A Bird’s Eye View on Social Network Sites and Requirements
Nazakat Ali
and Jang-Eui Hong
Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea
Keywords: Social Network Sites, Requirements Engineering, Overview, User Requirements.
Abstract: Social network sites have become popular and their popularity is growing exponentially every day. From the
requirements engineering point of view, social network sites have provided unprecedented opportunities for
software development organizations to understand the requirements of unknown end-users. Using social
network sites, end-users express their experiences, needs, or concerns about a particular system or a product.
Such information can be useful for software developers to address the concerns of users quickly. To get an
overview of how social network sites are helping requirements engineering and new research trends in this
area, we have surveyed a large number of research papers. We found that social network sites can be a major
source that can be used for requirements elicitation, requirements prioritization, and negotiation. We also
found that the research in this domain is at its beginning stage, but it is rapidly growing with the passage of
Social Network Sites (SNS) have become a major
source for millions of users to share their daily
activities, experiences, and opinions for a particular
product through posting. As of year, 2019, 2.77
billion users around the globe use SNS (Statista
2019). As a result, a huge amount of real-time and
highly diverse data is produced per day, which
brought a revolution in many research fields such as
in the data science field. In data science, this data can
be used to predict the political affiliations, forecasting
stock market fluctuations, and marketing trends, etc.
(Conver 2011). Being a communication channel, SNS
have been used in various software development
activities such as in Requirements Engineering (RE).
In RE, SNS have proved their support and improved
several process activities such as requirements
elicitation, negotiation, prioritization (kukreja 2012
and Seyff 2015), and identification and prioritization
of stakeholders (Lim et al. 2010). RE is a critical
activity of the software development lifecycle and
user participation in RE can lead to success in
software projects (Hofman et al. 2001). Therefore, it
seems most suitable to use SNS in order to conduct
various activities of RE due to the presence of a huge
population on it.
The purpose of this study is to present a bird’s eye
view survey of how SNS benefit RE activities. More
specifically, we are formulating the following
research questions to accomplish our goal.
RQI: What is the current state-of-the-practice of the
SNS (Facebook, Twitter, etc.) as a platform to
conduct RE activities?
RQ2: What RE activities are supported by SNS?
RQ3: Is SNS improving the state-of-the-art of RE?
RQ4: What challenges requirements engineers are
face while using SNS to conduct RE activities?
Based on these formulated research questions, we
have conducted a literature review to summarize the
existing literature in which SNS are considered as a
platform to conduct RE activities. The literature
review is conducted by splitting it through
preparation, data collection, and data analysis phase.
In preparation, a search string was prepared to search
the literature for answering formulated questions,
then searching was performed over a number of
databases such as Scopus, IEEEXplore, Springer
Link, Google Scholar, ACM Digital Library, and
Science Direct. All the identified research articles
Ali, N. and Hong, J.
A Bird’s Eye View on Social Network Sites and Requirements Engineering.
DOI: 10.5220/0008117303470354
In Proceedings of the 14th International Conference on Software Technologies (ICSOFT 2019), pages 347-354
ISBN: 978-989-758-379-7
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
were assessed by means of their title and abstract.
Along with this, references and citations of specific
articles were reviewed to explore more relevant
research articles of our focus. Our intention is not to
conduct an extensive literature review of the targeted
field - rather we are presenting a snapshot of the state-
of-the-art by following some guidelines (study
selection, formulating research questions,
information synthesis, and result reporting ) of
Kitchenham (Kitchenham 2004) to conduct
systematic literature in our targeted field.
The major contributions of this study are as
We are presenting an overview of the state-of-
the-art on how SNS supports existing RE
activities. Precisely, we are focusing on how
requirements elicitation, requirements analysis,
and requirements management is supported by
utilizing SNS.
Since machine learning is utilized to carry out
all these mentioned RE activities, therefore, we
are offering an overview on how machine
learning algorithms (classification, clustering,
regression, etc.) are utilized to get full advantage
of SNS data.
Furthermore, we analyze existing literature to
uncover research trends and future research
directions on the use of SNS for conducting RE
The rest of this paper is organized as follows. Section
2 provides necessary background information on SNS
and RE is required to understand the rest of the paper.
Section 3 presents an overview of RE activities where
SNS is used as a platform to conduct RE. In section
4, we summarize our major findings of this research
and answer the formulated questions while section 5
concludes this paper.
