=Paper= {{Paper |id=Vol-2767/paper09 |storemode=property |title=Understanding the Involvement of Developers in Missing Link Community Smell: An exploratory Study on Apache Projects |pdfUrl=https://ceur-ws.org/Vol-2767/08-QuASoQ-2020.pdf |volume=Vol-2767 |authors=Toukir Ahammed,Moumita Asad, Kazi Sakib |dblpUrl=https://dblp.org/rec/conf/apsec/AhammedAS20 }} ==Understanding the Involvement of Developers in Missing Link Community Smell: An exploratory Study on Apache Projects== https://ceur-ws.org/Vol-2767/08-QuASoQ-2020.pdf

8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

Understanding the Involvement of Developers in Missing
Link Community Smell: An Exploratory Study on Apache
Projects
Toukir Ahammed, Moumita Asad and Kazi Sakib
Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh

Abstract
Missing link smell occurs when developers collaborate in source code without communication. This can affect software
maintenance by the means of lacking mutual awareness, mistrust and knowledge gap. Existing studies have investigated
the relationship of missing link smell with code smell and different socio-technical factors like turnover. This study aims to
understand how many developers are involved with missing link smell, by calculating the percentage of smelly developers
for a project. The study also investigates the relationship between the number of contributions and the number of missing
link involvements of a developer. The result shows that the percentage of smelly developers involved with missing link smell
is 8.7% on average. The result also suggests a moderate positive correlation between the contribution of a developer to the
project and the involvement in smell.

Keywords
missing link smell, community smell, software engineering, empirical analysis

1. Introduction The detection of missing link smell and its impact on
software artifacts have been analyzed in previous stud-
Community smells are the organizational and social anti- ies. S. Magnoni proposed the identification pattern of
patterns in a development community [1]. Community missing link community smell [3]. Tamburri et al. ex-
smells may lead to the emergence of social debt which amined the relationship between community smells and
indicates unforeseen project costs connected to a sub- different socio-technical factors, e.g., socio-technical con-
optimal software development community. Community gruence, turnover etc [4]. This study considered missing
smells may not be an immediate obstacle for software link, organizational silo, black cloud and radio silence
development but these can affect software maintenance community smell. Palomba et al. investigated the impact
negatively in the long run [2]. Missing link is one of the of missing link smell and four other community smells on
common community smells. It refers to the condition code smell intensity [2]. Catolino et al. analyzed the role
when two co-committing developers show uncooperative of four community smells including missing link smell
behavior by not communicating [3]. on gender diversity and women participation in open-
Missing link community smell decreases communi- source community [5]. However, developer involvement
cation activities in the development community. The in missing link smell and how developer contributions in
lack of communication and cooperation negatively af- the project relate to missing link smell have not been ana-
fects mutual awareness and trust among developers [3]. lyzed yet. In this context, the current study aims to focus
A software product can be thought of as the combined ef- on these factors by addressing the following Research
fort of all developers. So, collaboration along with proper Questions (RQs).
communication is necessary among developers. It is im- RQ1: How many developers are involved in miss-
portant to know how many developers are involved in ing link community smell?
missing link smell as they may affect the whole project. In an open-source project, there can be many devel-
Identifying these developers and analyzing their charac- opers who contribute to the project. All the developers
teristics is important. This will help the project managers may not be involved in missing link community smell.
to take steps such as task reassigning, team reformation, This RQ aims to find how many developers are involved
increasing awareness about communication etc. to keep in missing link smells in a community. This is important
communication issues lower among the developers in to know the collective contribution of developers to the
the community. number of missing link smells in a project. This finding
QuASoQ 2020: 8th International Workshop on Quantitative will help the project managers to understand the severity
Approaches to Software Quality of communication issues among developers in the com-
email: bsse0806@iit.du.ac.bd (T. Ahammed); bsse0731@iit.du.ac.bd munity. The action can be different to mitigate missing
(M. Asad); sakib@iit.du.ac.bd (K. Sakib) link smell based on the number of developers involved
© 2020 Copyright for this paper by its authors. Use permitted under Creative
CEUR
Workshop
http://ceur-ws.org
ISSN 1613-0073
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
in smells.
Proceedings

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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

