# The Pot of Gold at the End of the Rainbow:
What is Success for Open Source Contributors?

Bianca Trinkenreich, Mariam Guizani, Igor Wiese, Tayana Conte, Marco Gerosa,
Anita Sarma, Igor Steinmacher Bianca Trinkenreich and Marco Gerosa are with
School of Informatics, Computing & Cyber Systems of the Northern Arizona
University, Flagstaff, AZ, USA.Mariam Guizani and Anita Sarma are with School
of Electrical Engineering and Computer Science of the Oregon State University,
Corvalis, OR, USA. Igor Wiese and Igor Steinmacher are with the Department of
Computer Science, Federal University of Technology – Paraná (UTFPR), Campo
Mourão, PR, Brazil.Tayana Conte is with the Institute of Computing (IComp) of
the Federal University of Amazonas (UFAM), Manaus, AM, Brazil.

###### Abstract

Success in Open Source Software (OSS) is often perceived as an exclusively
code-centric endeavor. This perception can exclude a variety of individuals
with a diverse set of skills and backgrounds, in turn helping create the
current diversity & inclusion imbalance in OSS. Because people’s perspectives
of success affect their personal, professional, and life choices, to be able
to support a diverse class of individuals, we must first understand what OSS
contributors consider successful. Thus far, research has used a uni-
dimensional, code-centric lens to define success. In this paper, we challenge
this status-quo and reveal the multi-faceted definition of success among OSS
contributors. We do so through interviews with 27 OSS contributors who are
recognized as successful in their communities, and a follow-up open survey
with 193 OSS contributors. Our study provides nuanced definitions of success
perceptions in OSS, which might help devise strategies to attract and retain a
diverse set of contributors, helping them attain their “pots of gold at the
end of the rainbow”.

###### Index Terms:

open source software, success, career, qualitative analysis

##  1 Introduction

Success in Open Source Software (OSS) encompasses much more than code
contributions alone; In the words of one of our participants: Success in OSS
is “contributing more than code, [and involves] contributing documentation,
processes …the governance of the project”. However, currently there is a
misperception that being successful in OSS is determined by programming skills
[1]. This perception is promoted in how projects highlight programming-related
metrics on their sites (e.g., number of lines of code, number of commits) and
in how they determine advancement in roles (e.g., becoming a core
member/maintainer, gaining commit access)—all code-centric concepts [2, 3].
Academic research has also, perhaps inadvertently, added to this
misperception, as past studies have largely been code-focused. An example is
the numerous papers recognizing the “onion model” as the mechanism through
which contributors join, grow, and receive commit access to the code
repository [4, 5, 6, 7].

OSS contributors, however, comprise an heterogeneous group, with differing
talents, skills, career goals, and motivations [8, 9, 10, 11]. Some perform a
variety of non-code related activities (e.g., advocacy, technical writing,
translation, project management) [1, 12] and follow a much different pathway
than the celebrated “onion model” [13, 14, 1]. Given the fact that OSS
communities involve many more players than simply their “code warriors”, the
definition of success must be broadened beyond the quantity of code one
produces.

The way we define success has a remarkable impact on the choices we make in
our personal and professional lives. Definitions of success can affect our
educational choices, decisions of where to work, project involvement, career
attainment, life satisfaction and so on [15]. Therefore, it is important to
create a comprehensive understanding of the multitude of factors that
underpins what success means to an individual in OSS. Without such an
understanding, how can OSS communities support diverse individuals whose
background, career goals, and pathways do not fit the typical onion model
career mold?

In this study, therefore, we tackle the fundamental question: RQ. What does it
mean to be successful in OSS?

To answer this question, we interviewed 27 OSS contributors who are recognized
as successful figures in their communities. We qualitatively analyzed the
interviews by using as a lens the “success model” proposed by Dries et al.
[16]. We then triangulated our results with data from a survey of 193 OSS
contributors.

Our results indicate that the definition of success is multi-faceted and
nuanced. It includes both objective perspectives (e.g., monetary
compensations, amounts of contribution) as well as subjective perceptions
(e.g., recognition in the community, satisfaction). And while the end goal of
contributors can be similar, how they go about being successful can be very
different for different people and is rooted in their perceptions of what
success means to them.

In the words of von Krogh [10]: “Occasionally, humans also make elaborate
detours, strive for bigger things in life, and undertake long voyages to find
the gold at the end of the rainbow.” Thus, it is time that we reflect on what
we consider as success in OSS, and how we can help make OSS diverse by finding
different ways to support diverse individuals with diverse backgrounds and
motivations and who have diverse definitions of success.

