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Integrating Research Testbeds into Social Coding Platforms


Abstract and Figures

The adoption of social coding platforms among software developers is particularly high because it increases collaboration and productivity as well as code re-use. On the other hand research testbeds do not generally have such high adoption rate. The learning curve of adopting new technology represents an initial drop in performance before it increases. However this effect is unexpected by many users and there is a risk that they will abandon new technology before their performance increases. The main contribution of this paper is to introduce methodology to take advantage of the high adoption rate of social coding platforms to improve the adoption of research testbeds. The proposed approach is that a social coding platform serves as a common gateway to various testbeds since it is something many developers are comfortable with and thus the initial effort needed to start using the testbed is decreased and the probability that experimenters continue to use a testbed is higher.
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Integrating research testbeds into social coding
z Vuˇ
cnik, Carolina Fortuna, Tomaˇ
Solc and Mihael Mohorˇ
zef Stefan Institute, Ljubljana, Slovenia
{matevz.vucnik, carolina.fortuna, tomaz.solc, miha.mohorcic}
Abstract—The adoption of social coding platforms among
software developers is particularly high because it increases
collaboration and productivity as well as code re-use. On the other
hand research testbeds do not generally have such high adoption
rate. The learning curve of adopting new technology represents
an initial drop in performance before it increases. However this
effect is unexpected by many users and there is a risk that they
will abandon new technology before their performance increases.
The main contribution of this paper is to introduce methodology
to take advantage of the high adoption rate of social coding
platforms to improve the adoption of research testbeds. The
proposed approach is that a social coding platform serves as a
common gateway to various testbeds since it is something many
developers are comfortable with and thus the initial effort needed
to start using the testbed is decreased and the probability that
experimenters continue to use a testbed is higher.
Working with testbeds [1] we observed a trend that testbeds
sometimes are not utilized to their full potential since the
initial effort required to bring to life even the simplest example
is often very high. Testbed providers many times implement
their own special registration system responsible for user reg-
istration, creating user accounts and managing user rights [2].
This system is the first thing an experimenter will encounter.
Extensive studies have been done in the field of technology
adoption and have proven that difficulty of use can discourage
adoption of an otherwise useful system [3].
In [4] the authors go even further and present the learning
curve of relative performance over time Figure 1. Initially
the learning curve goes below 0 on the relative performance
scale which means that at the start of using a new system
user performance is worse than before although in time the
performance will increase. This effect is unexpected by many
users and there is a risk that they will abandon new technology
before their performance increases and they realize the benefit
of it.
Fig. 1. Relative performance over time with learning effect as illustrated in
[4, Fig. 1].
Based on this knowledge we can safely assume that the
initial interaction with the system needs to be as pleasant as
possible this way keeping the time spent “below 0” as short
as possible thus increasing the probability of adoption. One
way to achieve this goal is to integrate the testbed into an
already successfully adopted platform. Social coding platforms
adoption is considerably high among software developers
[5] therefore the research testbed integrated in one of those
platforms would automatically benefit from its adoption rate.
The proposed methodology of integrating a wireless ex-
perimentation testbed into a social coding platform is suitable
for any testbed provider that offers some kind of hardware to
experimenters. The idea is that a social coding platform serves
as a common gateway for various testbeds since it is something
many developers are familiar with and thus the initial effort
needed to start using the research testbed is decreased and
the probability that experimenter continue using the testbed
are higher. Furthermore many potential experimenters already
have user accounts created on at least one social coding
platform therefore there is no need to create a new one
and the administrator can simply grant an existing user the
right to access the testbed repository and she/he can start
At the moment of writing three the most popular social
coding platforms are GitHub1, Bitbucket2and Gitlab3[6].
Our reference implementation of the proposed methodology is
based on social coding platform GitHub although near identical
integration could be achieved using any of the two remaining
The rest of the paper is structured as follows. Section II
positions our work against the others. Section III details the
reference architecture and implementation of the integration.
Finally, Section IV identifies the future work and concludes
the paper.