RQ2: How does missing link smell relate with a
developer contribution?
This RQ focuses on the involvement of individual de-
veloper in missing link smell. This RQ relates an impor-
tant characteristic of a developer, i.e., contribution, to
missing link smell. This finding will help project man-
agers understanding which type of developers involve
more in missing link smell. This information can be used
to decide which developers can be monitored to control
missing link smell in the community from the beginning
of a project. Figure 1: Developer Social Network
In this study, missing link smells are analyzed on seven
open-source projects of Apache ecosystem. These projects
are selected for being large enough to analyse and the
in the defined communication channel, i.e., mailing list.
availability of communication data, i.e., mailing list. First,
Two developers are connected through an edge if they
the instances of missing link smell are detected in each
replied in the same e-mail within a given time frame [3].
project. The missing link smell is identified by finding
A communication network is illustrated in Figure 3.
cases where a collaboration does not have its communi-
Missing Link Community Smell: A missing link
cation counterpart. Then the developers associated with
community smell occurs when a couple of developers
each smell are identified by extracting the instance of
collaborate with each other but show uncooperative be-
smell. The fraction of developers involved with missing
haviors by not communicating. This smell can be identi-
link smell is calculated to check whether a subset of de-
fied by detecting collaboration between two developers
velopers are involved with this type of smell. Then the
that do not have the communication counterpart in de-
correlation is investigated between the contribution of
fined communication channel, e.g., development mailing
developers and their involvement in missing link smells.
list [3].
The results of the study show that a small part of the
An example of DSN is illustrated in Figure 1. The up-
total developers involved with missing link community
per part of the graph represents communication and the
smell. On average, 8.7% of the total developers of a project
lower part represents the collaboration among develop-
are involved with missing link smell. This study also finds
ers. The developers are connected with a solid line if
a significant moderate positive correlation between the
they communicate with each other. The developers are
developer contribution and their involvement in missing
connected to the file icon through a dashed line if they
link smell.
contribute to that source code file.
The collaboration and communication network can be
2. Background generated separately from this DSN. Figure 2 and Fig-
ure 3 represent the collaboration and the communication
This section provides some important terminologies to network respectively. The missing link smell can be iden-
better understand the missing link community smell. tified comparing the collaboration network with the com-
Developer Social Network (DSN): A network of a munication network. There is a link between developer
software development community where a node repre- E and F in the collaboration network (Figure 2) but there
sents developer and relationships between developers, is no corresponding link between these two developers
e.g., communication, coordination, are represented by an in the communication network (Figure 3). Developer E
edge. and F are collaborating on the same part of source code
Collaboration Network: A specific type of DSN which but they are not connected through any communication
indicates the collaboration in a development community. link. Thus, this is considered as an instance of a missing
Here, a node represents a developer who contributes to link between developer E and F.
the project in the version control system. Two develop-
ers are connected through an edge if they contribute to
the same part of source code within a given time frame 3. Related Work
[3]. Figure 2 represents an example of a collaboration
In recent years, community smells are studied to incor-
network.
porate the organizational and social aspects of developer
Communication Network: A specific type of DSN
community in software engineering research. Some stud-
which indicates the communication within the defined
ies focused on defining different community smells that
communication channel of a development community.
can lead to unforeseen project costs [1], [6]. On the other
Here, a node represents developers who communicate
hand, some studies investigated the impact of community

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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

zational Silo, Lone Wolf, Black Cloud and Radio Silence.
They found that gender diverse team had a lower num-
ber of community smells than non-gender diverse team.
They also showed that gender diversity and women par-
ticipation were important factors for Black Cloud and
Radio Silence whereas organizational Silo and Lone wolf
were found partially related.
The existing studies have focused on community smells
and the impact of these smells on software artifacts. The
Figure 2: Collaboration Network phenomenon of community smells is surrounded with
developers in a development community. However, devel-
oper involvement in missing link smell and the relation
between missing link smell and developer contributions
have not been investigated yet. So, the developers in-
volved with community smells and how their contribu-
tion relate to missing link smell need to be explored.