##  2 Defining Success

Success can be defined as “the accomplishment of desirable work-related
outcomes at any point in a person’s work experiences over time” [17] and is a
dynamic concept [18]. Existing literature shows that career success can be
characterized from different perspectives, such as: job, interpersonal,
financial stability, and life success [19]; balance, relationships,
recognition, and material success [15]; psychological, and social career
success [20]; and extrinsic and intrinsic success [21].

Dries et al. [16] created a comprehensive multidimensional theoretical model
to explain success, which encompasses several concepts from the other studies
[19, 22, 21, 15, 23, 20, 24, 25, 26, 27] (Figure1).

Figure 1: The multidimensional model of success [16]

This model comprises two dimensions, and further classified into four
quadrants, and ten regions. The first dimension is Affect $\times$
Achievement. Affect represents the subjective side, the feelings and
perceptions that cause people to weigh their success as high or low.
Achievement represents the objective side: the factual accomplishments through
which people measure their success.

The second dimension is Interpersonal $\times$ Intrapersonal. Interpersonal
represents actors’ relationships and interactions with the outside world.
Intrapersonal represents the actor’s “self”, their internal world. The
combination of these two dimensions (Affect $\times$ Achievement and
Interpersonal $\times$ Intrapersonal) generates four quadrants: (Quad1)
Interpersonal x Affect; (Quad2) Interpersonal $\times$ Achievement; (Quad3)
Intrapersonal $\times$ Achievement; (Quad4) Intrapersonal $\times$ Affect. The
multidimensional nature of this model shows how success can have several
different—yet complementary—meanings that may serve people with different
goals.

Akkermans and Kubasch [28] explain that careers have been changing in the past
decades–evolving into more complex and unpredictable endeavors—that require
empirical studies in different domains to understand success. We use the above
model to analyze the definition of success in OSS.

##  3 Research Design

This section presents the design of our study, which included interviews and a
survey, as depicted in Figure 2.

Figure 2: The research method, which included face to face interviews at the
OSCON’19 event and later with OSS maintainers through video conferencing, as
well as a large-scale survey. We conducted qualitative analysis to build the
OSS Success Model and qualitative and quantitative analysis to triangulate the
definition of success we found from the interviews’ data

###  3.1 Interviews: Building the OSS Success Model

Due to the complexity of the phenomenon under study, we started with in-depth
interviews to understand how OSS contributors perceive success.

####  3.1.1 Interviews Planning

For the interviews, we aimed to recruit recognized OSS contributors to
understand successful OSS career stories. We started by recruiting invited
speakers of the Open Source Software Conference (OSCON-2019), a well-
recognized open source conference focused on practitioners. These speakers
were invited to give talks in the main lineup of the conference and, arguably,
are successful in OSS.

Before OSCON started, We emailed and sent direct messages via Twitter to all
panelists whose contact information was publicly available (15). We also
approached some of them during the event. We conducted face-to-face interviews
with 11 people who agreed to be interviewed. In addition, we used a snowball
approach to recruit more interviewees. At the end of each interview we asked
them to introduce us to other qualified participants for the study. We
conducted 4 additional interviews from this snowballing approach. A majority
of our interviewees were women, possibly because they cared about our goal to
investigate diversity and inclusion in OSS. This initial imbalance was
counterbalanced in the subsequent interviews (see Table II).

After this first cycle of interviews and analysis, given the gender imbalance
in our dataset, we recruited 12 additional participants. We invited
maintainers of mature OSS projects who could share their perspectives of
contributing to OSS. In both phases of interviews (at OSCON and post event),
we used a snowballing approach: at the end of each interview, we asked
interviewees to introduce us to other qualified participants for the study,
aiming to reach other speakers and successful project maintainers. Our goal
was to interview experienced contributions working at least 5 years in well-
known and mature OSS projects. We compensated interviewees with a 25-dollar
gift card.

Before interviewing participants, we conducted five pilot interviews with a
professor and four PhD students who were experienced in OSS. The goal was to
solicit feedback on the script and ensure that the interview would fit in a
40- to 60-minute time slot. We analyzed the pilot interview responses to
ensure that we answered our research question with an adequate level of
detail.