The fundamental goal of a software system is to meet
the requirements of its users by offering
functionalities that can satisfy and fulfill the
expectations of its users. This goal is accomplished
by applying several engineering techniques. RE is an
activity through which user requirements are
identified for a specific domain in a systematic
manner to understand what features a specific product
should have in order to satisfy its users’ expectations.
The success of a software project mainly depends
on its RE process because requirements are the
determining factor of software quality. The empirical
evidence has shown that errors in the requirements
are most common in the software development
lifecycle and it is considered as most time- consuming
and expensive task to correct in later stages (Wohlin,
2005). According to a research report (Standish,
2014), 52.7% of projects met with challenges and
problems and cost 189% of their total estimated
budget. The report shows that only 16.1% of all US
projects completed on-budget, on-time with all
planned features, while 31.1% of projects were never
completed. The investigation report identified poor
requirements as the main source of problems, along
with other factors such as unclear objectives and low
user involvement. Likewise, another survey
(European 1996) which was carried out with 3800
organizations from 17 countries in Europe, the
findings show that half (50%) of the problems were
due to requirements specification and rest half (50%)
problems emerged due to requirements management.
In this section, we will summarize the existing
literature which has considered SNS as a platform to
conduct RE activities. A comprehensive inclusion
and exclusion criteria were followed to select the
literature. As a whole, Kitchenham et al. guidelines
(Kitchenham, 2004) were partially followed to
conduct this survey.
The success of any software project majorly
depends on the accurate identification of
stakeholders’ expectations and requirements for their
anticipated system. Requirements elicitation through
the manual process is costly in terms of resource and
efforts. Therefore, SNS provided a platform to
conduct requirements elicitation by mining
microblogs e.g., social media, app store reviews, and
requirement documents. The mining task is carried
out by applying various techniques such as Natural
Language Processing (NLP), text mining and
Machine Learning (ML) (Ali, 2016). In current
practice, popular SNS such as Twitter, Facebook, and
Snapchat are used to extract user requirements by
applying ML techniques. User comments, tweets, or
reviews are not structured documents, they contain
useful information along with noise which makes
difficult to extract user requirements with manual
annotations. Therefore, ML techniques are most
favorable to identify user requirements automatically
ICSOFT 2019 - 14th International Conference on Software Technologies
or semi-automatically by reducing efforts, cost, and
time significantly.
3.1 RE, ML and Social Network Sites
Guzman et al. (Guzman, 2016) have proposed an
approach that uses Twitter as a platform to elicit user
requirements. Their exploratory study investigated
the content of 10,986,494 tweets about 30 software
application. The authors have categorized tweets into
more 22 categories including feature shortcomings,
bug reports, feature request, and feature strength, etc.
The authors have applied Support Vector Machine
(SVM) and C4.5 ML classifiers to classify the tweets
automatically. Both classifiers had a similar
performance to classify tweets related to non-
technical stakeholders. However, SVM performed
well to classify tweets related to technical
stakeholders and the general public.
Guzman et al. (Guzman, 2017(a)) presented a
similar study for classifying, ranking and grouping
tweets for software evolution. The authors used
Twitter as a platform to elicit user requirements. The
proposed approach classifies tweets into two
categories including improvement request and others.
The authors used 68,108 tweets for their experiment.
Naïve Bayes (NB) and Random Forest (RF)
classifiers were used to classifying the tweets. In
summary, the proposed approach was able to classify
tweets automatically into improvement request and
other categories with F-measure of 0.79.
Williams et al. (Williams, 2017(a)) proposed an
approach which also used Twitter as a platform to
elicit user requirements. The authors first analyzed
4000 tweets of 10 various software systems to
classify tweets into bug reports, user requirements
and miscellaneous and spam. Then, the authors used
NB and SVM to classify the tweets automatically.