Figure 3: Communication Network 4. Methodology
This study aims to understand how many developers of a
project are involved in missing link smell. This study also
smells on different software artifacts [2], [4].
wants to assess the relationship between a developer’s
Tamburri et al. first introduced the concept of social
contribution and involvement in missing link smell. First,
debt in software engineering [6]. Later, in an industrial
the missing link smell is detected for all the selected
case study, they improved and elaborated the definition of
projects. Then the percentage of smelly developers is
social debt. In the same study, they defined nine different
retrieved for each project. Later, the correlation analysis
community smells which are connected to social debt
is performed between a developer’s contribution and
[1]. They also suggested a list of possible mitigations of
involvement in missing link smell.
community smells such as learning community, cultural
conveyors, stand-up voting etc., to avoid the negative
effects. 4.1. Dataset
Magnoni proposed the identification pattern of four
In this work, seven large open-source projects belonging
out of nine community smells [3] defined in [1]. He
to APACHE ecosystem are selected for analysis. These
developed an open-source tool CODEFACE4SMELLS 1 as
projects have been chosen because they are large and the
an extension to CODEFACE [7]. This tool is capable of
mailing lists are publicly available. Table 1 provides the
detecting community smells from the change history
list of analysed projects with their name, source code link,
in the version control system and the communication
development mailing list and analysis period. All projects
history in development mailing list.
are hosted in online version control system GitHub and
Tamburri et al. analysed the distribution of community
the development mailing list archives are available on
smells in open-source projects [4]. They also assessed the
Gmane 2 .
relation between community smells and existing socio-
The selected projects are large enough in terms of
technical quality factors, e.g., socio-technical congruence,
community members and the number of commits. The
communicability, turnover etc.
projects have 668 community members on average. All
Palomba et. al examined the relationship between so-
the projects have a substantial number of commits, with
cial and technical debt [2], [8]. They assessed the impact
an average of 10359. Thus the study has enough collabo-
of community smells on code smells. They found commu-
ration and communication data for analysis.
nity smells significantly influencing code smell intensity.
They also proposed a community-aware code smell in-
tensity model in which both technical and community 4.2. Missing Link Smell Detection
related factors were considered. The selected projects are analysed using a six-month anal-
Catolino et al. analysed the role of gender diversity ysis window. The analysis period of a project starts from
and women participation in community smell [5]. They when both communication in mailing list and change
considered four types of community smell i.e., organi- history in repository are available. A few more months
1 2
https://github.com/maelstromdat/CodeFace4Smells http://gmane.io

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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

Table 1
List of Analysed Projects

# Project Name Source Code Mailing List Analysis Period
1 Apache Cassandra github.com/apache/cassandra gmane.comp.db.cassandra.devel Oct-2009 - Sep-2020
2 Apache Cayenne github.com/apache/cayenne gmane.comp.java.cayenne.devel Nov-2007 - Aug-2020
3 Apache CXF github.com/apache/cxf gmane.comp.apache.cxf.devel Nov-2010 - Sep-2020
4 Apache Jackrabbit github.com/apache/jackrabbit gmane.comp.apache.jackrabbit.devel Dec-2005 - Sep-2020
5 Apache Jena github.com/apache/jena gmane.comp.apache.jena.devel Oct-2012 - Sep-2020
6 Apache Mahout github.com/apache/mahout gmane.comp.apache.mahout.devel Oct-2008 - Aug-2020
7 Apache Pig github.com/apache/pig gmane.comp.java.hadoop.pig.devel Oct-2010 - Aug-2020