TABLE I: Interview Script and Survey Questions (excluding demographic
questions) Interview Script
---
| I-1. Can you please tell me all the story of your career? All your
professional journey, from the beginning of your career until where you are
today
---
I-2. How would you define yourself as being a successful OSS contributor?
| I-3. Is there any kind of success that you didn’t achieve yet? What do you
plan to achieve in the future?
---
| I-4. Please think about a person you consider successful in OSS. Why do you
think this person is successful?
---
| I-5. Now the opposite. Please think about a person you consider not
successful in OSS. Why do you think this person is not successful?
---
Survey Questions
S-1. Do you consider yourself a successful OSS contributor? (Yes/No/I don’t
Know)
S-2. How would you define a successful person in F/OSS? (Open Question)

We conducted semi-structured interviews [29]. We used a script as we present
in Table I to guide the different areas of inquiry, while also listening for
unanticipated information during the flow of the conversation. The interviews
revolved around the central question: “How would you define being successful
in Open Source?” We approached this topic after establishing rapport with the
interviewee, asking about their career story and contributions.

We interviewed participants until we could not find any new concept related to
our research question for five consecutive interviews. Our final sample
comprised 27 participants. Table II presents their demographics.

TABLE II: Demographics for the Interview Participants Participant ID | Gender | Years in OSS | Main Type of Contribution | Recruitment | Interview Mode
---|---|---|---|---|---
P1 | Woman | 6 | OSS Advocate | OSCON Speaker | In-person
P2 | Woman | 5 | OSS Coder | Mature project | Video-conference
P3 | Woman | 13 | OSS Treasurer | OSCON Speaker | In-person
P4 | Man | 9 | OSS System Admin | OSCON Speaker | In-person
P5 | Prefer not to say | 7 | OSS Coder | OSCON Speaker | In-person
P6 | Man | 5 | OSS Coder | OSCON Speaker | In-person
P7 | Man | 12 | OSS Coder | OSCON Speaker | In-person
P8 | Woman | 30 | OSS Strategist | OSCON Speaker | In-person
P9 | Man | 13 | OSS Advocate | OSCON Speaker | In-person
P10 | Woman | 20 | OSS Advocate | OSCON Speaker | In-person
P11 | Woman | 20 | OSS Writer | Snowballing | Video-conference
P12 | Woman | 20 | OSS Advocate and Writer | OSCON Speaker | In-person
P13 | Woman | 7 | OSS Advocate | Mature project | Video-conference
P14 | Woman | 20 | OSS License Manager | Mature project | Video-conference
P15 | Woman | 15 | OSS Advocate | OSCON Speaker | In-person
P16 | Woman | 10 | OSS Advocate | Snowballing | Video conference
P17 | Woman | 5 | OSS Project Manager | Snowballing | Video conference
P18 | Man | 8 | OSS Coder | Mature project | Video-conference
P19 | Man | 8 | OSS Coder | Mature project | Video-conference
P20 | Man | 5 | OSS Coder | Mature project | Video-conference
P21 | Man | 15 | OSS Coder | Mature project | Video-conference
P22 | Man | 10 | OSS Advocate | Mature project | Video-conference
P23 | Man | 7 | OSS Coder | Snowballing | Video-conference
P24 | Man | 20 | OSS Coder | Mature project | Video-conference
P25 | Man | 23 | OSS Coder | Mature project | Video-conference
P26 | Prefer not to say | 10 | OSS Project Manager | Mature project | Videoconference
P27 | Woman | 10 | OSS Coder | Mature project | Video-conference

####  3.1.2 Data Collection

Five researchers participated in performing the interviews, where there were
at least 2 researcher per interview. The researchers have at least six years’
experience in qualitative studies. The interviews were face-to-face during
OSCON and over video conference calls afterward. Interviews lasted between 40
and 60 minutes. With participant consent, we recorded all interviews. The
first author of this paper transcribed the interviews for the analysis.

Our sample comprises paid and volunteer contributors across 20 different OSS
projects (e.g., Kubernetes, Drupal, R, Noosfero, Fedora, Debian, GitLab),
which vary in terms of number of contributors (30 to 3,000 contributors),
product domains (including infrastructure and user-application projects), and
types (backed by foundations, communities, and companies). Table II presents
the demographics of our sample. Because of the terms of consent, we cannot
link each participant to their projects.

####  3.1.3 Data Analysis

The data analysis was performed in two stages. In the first stage we analyzed
the interview data collected at OSCON 2019 and in the second stage we analyzed
the data from the additional interviews. We qualitatively analyzed the
transcripts of the interviews by inductively applying open coding in groups,
where we identified the definition of success that each participant provided.
We built post-formed codes as the analysis progressed and associated them with
respective parts of the transcribed text, so as to code the success
definitions according to the participants’ perspectives, who were identified
as P1 to P27.

Researchers met weekly to discuss the coding. We discussed the codes and
categorization until reaching consensus about the meaning of and relationships
among the codes. The outcome was a set of higher-level categories as cataloged
in our codebook 111https://figshare.com/s/39491da83e398612dffa.