Open access testbeds in Europe are brought together un-
der various European projects more notably FED4FIRE4and
WiSHFUL5. FED4FIRE defines a common access mechanism
for different testbeds while also taking care of unifying the
experiment control based on OMF framework [7]. Wishful
978-1-5386-1478-5/18/$31.00 c
2018 IEEE
Hardware infrastructure
Local network
single board
USB hostSD card
infrastructure node
Testbed device
Repository (GitHub)
interface (SSH)
Experiment controller and build
automation system
Node registry
Orchestration & configuration
Container (Docker)
Deployment (Ansible)
experimentation node
application module
Management server
Webhook service (GitHub
UWB testbed device
SRD testbed device
UHF sensing testbed device
IEEE 802.15.4clean slate
LPWA testbed device
Social coding
Fig. 2. Architecture of the reference integration.
project develops unified programming interfaces (UPIs) as a
radio and network control abstraction [8] which are particularly
useful as a basis for experiment automation. Some testbeds fol-
low the FED4FIRE authentication scheme and the others have
developed their own user registration and user management
US research testbeds are federated under the Global En-
vironment for Networking Innovation GENI6[9]. GENI ap-
proach to testbed experimentation is more traditional trying to
replicate the network simulator experience by implementing
a fully functional GUI [10]. Current trends in 5G go in
the direction of software defined radios and networks [11],
[12] however testbed experimentation have yet to follow the
software defined approach. Popular 5G related SDR social
coding projects among others include Microsoft OTP4LTE-U7
and SRS srsLTE8. OTP4LTE-U project develops experimental
LTE/LTE-U PHY and MAC layers while srsLTE offerers
a more complete LTE software suite up to IP layer. Both
projects support commercial off-the-shelf SDR hardware such
as bladeRF9and USRP10.
After examination of different testbed access systems we
observed that it is still a common practice for testbeds to
use a custom user registration and management system. For
each testbed an experimenter needs go through a different
registration process to create a new user account before she/he
can start experimenting. By integrating testbeds into a social
coding platform and automating the build process it is possible
to completely avoid multiple user accounts furthermore an
experimenter can immediately start by running an example
experiment with minimal effort. After the example experiment
is done an experimenter can continue by adjusting some trivial
parameters and observing the results to get the filling of using
the testbed therefore not spending so much time reading the
documentation and possibly losing the interest to go deeper.
Beyond the user registration and management we observed
that testbeds generally already provide good abstraction layers
or frameworks required to support automation. Furthermore
many times the code is hosted on one of the social coding
platforms i.e. GitHub while the integration with the testbed
itself is typically missing. From what we learned about differ-
ent experimental testbeds we can conclude that social coding
platform integration is achievable by any of the testbeds we
In order to make low level wireless firmware experiments
based on social coding platforms functionality possible, we
had to introduce multiple layers of abstraction. The abstraction
is in form of software modules and libraries as well as
hardware. After researching the related work we can conclude
that testbeds generally already implement most of the needed
abstractions for successful social coding platform integration.
Before the integration we need to ensure that the experiment
build is completely automated and that everything needed for
the actual build is hosted inside the repository located on social
coding platform.
We identified three features a social coding platform needs
to support for the successful integration with wireless experi-
mentation testbed:
1) Creating releases or tags from repository
2) Triggering a webhook
3) Uploading and appending files to the created releases
Creating a release or tag takes a current snapshot of the
repository and attaches a version number and the description
this way we can reproduce this experiment results at any time.
Social coding platform needs to support webhook triggering
mechanism this way the testbed management server can initiate
a build wen a new release is created. There should be an option
to append a file to the created release when the experiment
is done thus the collected results are made available to the
experimenter for download and analysis. Additionally Wiki
support is also desired to host the testbed documentation and
testbed topology along with the code this way the testbed
experimenter has all the needed information in one place.
During the extension of our testbed LOG-a-TEC [13] we
upgraded the architecture depicted on Figure 2, by defin-
ing a completely new experiment deployment and execu-
tion system based on Ansible11 orchestration and container
technology Docker12. The reference implementation combines
several features that comprise a complete testbed management,
monitoring and experimentation solution. Internally, it is a
complex setup composed of self-sufficient systems packaged
in Docker containers. Each container includes all dependencies
with the purpose of being easily redistributable. Externally, the
management server exposes a user-facing web interface and an
A. Hardware
The hardware infrastructure is composed of several types
of testbed devices, which are comprised of two VESNA
(VErsatile platform for Sensor Network Applications) nodes,
as depicted on Figure 3. Notably, the SNA-LGTC experimen-
tation nodes host several different radio interfaces and thereby
support experiments in various frequency bands. In particular,
we classify them to those enabling experimentation with: (i)
LPWA communications, (ii) short range UWB communication,
(iii) advanced spectrum sensing in sub-1 GHz bands, (iv)
efficient lightweight protocols in 2.4 GHz and sub-1 GHz
bands that are not based on IEEE 802.15.4 (clean slate) and
(v) spectrum sensing and signal/interference generation by
reconfigurable transceivers.
Fig. 3. SNA-LGTC assembled.