are excluded to make the analysis period divisible by six To calculate the percentage of smelly developers in a
months. The analysis period for each project is given in project, the total number of developers of that project is
Table 1. For example, Apache Cassandra project has the required. The total number of developers is defined as
analysis period of 11 years starting from October 2009 to the sum of the number of developers who contribute to
September 2020. source code and the number of members who communi-
For every analysis window of a project, a communica- cate on mailing list [3]. The total number of developers
tion network and a collaboration network is built. The of a project is obtained by counting the number of mem-
communication network is generated by extracting com- bers present in either collaboration or communication
munication data from development mailing list and the network generated by 𝐶𝑂𝐷𝐸𝐹 𝐴𝐶𝐸4𝑆𝑀𝐸𝐿𝐿𝑆. The per-
collaboration network is generated by extracting collab- centage of smelly developers of a project is calculated
oration data from the project repository. After having using the following formula (Equation 1),
both communication and collaboration networks, the in- 𝑛𝑢𝑚𝑆𝐷𝑥
stances of missing link smell are identified by comparing 𝑝𝑒𝑟𝑐𝑆𝐷𝑥 = × 100%, (1)
every collaboration link with communication networks. 𝑡𝑜𝑡𝑎𝑙𝐷𝑒𝑣𝑥
If any collaboration link does not have its communica- where 𝑛𝑢𝑚𝑆𝐷𝑥 is the number of smelly developers in
tion counterpart, this link is identified as a missing link project 𝑥 and 𝑡𝑜𝑡𝑎𝑙𝐷𝑒𝑣𝑥 is the number of total developers
instance. in project 𝑥.
An open-source tool, CODEFACE4SMELLS [4], is used
to detect missing link community smell in this study. 4.4. Correlation Analysis
This tool is capable of detecting missing link smell in
the aforementioned way from project repository and RQ2 aims to understand the relationship between a de-
development mailing list. The tool requires the link of veloper’s contribution and involvement in missing link
source code repository and mailing list archive as input. smell. To address this RQ, the correlation between fol-
Then the tool returns a list of missing link instances for lowing two measures is analysed:
each window of the project. A missing link instance is 1. how many commits a developer has in the project
represented by a pair of developers. For example, (𝑎, 𝑏) repository
represents a missing link instance between developer 𝑎 2. how many times a developer is involved in miss-
and 𝑏. ing link smell
In open-source projects, commits are the most representa-
4.3. Smelly Developers Identification tive form of coding contribution [9]. So, the contribution
A developer involved with a missing link smell is consid- of a developer in a project is measured by the number
ered as a smelly developer. An instance of missing link of commits of that developer in the project repository.
smell consists of two collaborating developers who do The number of commits of every individual developer is
not communicate with each other. Thus for every miss- retrieved from the source code repository.
ing link smell, there are two smelly developers. CODE- The number of involvement in missing link smells can
FACE4SMELLS outputs a missing link instance as a pair be obtained from the list of missing link instances of a
of developers. So, the smelly developers can be obtained project. First, the developers are extracted from all the
by extracting all missing link instances of a project. The missing link instances of the project. Then the number
smelly developers of a project 𝑥 can be denoted by a set of involvement is calculated counting how many times a
𝑆𝐷𝑥 . The number of smelly developers of the project will developer occurs in the list.
be the number of elements in 𝑆𝐷𝑥 . Both the number of commits and the number of in-
volvement in smells of a developer are converted into

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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

Table 2
Correlation coefficient interpretation

Correlation Coefficient (Negative) Correlation Coefficient (Positive) Interpretation
-0.4 < 𝜏𝑏 ≤ 0.0 0.0 ≤ 𝜏𝑏 < 0.4 Weak
-0.7 < 𝜏𝑏 ≤ -0.4 0.4 ≤ 𝜏𝑏 < 0.7 Moderate
-0.9 < 𝜏𝑏 ≤ -0.7 0.7 ≤ 𝜏𝑏 < 0.9 Strong
-1.0 ≤ 𝜏𝑏 ≤ -0.9 0.9 ≤ 𝜏𝑏 ≤ 1.0 Very Strong