The authors used three classification features (bag-of-
words, sentiment and stemming) for their tweets
classification. The study claims that the sentiment
feature did not affect classification results. The
authors used 188,737 unique tweets for their
Xiao et al. (Xiao, 2015) have used popular SNS
such as StackOverflow, a popular Q & A software
community site, where programmers and software
engineers share their knowledge and experience. The
collected data from StackOverflow was used to
extract requirements requests to help software
developers. In their study, SVM classifier was used to
test the data. The authors extracted tagged questions
along with related information to elicit requirement
requests for a relevant software application. Initially,
three ML algorithms including pure SVM, linguistics
rules and SVM with dictionary were selected for
experiments and comparison. SVM with requirement
dictionary is the combination of a dictionary
(keyword sets) and SVM to enhance the performance
of classification. It is concluded from the
experimental result that SVM, with precision 69%,
recall 75% and F-measure 72%, performed better
than traditional linguistic rules. SVM with
requirement dictionary improved performance with
precision 72%, recall 77% and F-measure 74.4%.
Prasetyo et al. (Prasetyo, 2012) performed a
preliminary study to investigate the feasibility of
automatic classification of microblogs into relevant
and irrelevant categories. All those tweets that could
help software developers to understand user needs
were categorized as relevant tweets while rest were
categorized as irrelevant tweets. The authors used 300
tweets of [32] comprising either one of the following
9 hashtags including #csharp, #scrum, #javascript,
#opensource #.net, #jquery, #testing, #azure, and
#java, and tweets were classified into 10 categories
including news, commercials, tools and code, events,
personal, tips, opinions, jobs, Q &A, and
miscellaneous. The aim of this study was to classify
tweets automatically into relevant and irrelevant.
Therefore, the authors have re-labeled data set into
relevant and irrelevant categories. If a tweet was
potentially interesting to a software developer to
develop a target system, otherwise they labeled it as
irrelevant. The result shows that 41% of tweets were
relevant while 53% were irrelevant. For the automatic
classification of tweets, the authors used SVM
Guzman et al. (Guzman et al., 2017(b)) used
Twitter to elicit user requirements. This study reports
on the usage characteristics, contents, and automatic
classification potential of user tweets about RE and
software evolution. The authors explored a dataset
containing 10,986,495 tweets about 30 various
software applications by using descriptive statistics,
content analysis, lexical sentiment analysis, and ML.
The authors have used NB, multinomial NB, SVM,
J48, and RF to classify a huge amount of user tweets
according to the relevance to different stakeholders
and user type. The result shows that the classifiers
produced promising results in some cases. The
automated relevance classification was possible with
an F-score ranging from 77% to 52%, while the
identification of tweets tweeted by bots got a
promising F-score of 84%. This study has performed
a fine granular evaluation of user feedback from
Twitter and its comparison with App store reviews.
A Bird’s Eye View on Social Network Sites and Requirements Engineering
Deshpande et al. (Deshpande et al., 2018)
proposed a study that assessed user feedback from
Twitter in terms of timing as well as content and
compared with the App store review. This study
employed various text analysis and NLP methods
such as semantic analysis to analyze tweets and app
store reviews. The authors have used SVM and NB
for their classification. The analysis of topics showed
that 72% of tweets were functional requirements
which discussed the behavioral aspects of mobile
apps while in the App store it was 80%.
Singer et al. (Singer et al., 2014)) surveyed 271
GitHub users and interviewed 27 software developers
to know about the importance of Twitter in software
development. Authors found that developers often
rely on various online resources, such as Twitter to
keep themselves informed about their systems. The
authors also found that Twitter helps software
developers to stay aware of industry changes,
technology changes and for learning.
Williams et al. (Williams et al., 2017(b))
presented a preliminary study aiming to detect and
interpret emotions present in software related tweets.
The preliminary study was conducted using a dataset
of 1000 tweets taken from tweets of a broad range of
software applications. At first, the tweets were
manually classified by two annotators with 5 years of
programming into two levels of abstractions,
including its general emotional polarity such as
positive, negative and neutral, and the specific
emotions it conveys. After manual classification
dataset was trained and applied SVM and NB to
extract sentiment automatically. The authors have
seen that emotions were closely related to specific
events such as product release etc. The results also
show that tweets which carry negative sentiment
usually report bugs, report feature shortcoming, etc.
Seyff et al. (Seyff et al., 2018) propose a crowd
and sustainability-focused platform for semi-
automated requirements elicitation, negotiation, and
analysis. This platform enables a diverse and
distributed group of stakeholders or users (crowd) to
communicate and negotiate their requirements. The
proposed conceptual solution includes three
components: 1) CrowdFeed enables users to give
their feedback regarding the software service or
products they use, 2) Requirements and Sustainability
Service classifies, clusters, and analyze the feedback
received from the crowd, 3) Requirements and
Sustainability Integrator supports the visualization
and assessment of effects on sustainability.