The infrastructure node is a custom-designed single board
computer based on the BeagleCore13 module running the
GNU/Linux operating system. It features wired Ethernet and
Wi-Fi for infrastructure connectivity, SD card slot for storage
expansion, VESNA and USB interfaces for extensibility and
development/debug process. The interfaces are depicted on
Figure 4. The experimentation node is custom-designed board
based ARM Cortex-M3 microcontroller running low level bare
metal firmware. It has a modular design and is able to host
different application modules with dedicated experimentation
transceivers. Both nodes are interconnected via application and
development interfaces, providing the exchange of application
data as well as remote low-level application debugging. The
development interface is used for programming and debugging
of the experimentation node, while the application interface is
used for communication while the experimentation node is in
operation. The development interface is based on JTAG and for
the application interface protocols such as LCSP (Light-weight
Client Server Protocol; an HTTP-inspired protocol) running
on top of serial interface are used. Optionally more standard
protocols can be used for the application interface such as
CoAP protocol on-top of IPv6.
Fig. 4. SNA-LGTC connectors.
The main reason for the separation of the hardware infras-
tructure in two functional blocks is to have a generic infrastruc-
ture node that can be combined with various experimentation
nodes to support a range of heterogeneous experiments but
keep a unified way of nodes management and reconfiguration.
This way, the infrastructure node which takes care of commu-
nication for the remote management and hosting containers can
use more reliable components ensuring better uptime, whereas
the experimentation node that is more prone to errors and
crashes is executed on a separate hardware and configured
within a container. An additional benefit is that the required
dependencies do not need to be installed on the infrastructure
node; instead, all the required experimentation tools including
all the dependencies are packaged in a redistributable container
image which can be safely downloaded and executed on the
infrastructure node without sacrificing reliability.
B. Experiment description
The experiment description is based on the VESNA Man-
agement System (VMS) with custom developed protocol LCSP
(Lightweight Client Server Protocol) for communication be-
tween VESNA and the infrastructure node. On top of this
protocol we implemented an abstraction library called ALH
Tools which serves as basis for developing distributed wireless
The LCSP protocol is based on client-server architecture.
In our case the server side is on VESNA sensor nodes and
the client side is on a PC hosting VMS. In order to access the
resources of nodes, the gateway has to establish a connection
with the management system. This is done by setting up a
serial/TCP/SSL link. Resources that are pre-prepared on the
nodes are exposing different features. In the following we
describe the LCSP protocol characteristics which are essential
for understanding of the management system.
LCSP was developed for communication between sensor
network and a remote server. It was inspired by the HTTP
protocol, but purposely kept simple for fast and easy im-
plementation on VESNA nodes. The protocol defines two
methods, GET and POST, which are understood by each
VESNA node. GET method is used for “safe” requests which
do not change the state of the system. POST is used for
“unsafe” requests which can change the state of the system.
The response is considered to be in a binary format although
it is normally in a text format except the spectrum sensing
data which is in binary. Each response ends with a sequence
OK\r\n to mark the end of the response. The format of the
request and reponse is shown in Listing 1.
The protocol includes simple and efficient error handling
mechanism. There are two types of errors that can be en-
countered as a response to these requests. The first type
is JUNK-INPUT which is the more common situation when
someone mistypes the resource name and the parser on the
node does not recognize it. After this response, the parser on
the node expects 5 new lines which reset the parser before
one can try to access the resource again. The second type of
error is CORRUPTED-DATA, meaning that CRC check did not pass
successfully. Thus we can conclude that the error happened
somewhere on the link between the infrastructure and the
gateway. This situation will occur with lower probability.
Additionally VMS implements an HTTP API which trans-
lates HTTP requests to LCSP requests. The call to the API
has to meet the specified form for GET and POST request. In
order to make a GET or a POST request we have to make a
call to the handler called “communicator” located on the web
server inside the management system. Requests that do not
meet the template are rejected by the VMS. The call includes
several parameters:
Cluster - corresponds to the serial/TCP/SSL/ port
which VESNA is connected to.
Method - can be GET or POST.
Resource - corresponds to the resource name located
on the target node.
Content - a specific parameter of POST requests
specifies the data transmitted to nodes.