percentage to achieve the relative measurement. The 5.1. RQ1: How many developers are
commit percentage of a developer is calculated using involved in missing link community
Equation 2. smell?
To answer this RQ, all missing link smells of a project are
𝑛𝑢𝑚𝐶𝑜𝑚𝑚𝑖𝑡𝑖
𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝐶𝑜𝑚𝑚𝑖𝑡 = 𝑛 × 100% (2) considered. For every project, the number of total devel-
∑𝑖=1 𝑛𝑢𝑚𝐶𝑜𝑚𝑚𝑖𝑡𝑖 opers and the number of smelly developers are calculated.
Then the percentage of smelly developers is obtained for
where 𝑛𝑢𝑚𝐶𝑜𝑚𝑚𝑖𝑡𝑖 is the number of commits of devel-
each project.
oper 𝑖 and 𝑛 is the total number of smelly developers.
Table 3 demonstrates the percentage of smelly devel-
Equation 3 is used to calculate missing link smell in-
opers for each project. For example, Apache Cassandra
volvement of a developer in percentage.
project has 1380 total developers and 205 smelly devel-
opers which is 14.9% of total developers. It is observed
𝑛𝑢𝑚𝑀𝑖𝑠𝑠𝑖𝑛𝑔𝐿𝑖𝑛𝑘𝑖 that on average 10.5% of total developers of a software
𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑀𝑖𝑠𝑠𝑖𝑛𝑔𝐿𝑖𝑛𝑘 = 𝑛 ×100% (3) community are involved in missing link smells. Apache
∑𝑖=1 𝑛𝑢𝑚𝑀𝑖𝑠𝑠𝑖𝑛𝑔𝐿𝑖𝑛𝑘𝑖
Cayenne community has the highest percentage of smelly
where 𝑛𝑢𝑚𝑀𝑖𝑠𝑠𝑖𝑛𝑔𝐿𝑖𝑛𝑘𝑖 is the number of involvement developers (21.1%). This is also the smallest community
in missing link smells of developer 𝑖 and 𝑛 is the total among 7 communities. Tamburri et. al. found that the
number of smelly developers. number of community smell grows quadratically with
Finally, the correlation analysis is performed between the number of community members until the threshold
𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝐶𝑜𝑚𝑚𝑖𝑡 and 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑀𝑖𝑠𝑠𝑖𝑛𝑔𝐿𝑖𝑛𝑘 for each project of 200 community members [4]. The occurrences of com-
individually. Kendall’s tau-b [10] is used to assess the munity smell tend to stabilize after this threshold. As the
degree of association between these two variables. Both number of total developers in Apache Cayenne commu-
𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝐶𝑜𝑚𝑚𝑖𝑡 and 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑀𝑖𝑠𝑠𝑖𝑛𝑔𝐿𝑖𝑛𝑘 have tied values nity is less than 200, the number of missing link smell
in the dataset. As Kendall’s tau-b can handle tied ranks, has not stabilized yet. So, this project has relatively more
this is used for the correlation analysis. The correla- missing link smell and consequently more smelly devel-
tion coefficient is considered significant if the p-value is opers. Excluding Apache Cayenne project, the rest six
less than 0.01. The correlation coefficient is interpreted projects have 8.7% smelly developers on average.
according to Table 2. The correlation coefficient, 𝜏𝑏 , in- These results suggest that only a small portion of de-
dicates the strength of the correlation. 𝜏𝑏 has a range velopers in an open-source software community are in-
of value from -1.0 to 1.0. As 𝜏𝑏 closes to 0, it indicates volved with missing link smells. They do not commu-
less correlation between two variables. As 𝜏𝑏 approaches nicate appropriately with their co-committing or collab-
to -1.0 or +1.0, the strength of correlation between two orative developers. Thus, they contribute to the total
variables is increased. The positive value of 𝜏𝑏 indicates a number of community smells in a software community.
positive correlation and the negative value of 𝜏𝑏 indicates
a negative correlation between two variables. 5.2. RQ2: How does missing link smell
relate with a developer contribution?
5. Result Analysis To answer this RQ, the correlation between a developer’s
contribution and involvement in missing link smell is an-
This section presents the result analysis and discussion
alyzed. Kendall’s tau-b is used as a correlation technique
of this study. All the missing link smells found in se-
since it can handle tied values.
lected projects are analysed to answer the two research
First, the correlation analysis is performed individually
questions. Analysis and discussion for both research
for each development community. The Kendall’s tau-b
questions are provided as follows.
coefficients and p-values are provided in Table 4. For

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8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

Table 3
Percentage of Smelly Developers

# Project Name Total Developers Smelly Developers Smelly Developers(%) Average
1 Apache Cassandra 1380 205 14.9%
2 Apache CXF 972 94 9.7%
3 Apache Jena 244 34 13.9%
8.7%
4 Apache Mahout 615 28 4.6%
5 Apache Pig 668 22 6.0%
6 Apache Jackrabbit 927 28 3.0%
7 Apache Cayenne 175 37 21.1%
Average 668 64 10.5%

