, presented next.
Project Involvement. This view provides the projects to
which a person committed monthly. As for the contributors
involvement view, we use a stacked area chart with number of
commits on the y-axis and time on the x-axis. Figure 8 shows
that Kjartan Maraas has been active in many different projects.
Number of Commits
Fig. 8.Projects Involvement View of the Kjartan Maraas
The number of projects he worked on increased after the first
few years, which can be revealed by the fact that the colors
representing the projects in this view become less differentiable
towards the right part of the visualization. Also the color
intensity and size of the labels become more similar, which
means that he committed with similar rate to these projects. At
this point of our analysis it might be interesting to understand
how it is possible for a single person to do so many commits
and changes while working on so many projects. To answer
this question we devised the expertise view.
Expertise View. This visualization yields information about
a contributor’s expertise based on file extensions. It is drawn as
a stacked area chart, counting the number of files that has been
changed within a month aggregated by their file extension.
Figure 9 shows the expertise view applied to Kjartan Maraas.
He is a translator and C developer, as he changed a large number
of .po files (light green) as well as many C files (blue).
Number of Files
Fig. 9.Expertise View of Kjartan Maraas
The combination of the three previous visualizations explains
how Kjartan Maraas does so many changes in so many projects:
He regularly worked on the GNOME ecosystem over a very
long period. Exploring the projects involvement view, we see
that he commits to most of the projects more than once but
with a low monthly commit rate.
Knowing that besides being a translator, which makes it
possible to work on many projects simultaneously, he is also a
developer, we can conclude that he is likely to be a maintainer
trying to fix bugs on different projects. Indeed, by checking his
profile on LinkedIn3we found out that he has been member
of the Bugquads at GNOME for almost ten years beside being
a member of the release team and of the GNOME foundation.
The views at contributor level provide details about a
single person’s activity level and expertise. However, to better
understand how a contributor affects the GNOME ecosystem,
we need visualizations at ecosystem level.
Lifetime (in days)
Number of Commits
contributors of the GNOME project within the Activity Fire View
Kjartan Maraas (marked by the arrow) compared to all other
The Activity Fire - Ecosystem Graph is constructed with
number of commits on the y-axis, lifetime on the x-axis, and
number of projects as width, height and color of the boxes. It
illustrates the distribution of the contributors according to their
activity over their lifetime in the GNOME project.
Kjartan Maraas is an outlier compared to the contributors
of Nautilus (see Figure 6) and within the GNOME ecosystem
(marked by an arrow in Figure 10). He performed by far most
commits (positioned to the top) and worked on more projects
than anybody else (large box) in the ecosystem. This might be
related to his long lifetime (positioned to the right) at GNOME
but also to the fact that he is both a translator and a developer.
Projects do not always have long lifetimes. As consequence,
Kjartan Maraas worked on many projects but probably not on
all at the same time. The following view provides clarification
by illustrating the projects’ lifetime.
Projects’ Lifetime View - Ecosystem Graph. This view
shows the projects’ life duration by distributing the projects
according to their first commit (x-axis), i.e., the creation date,
and their last commit (y-axis), which might correspond to
the death date if a project has not been changed for a longer
period. We define a project as dead if it has not been changed
for over a year. Since projects cannot die before they are
created, the boxes are distributed over a triangular surface with
the first created projects on the left most vertical line, the
still active projects on the top most horizontal line, and the
short-lived (less than one year) projects on the diagonal line,
closing the triangle. The width and height of each box are
defined by the total number of commits and the total number
of contributors, respectively. The color represents the different
project categories and might reveal some trends within the
ecosystem or preferences of a single contributor.
contributed to with A: active projects, P: prototypes, and D: dead projects
(Color scheme: projects of category archived (red), others (green), desktop
(orange), bindings (light blue), development tools (pink), and platform (purple))
Projects’ Lifetime View showing only the projects Kjartan Maraas
Figure 11 shows that many of the projects Kjartan has
contributed to have died (D). Most of them are colored red
(archived), green (others), or orange (desktop). The projects
within this area placed at the diagonal line (P) are likely to
be prototypes or projects that have been integrated into other
projects. They have a very short lifetime and die almost as
soon as they are created. Only the projects on top (A) are
still alive, and actively under change. Maraas never worked
on all projects at the same time as some of them died before
others had been created. Summarizing, we can say that Kjartan
Maraas is a very active person with a long lifetime as translator
and developer with an affinity for bug fixing. He is the most
important person according to the number of commits and the
number of projects he has been involved over his lifetime. He is
an outstanding person not only compared to the contributors of
the Nautilus project but within the entire GNOME ecosystem.
For the Nautilus project, we can conclude that it is under
continuous change with active contributors, who are equally
distributed number of translators versus developers. In addition,
it has at least one main driver in each phase with smooth
take-overs, which are essential for a good knowledge flow.
Importance of different abstraction levels. Our case study
shows the importance of different abstraction levels –ecosystem
and entity level– and the ability to interactively move from one
level to another, as they complete each other. These abstraction
levels allow analysts to identify dependencies between projects,
contributors, and between both. Also, by integrating both of
them in one analysis task, one can illustrate the impact of a
single project or contributor on the entire ecosystem.
Versatility of Complicity. We illustrated how one can make
use of a bottom-up approach for analyzing the evolution of
a software ecosystem. However, Complicity supports both
bottom-up and top-down approaches to analyze the evolution
of software systems individually and at ecosystem level. Also,
we presented multiple views that allowed us to explore the
history of a single project and contributor and their impact on
software ecosystem, e.g., GNOME. All of the views considered
in the case study are implemented in Complicity. Given the
space limitations, we did not present all of Complicity’s views.
Drawback of basic metrics. We showed that abundant
information can be extracted by applying only basic metrics,
e.g., number of commits, number of projects, etc. but it has
to be considered that these metrics might be misleading and
should be considered carefully. For instance, the fact one person
commits more than another does not necessarily mean that the
former does more work than the latter. Instead, it depends on
the committer’s attitude on performing large or small commits.
Git committers versus authors. Compared to other version
control systems, Git differentiates between author and commit-
ter4: The author is the person who originally wrote the work,
and the committer is the person who last applied the work. We
ignore the committer. This might lead to wrong conclusions if
the committers differ from the authors most of the time.
We presented Complicity, a web-based visualization tool
that allows interactive exploration and analysis of software
ecosystems evolution. The data used on the analysis has been
collected by reverse engineering super-repositories. Complicity
is applicable to analyze software ecosystems in a bottom-up
approach. In addition, we analyzed the history of a project and
a contributor, and showed their impacts on the ecosystem using
predefined views at two different abstraction levels. However,
analysts can use Complicity to visually explore the provided
data on their own using other viewpoints and approaches.
Future work. First, we intend to explore other ecosystems
with complicity and compare the results. Second, we plan to
improve our technique to automatically eliminate duplicate
contributors. This is a common problem, known as aliasing
, as contributors use different email addresses, and different
names to commit to a repository. Different solutions have been
proposed to tackle this problem, such as using the Leven-
shtein distance  or fuzzy string similarity, domain name
matching, clustering, and heuristics . To eliminate duplicate
contributors, we have focused on aggregating people based
on their email addresses, names and using the Levenshtein
distance. Identifying people based on their commit rate and the
projects they worked on would help to identify and aggregate
contributors with different names and email addresses. Lastly,
we aim at incorporating other metrics and data from other types
of archives (e.g., email archives, bug trackers and forums).
Acknowledgments. Hattori is supported by the Swiss Sci-
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