3.2 RE and Social Network Sites
Lee et al. (Lee et al., 2011) used both Twitter and
Facebook for requirements elicitation and
prioritization. The proposed approach has three
phases including preparing phase, gathering phase
and refining phase. The preparing phase describes the
preparation for requirements elicitation through SNS.
In the preparation phase, a keyword-based method is
used to approach a large number of user opinions. The
authors first selected domain related keywords e.g.
smartphone app, smartphone OS, smartphone design,
etc. The frequency of selected domain-related
keywords was recorded to select those keywords
which are most discussed on SNS. In the gathering
phase, the SNS and its users were approached, and
their opinions were collected. This phase was further
divided into two parts according to types of access to
the subject and the direction of approach: opinion-
based access and community-based access. Opinion-
based access targeted individual users, not SNS
communities while in community-based access
diverse communities were targeted and their opinions
were collected.
Seyff et al. (Seyff et al., 2015) proposed an
approach for RE that employs Facebook for
elicitation, prioritization, and negotiation of
requirements. The authors have conducted three
exploratory studies using Facebook to see whether the
potential of popular SNS (i.e. Facebook) can allow
and support end users to participate in requirements
elicitation, prioritization, and negotiation. This study
is inspired by EasyWinWin (Gruenbacher, 2000) and
authors foresaw that their proposed lightweight and
end-user focused RE approach had many potential
applications. The authors particularly see its
relevance with new software paradigms such as cloud
computing, mobile computing, and ecosystems,
where potential end users are not within instant reach
and support provided by traditional RE methods is
insufficient. The authors claim that their approach can
provide a new channel for eliciting innovative ideas
and needs as well as getting feedback from
stakeholders who are not directly reachable by
software development organizations or software
development teams. Furthermore, authors see its
applicability within traditional software projects as an
additional means for engaging end users.
Ali et al. (Ali et al., 2017) considered both Twitter
and Facebook to evolve systems. The authors
proposed a cyclic process that elicits user
requirements continuously from SNS. After data
extraction, the corpus is gone through a
comprehensive NLP phase to elicit user requirements.
ICSOFT 2019 - 14th International Conference on Software Technologies
In this approach, bigrams and trigrams were exacted
to get the related keywords. Later these bigrams and
trigrams were interpreted as user requirements.
Furthermore, the authors used the ontology-based
similarity algorithm to detect redundant keywords.
After requirements identification, a feature model
was manually built. After every iteration, the feature
model was updated, and the system was reconfigured
to adapt to the changes. In another study (Ali et al.,
2016) authors have elicited domain requirements
from SNS to support software product line evolution.
This study has analyzed Facebook, Twitter, and
LinkedIn and selected Twitter and Facebook as a
platform to elicit user requirements.
Based on the win-win (Gruenbacher, 2000)
negotiation model, Kukreja et al. (Kukreja, 2012)
developed a social network site such as “Winbook”,
which is based on the social networking paradigm.
Winbook is similar to Facebook, but its contents are
organized using color-coded labels similar to Gmail.
It also resembles Excel because it has the ability to
prioritize requirements by performing a number of
sensitivity analyses with respect to business goals.
This study is able to elicit, negotiate and prioritize
user requirements.
Romasha et al. (Romasha et al., 2018) have
conducted a survey to know how SNS empower RE.
In this questionnaire-based survey, among
respondents, 41% were software developers, 31 were
team leaders, and 10% of respondents were a business
analyst. As per the survey result, majority
respondents agreed that RE is a communication
activity and 73.8 % respondent expressed that they
face communication problem to gather requirements.
Among respondents, 57% of respondents reported
that they used Facebook to elicit user requirements
and solve the communication problem.
Maalej et al. (Maalej et al., 2015)] have discussed
the usage of SNS and app stores to elicit user
requirements. The authors discussed how software
development organizations can use user feedback to
identify, prioritize, and manage requirements. The
authors have seen three directions in practice. First,
tools for feedback analytics would help in order to
deal with a large number of user opinions by
classifying, summarizing, and filtering them. Second,
automatically collected opinions, logs, and
interaction traces could help software developers to
understand user feedback and improve feedback
quality. Third, once the feedback is collected and
analyzed, the question arises how can software
development organizations use this information and
integrate it into their software development process to
decide when to release new updates. What feature or
requirements should be added, deleted, or changed?