Client requests must be formated:
GET resource?arg1=val1&...&argN=varN\r\n
resource: abstract resource identifier
examples: - firmware/version
- sensors/temperature
arg1: parameter 1 name
val1: value of parameter 1
argN: parameter N name
valN: value of parameter N
POST resource?parameters\r\n
<data having len bytes length>\r\n
resource: abstract resource
for example: firmware
parameters: parameters given to the
POST handler same format
as GET parameters
len: length of the data that
will be written to the
specific resource
data: possibly binary data
to be transmitted
crc_value: CRC value calculated on all
the previous content except
the line starting with crc=;
value represented as an
unsigned decimal number
Server responses must be formated:
<response to a specific request>\r\n
Error messages must be formated:
<response with error description>\r\n
Listing 1. LCSP request and response format
The ALH Tools package provides utilities and modules for
managing VESNA-based wireless sensor networks that are us-
ing the proprietary LCSP protocol. In a typical setup, VESNA
nodes participate in the network with an infrastructure PC
connected over serial/TCP/SSL. In this network, each sensor
node exposes an HTTP-like interface, supporting two types of
requests: GET and POST. The TCP/SSL tunnel terminates in
an infrastructure server that performs the translation between
the LCSP protocol and a proper HTTP REST interface exposed
on the web.
+-----------+ +-----------+
| VESNA | LCSP --- | VMS | HTTP -->
+-----------+ +-----------+
Alternatively VESNA can also be directly connected to
a client over a serial line. This setup is typically used for
the development or debugging. The ALH Tools with LCSP
protocol transparently support both modes of operation. Op-
tionally either an URL of an HTTP REST endpoint is given
or a character device for the serial line.
ALH Tools are implemented in Python programming lan-
guage and can be installed as a standard Python package by
$ pip install vesna-alhtools
C. Experiment deployment and build automation
The experimentation system was designed to abstract the
underlying complexity and enable the desired workflow. Ev-
erything needed for the experiment is available in a GitHub
repository starting with the Ansible playbook which gets
executed when new release is created in the repository by
triggering GitHub Webhook listening on experiment controller,
which is part of the central management server. The playbook
contains a description of the experiment setup, which includes
the list of testbed devices performing the experiment while
the node registry holds all available testbed devices. There
are two parts of the experiment description first will be
executed on the target experimentation nodes and the second
in the experiment controller. The experimentation node will
download the GitHub repository containing the Docker file
which is the description of the automated build and the
actual experiment code. The Docker image will not be built
each time from scratch; the testbed generic image will be
downloaded from Docker store, which has all the needed
dependencies and services already installed and set up. Thus,
the experimenter only needs to write the code for the actual
experiment performed on the node and the part performed by
the experiment controller.
An important part of the experiment setup process is the
flashing of the experimentation node. This can be done by
choosing an existing embedded binary from the list of pre-
prepared embedded images suitable for the experiment, or by
specifying own source to be compiled for the experimentation
D. Result collection and publishing
Results produced by the distributed experiment framework
ALHtools are copied using Ansible commands to the experi-
ment controller which will eventually compress these results
in a single zip file along with the other documents produced
by running the experiment. When the process is complete, the
experiment controller will automatically upload the resulting
zip file to social network platform repository created by the
new release which initiated the experiment build. This way the
results become available to to the experimenter for download
and post processing. The results can either be in a raw binary
format or text format or some other standard format i.e. JPEG
or PNG. The format of the results is actually defined by the
experiment description.
In this paper we showed that wireless testbeds can be
integrated with social coding platforms thus avoiding unnec-
essary user account management and making testbeds more
attractive by lowering the initial effort needed to start using
experimental facilities. One direction of future work is the
automated experiment scheduler. Currently testbed resources
are manually and statically assigned during the experiment
description therefore only one user at the time is allowed
to access the testbed. The idea is to implement automated
time, frequency and space devision multiplex scheduler for the
testbed while also keeping experiment results reproducible.
This work was partly funded by the Slovenian Research
Agency (Grant no. P2-0016) and the European Community
under the H2020 eWINE (Grant no. 688 116), WiSHFUL
(Grant no. 645 274) and Fed4FIRE+ (Grant no. 732 638)
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Software developers rely on media to communicate, learn, collaborate, and coordinate with others. Recently, social media has dramatically changed the landscape of software engineering, challenging some old assumptions about how developers learn and work with one another. We see the rise of the social programmer who actively participates in online communities and openly contributes to the creation of a large body of crowdsourced socio-technical content. In this paper, we examine the past, present, and future roles of social media in software engineering. We provide a review of research that examines the use of different media channels in software engineering from 1968 to the present day. We also provide preliminary results from a large survey with developers that actively use social media to understand how they communicate and collaborate, and to gain insights into the challenges they face. We find that while this particular population values social media, traditional channels, such as face-to-face communication, are still considered crucial. We synthesize findings from our historical review and survey to propose a roadmap for future research on this topic. Finally, we discuss implications for research methods as we argue that social media is poised to bring about a paradigm shift in software engineering research.