Table 4 6. Threats to Validity
Correlation Analysis
This section discusses the potential threats that may af-
# Project Name Tau-b p-value fect the validity of this study.
1 Apache Cassandra 0.508 < 0.01 Threats to external validity: Threats to external
2 Apache Cayenne 0.543 < 0.01 validity concern the generalization of the obtained results.
3 Apache CXF 0.528 < 0.01 In this study, seven projects from Apache are analysed.
4 Apache Jackrabbit 0.589 < 0.01 Thus the generalisation requires more projects belonging
5 Apache Jena 0.452 < 0.01 to different systems. However, to mitigate this threat
6 Apache Mahout 0.409 < 0.01 large and diverse projects are selected that have a long
7 Apache Pig 0.513 < 0.01
change history - 11 years on average.
Overall 0.612 < 0.01 Threats to internal validity: Threats to internal va-
lidity concern the factors that can influence the result but
are not accounted for. In this study, CODEFACE4SMELLS
example, the correlation coefficient for Apache Cassan- tool is used for the detection of missing link smell. The
dra project is 0.508 and it represents a moderate positive outputs of CODEFACE4SMELLS are directly incorporated
correlation. The value of correlation coefficient is sig- in this study without checking whether there is any de-
nificant with a p-value less than 0.01. All seven projects fect in the tool. However, the capability of this tool of
of this study show a moderate positive correlation be- identifying missing link smell was evaluated in [3]. This
tween number of commits and number of smells which tool is also used in other studies in detecting community
is statistically significant with p<0.01. smells [2], [5], [11].
Another correlation analysis is performed after com- Moreover, this tool relies on mailing list to detect
bining the data from all the projects. The value of the communication among developers. But there may ex-
correlation coefficient is slightly increased to 0.612 but ist other communication channels, e.g., Skype, Facebook
still falls under the range of moderate positive correlation. etc., where developers communicate with each other. The
This result is also statistically significant with a p-value result can be changed if these communication source are
less than 0.01. considered. However, mailing list represents the main
These results suggest that a developer who contributes communication channel for the projects analysed in this
more in a project tends to have more missing link smells. study according to the contribution guidelines of these
This can happen because a developer, who contributes projects. Besides, mailing list is used as the communica-
more, have to communicate more with other develop- tion source in other related studies [4], [7].
ers. The overload of communication may be the reason
for involving in more missing link smells than others.
From another point of view, a developer having more 7. Conclusion
contribution to a project is likely to be more familiar and
This study explores the percentage of developers in a
experienced with that project. As he knows most of the
software development community involved in missing
aspects of that project, he may take the communication
link smells. Furthermore, the relationship between devel-
with co-committers lightly while contributing. However
oper contribution and involvement in missing link smell
further analysis is required to find out the causes of in-
is examined. At first, missing link smells are detected for
volving in more smells.
all the projects. Next, the smelly developers are identified

69
8th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2020)

by extracting missing link instances. The percentage of and Human Aspects of Software Engineering, IEEE,
smelly developers are calculated for every project. The 2013, pp. 93–96.
number of appearances of a developer in missing link [7] M. Joblin, W. Mauerer, S. Apel, J. Siegmund,
smell is counted. The contribution of a developer to a D. Riehle, From developer networks to verified
project is measured by the number of commits. Finally, communities: a fine-grained approach, in: 2015
correlation analysis is done between contribution and IEEE/ACM 37th IEEE International Conference on
involvement in smell. Software Engineering, volume 1, IEEE, 2015, pp.
This study analyses seven open-source projects of 563–573.
Apache. The result shows that the number of developers [8] F. Palomba, D. A. Tamburri, A. Serebrenik, A. Zaid-
involved in missing link smells is 8.7% on average. This man, F. A. Fontana, R. Oliveto, Poster: How do
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relation between the number of commits of a developer IEEE/ACM 40th International Conference on Soft-
and the number of involvement in missing link smells. ware Engineering: Companion, IEEE, 2018, pp.
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In future, projects from other systems can be analysed versity in global, distributed collectives: A study of
to assess the generalization of the result. Besides, other open source project success, Information Systems
types of community smell, e.g., organizational silo, radio Research 24 (2013) 312–333.
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in open-source: an automated approach (2018).

Acknowledgments
The virtual machine facility used in this research is pro-
vided by Bangladesh Research and Education Network
(BdREN).

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