Groen et al. (Groen et al., 2017) have proposed
CrowdRE approach to elicit user requirements from
the crowd. The authors considered the crowd as a
sender of the feedback and software development
companies as a receiver. The authors dived user
feedback into two categories: pull feedback and push
feedback. Push feedback is when software
development organizations explicitly ask the crowd
for feedback while the push feedback is when the user
in the crowd start a discussion. In both cases, user
feedback was utilized to elicit user requirements.
Additionally, the authors have listed a number of
challenges as well including crowd motivation,
privacy and personalization, and feedback
analyzation issues.
Mughal et al. (Mughal et al., 2018) have proposed
a network-based process using Facebook to reduce
the in-group biasness during RE. The proposed
approach combined hybrid centrality measure and
power, legitimacy, and urgency technique. This study
has focused on stakeholder identification and
prioritization during RE. The main strength of this
article is that it has shown better performance than
partner biasness issue through hybrid centrality
measure and the power, legitimacy, urgency and
urgency model, as claimed. The feasibility of the
proposed social network-based process for
identification and prioritization of requirements of
stakeholders is shown through a controlled
experiment conducted on an example set of 40
Kanchev et al. (Kanchev et al., 2017) have
proposed a methodology called Canary that generates
a requirements-oriented view of online discussions.
The methodology is a conceptual model that brings
requirements-relevant and argumentation-relevant
aspects of online discussions together. The authors
have claimed that it was feasible to convert online
discussions into instances Canary model by
crowdsourcing annotations of the discussions.
Our study focuses on a specific area and points to a
number of trends that we will explain in this section.
Overall, the goal of this study is to provide a state-of-
the-art of use of SNS to conduct various activities of
RE for giving an overview to domain experts and act
as a foundation to this field for researchers.
Furthermore, the results we provide here are derived
from the literature review we conducted. Our survey
A Bird’s Eye View on Social Network Sites and Requirements Engineering
Table 1: Contributions and RE activities supported by SNS.
Objective/Purpose ML Tasks
RE Activities
Guzman et al.
Categorization of tweets into 22 categories including
bug reports, feature shortcoming, feature demand etc.
Classification Elicitation
Guzman et al.
Classification of tweets into two categories:
improvement request and others.
Classification Elicitation
Williams et al.
Classification of tweets into bug reports, user
requirements, and miscellaneous
4 Xiao et al (-) 2015 Extract requirement requests Classification Elicitation
5 Lee et al. (+) 2011 Requirements elicitation and prioritization -
6 Seyff et al. (-) 2015
Requirements elicitation, negotiation, and
prioritization using SNS
7 Ali et al. (*) 2017
Eliciting user requirements from SNS to evolve
software product lines
Prasetyo et al.
To know the relevance of tweets with software
Classification Elicitation
Guzman et al.
searching for relevant information on software
applications within the vast stream of tweets
Deshpande et
al. (*)
Comparison between App store reviews and Tweeter
messages for requirements
Classification Elicitation
Kukreja et al.
2012 Developing Winbook -
Romasha et
al. (*)
Conducting a survey that to know how SNS empower
Maalej et al.
The authors discussed how software development
organizations can use user feedback to identify,
prioritize, and manage requirements
14 Leif et al. (*) 2014
Surveying and interviewing software developers to
know about the importance of Twitter in software
‐ ‐
Williams et al.
presenting a preliminary study aiming to detect and
interpret emotions present in software related tweets.
Sentiment Elicitation
Groen et al.
Proposing CrowdRE approach to elicit user
requirements from crowd.
- Elicitation
Mughal et al.
proposing a network-based process using Facebook to
reduce the in-group biasness during RE
prioritization in
20 Ali et al. (-) 2018
Introducing the idea of a platform for crowd-focused
requirements engineering that supports the evolution
towards sustainability
Intended to
Kanchev et al.
extracting and querying requirements-related
information in online discussions (reddit)
- Elicitation
22 Seyffetal.() 2018
sustainability-focused platform for semi-automated
requirements elicitation, negotiation, and analysis
Legends: (+) improves the state of the art; (*) no information how it connects with state of the art; (-) comparable with state
of the art;
is not a fully systematic literature review (as
mentioned in section 1) and our conclusions and
results can be revised or extended by future studies in
this domain. Table 1 provides answers to RQ1
(“What are the current state-of-the-practice that use
SNS as a platform to conduct RE activities?”) and
RQ2 (“What RE activities are supported by SNS?”).