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METIS is the EU flagship 5G project with the objective of laying the foundation for 5G systems and building consensus prior to standardization. The METIS overall approach toward 5G builds on the evolution of existing technologies complemented by new radio concepts that are designed to meet the new and challenging requirements of use cases today??s radio access networks cannot support. The integration of these new radio concepts, such as massive MIMO, ultra dense networks, moving networks, and device-to-device, ultra reliable, and massive machine communications, will allow 5G to support the expected increase in mobile data volume while broadening the range of application domains that mobile communications can support beyond 2020. In this article, we describe the scenarios identified for the purpose of driving the 5G research direction. Furthermore, we give initial directions for the technology components (e.g., link level components, multinode/multiantenna, multi-RAT, and multi-layer networks and spectrum handling) that will allow the fulfillment of the requirements of the identified 5G scenarios.
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Networking testbeds are playing an increasingly important role in the development of new communication technologies. Testbeds are traditionally built for a particular project or to study a specific technology. An alternative approach is to federate existing testbeds to a) cater for experimenter needs which cannot be fullled by a single testbed, and b) provide a wider variety of environmental settings at different scales. These heterogenous settings allow the study of new approaches in environments similar to what one finds in the real world. This paper presents OMF, a control, measurement, and management framework for testbeds. It describes through some examples the versatility of OMF's current architecture and gives directions for federation of testbeds through OMF. In addition, this paper introduces a comprehensive experiment description language that allows an experimenter to describe resource requirements and their configurations, as well as experiment orchestration. Researchers would thus be able to reproduce their experiment on the same testbed or in a different environment with little changes. Along with the efficient support for large scale experiments, the use of testbeds and support for repeatable experiments will allow the networking field to build a culture of cross verification and therefore strengthen its scientific approach.
Driven by the need to cope with exponentially growing mobile data traffic and to support new traffic types from massive numbers of machine-type devices, academia and industry are thinking beyond the current generation of mobile cellular networks to chalk a path towards fifth generation (5G) mobile networks. Several new approaches and technologies are being considered as potential elements making up such a future mobile network, including cloud RANs, application of SDN principles, exploiting new and unused portions of spectrum, use of massive MIMO and full-duplex communications. Research on these technologies requires realistic and flexible experimentation platforms that offer a wide range of experimentation modes from real-world experimentation to controlled and scalable evaluations while at the same time retaining backward compatibility with current generation systems. Towards this end, we present OpenAirInterface (OAI) as a suitably flexible platform. In addition, we discuss the use of OAI in the context of several widely mentioned 5G research directions.
Conference Paper
Popular social coding sites like GitHub and BitBucket are changing software development. Users follow some interesting developers, listen to their activities and find new projects. Social relationships between users are utilized to disseminate projects, attract contributors and increase the popularity. A deep understanding of project dissemination on social coding sites can provide important insights into questions of project diffusion characteristics and into the improvement of the popularity. In this paper, we seek a deeper understanding of project dissemination in GitHub. We collect 2,665 projects and 272,874 events. Moreover, we crawl 747,107 developers and 2,234,845 social links to construct social graphs. We analyze topological characteristics and reciprocity of social graphs. We then study the speed and the range of project dissemination, and the role of social links. Our main observations are: (1) Social relationships are not reciprocal. (2) The popularity increases gradually for a long time. (3) Projects spread to users far away from their creators. (4) Social links play a notable role of project dissemination. These results can be leveraged to increase the popularity. Specifically, we suggest that project owners should (1) encourage experienced developers to choose some promising new developers, follow them in return and provide guidance. (2) promote projects for a long time. (3) advertise projects to a wide range of developers. (4) fully utilize social relationships to advertise projects and attract contributors.
GENI, the Global Environment for Networking Innovation, is a distributed virtual laboratory for transformative, at-scale experiments in network science, services, and security. Designed in response to concerns over Internet ossification, GENI is enabling a wide variety of experiments in a range of areas, including clean-slate networking, protocol design and evaluation, distributed service offerings, social network integration, content management, and in-network service deployment. Recently, GENI has been leading an effort to explore the potential of its underlying technologies, SDN and GENI Racks, in support of university campus network management and applications. With the concurrent deployment of these technologies on regional and national R&E backbones, this will result in a revolutionary new national-scale distributed architecture, bringing to the entire network the shared, deeply programmable environment that the cloud has brought to the datacenter. This deeply programmable environment will support the GENI research mission and as well as enabling research in a wide variety of application areas.