Twenty-one papers (Contributions column in Table
1) were thoroughly examined to know the state-of-
the-art which employs SNS to conduct RE activities.
We have seen that; the majority of studies were initial
proposals with a little academic literature and no
industrial applications in real software projects. ML
tasks and RE support Activities columns, as
mentioned in Table 1, answer our RQ2. The
contributions column in Table I also provides a partial
response to RQ 3 (“Is SNS improving the state-of-
the-art of RE?”). The answer to RQ3 appears to be at
its beginning, given the prevalent lack of comparison
with the state of the art as can be seen in Table 1. The
ICSOFT 2019 - 14th International Conference on Software Technologies
RQ3 was answered by comparing proposed
approaches, that consider SNS for RE, with
traditional state-of-the-art. Some additional
information is also given in Table 1 i.e. what kind of
ML models were used in order to elicit user
requirements from the corpus. We have seen that NB
and SVM are mostly used for classification.
The SNS contains a huge amount of data
including user expectations and experiences.
Software developers could use this rich information
to understand the needs, experiences, and sentiment
of their product users. Mining SNS especially user
opinions, may yield useful information for the
software development organization to update their
products. However, extracting user requirements out
of huge data is also a challenging task. The opinions
are usually expressed without considering grammar
format which creates problems lately for corpus
processing. Therefore, we have seen that the collected
data was unstructured. Software developers rely on
user feedback for requirements elicitation,
negotiation, and prioritization. However, the
trustworthiness of comments, tweets, or, in general,
feedback is still a big challenge for the software
development community. Groen et al. (Groen et al.
2017) have mentioned a number of challenges in this
domain. User motivation is one of the big challenges
to get feedback. User privacy and personalization are
other issues which RE community faces while using
SNS as a platform to elicit user requirements. These
observations answer RQ4 (“RQ4: What are the
challenges requirements engineers face while using
SNS to conduct RE activities?”).
Table 1 did not mention the NLP techniques
applied in our surveyed papers. It is obvious that NLP
techniques are widely used by the majority of
researchers in their research that tackles the
application of SNS for RE. This is not astonishing and
even more common in data science. We observed that
NLP was used in the preprocessing phase in order to
bring data into the desired format that can be
consumed by ML algorithms. Majority of the articles
we have gone through in our survey have the opinion
that a large amount of imprecise data produced by
SNS users can bring about enormous benefits to
software development organizations when processed
by applying ML algorithms. The majority of papers
we surveyed on the topic of SNS and RE have to do
with either elicitation or prioritization activities of RE
and a little literature talked about negotiation activity
as well. Figure 1 shows the result of our investigation.
As mentioned above, the research in the domain
of RE and SNS is at its beginning but the research
trend is growing rapidly with the passage of time.
Figure 1: RE activities supported by SNS in our study.
Figure 2 shows the number of publications with
the passage of time which clearly shows the ongoing
research trend on SNS and RE domain. Figure 2 also
shows the number of publications that used ML
techniques to support RE activities.
Figure 2: Number of publications in SNS and RE domain.
Through our bird’s eye view on SNS and RE, we have
seen that in the past few years a research trend has
started to bring these two worlds together. The
investigation shows that studies in this domain are at
the beginning stage. Although, the academicians have
started to explore the potentials of SNS in order to use
it to conduct RE activities.
We have conducted a literature survey to know
how SNS is being used to conduct RE activities. We
formulated four types of research questions (Section
I) and extracted data according to answer these
questions. RQ1 revealed the ongoing research that
attempted to bring together SNS and RE worlds. RQ2
exposed that what kind of RE activities are supported
by SNS and what are the ML algorithms are used to
extract the required information from the SNS data.
In RQ3, we have seen that research in this domain is
in its early stages and need more reliable approaches
that will benefit software projects in the real world.
Finally, RQ4 answered that what challenges are faced
by the research community while bringing two worlds
A Bird’s Eye View on Social Network Sites and Requirements Engineering
This research was supported by Next-Generation
Information Computing Development Program
through the NRF of Korea funded by the Ministry of
Science, ICT (NRF-2014M3C4A7030505, NRF-
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