To organize our categories according to Dries et al.’s model [16] (see Section
2), three of the authors conducted multiple card sorting sessions [30],
arranging the codes according to the dimensions and regions of the model. More
specifically, for each category, we analyzed the definitions of each region of
the model and discussed until we reached consensus on how to classify the
category. The analysis used the process of continuous comparison [31].

###  3.2 Survey: Data Triangulation

Next, we conducted an online survey to triangulate the interview results by
gathering data from a different perspective [32] and a larger sample.

####  3.2.1 Survey Planning

In the survey, we asked two key questions about participants’ perceptions of
success (see Table I), and additional demographic-related questions, including
the relationship with OSS (paid/unpaid), types of contributions, gender
identity, country of residences, and age. The target population included any
person who contribute to OSS.

We advertised the survey on social media and community blogs (e.g., Linkedin,
Twitter, Facebook, Reddit, Hackernews, CHAOSS blog, and others). To reach a
broader audience, we paid to promote our posts on Twitter, Facebook, and
Reddit. We also sent direct messages to OSS contributors and discussion lists.

####  3.2.2 Data Collection

The survey was available between June 4th and July 3rd, 2020. We received 217
non-blank answers. We inspected these answers and removed 25 that did not
provide a relevant answer (e.g., “I don’t know”, “I don’t want to answer”).
The final analysis included 193 answers.

We asked participants their three main types of contributions and classified
participants as “coder” if they selected ”code developer” or “code reviewer”
as one of the three main types of contributions. We classified as non-coders
those who selected only a subset of these options: translation, documentation,
mentorship, user support, community building, bug triaging, event
presentations, advocacy and evangelism, creative work and design, and project
management. We present the demographics of the survey participants in Table
III.

TABLE III: Demographics for the Survey Respondents

Type of Contribution | # | % |  | Country of Residence | # | %
---|---|---|---|---|---|---
Coder | 163 | 84.46% |  | Germany | 89 | 46.11%
Non-Coder | 30 | 15.54% |  | USA | 59 | 30.57%
|  | Netherlands | 12 | 6.22%
Gender | # | % |  | Brazil | 9 | 4.66%
Men | 165 | 85.49% |  | Others | 23 | 11.92%
Women | 16 | 8.29% |  | Did not answer | 1 | 0.52%
Non-Binary | 2 | 1.04% |  |
Did not answer | 10 | 5.18% |  | Age | # | %
|  | 24 or less | 30 | 15.54%
Financial Relation | # | % |  | 25-34 | 60 | 31.09%
Paid | 36 | 18.65% |  | 35-44 | 59 | 30.57%
Unpaid | 137 | 70.98% |  | 45-54 | 27 | 13.99%
Partially Paid | 16 | 8.29% |  | 55 or more | 10 | 5.18%
Did not answer | 4 | 2.07% |  | Did not answer | 7 | 3.63%
|  |  |  |  |  |
Do you consider yourself a successful OSS contributor? | # | %
Yes | 72 | 37.31%ˆ
No | 80 | 41.45%
I’m not sure | 41 | 21.24%

####  3.2.3 Data Analysis

We qualitatively analyzed the answers to the open-ended questions using the
existing codebook from the interview stage. We use descriptive statistics to
summarize the survey responses, their association with each other (success
constructs) and the demographics data [33]. We use the chi-square test at
$\alpha=.05$ and Yates and Bonferroni corrections to investigate any
statistical relationship between the different analysis constructs.

See supplemental for additional details of our analysis, including sample
answers to the demographics and open question survey questions, the
qualitative analysis codes, as well as details of the chi-square tests.

##  4 Results

In the following, we present our participants’ definitions of success.

###  4.1 Understanding Success in OSS

Our analysis of the interviews (see Section 3.1.3) revealed 26 categories that
explain how our participants defined success. We organized these categories
using the multidimensional model of success proposed by Dries et al. [16] (see
Section 2), as can be seen in Figure 3. The 26 categories covered all ten
regions of the model. Table IV presents the number of participants (interviews
and survey) whose responses fit in each region. The survey analysis did not
provide any new definitions of success. In the following, we present our
findings organized by quadrant.

Figure 3: OSS Success Model. We mapped our participants’ definitions (shown
outside the bold square) to Dries et al.’s model [16], which organize success
in four quadrants. TABLE IV: Success meanings from the interviews and the
survey classified per Dries et al.’s model [16]

|  | # Interviews | # Survey
---|---|---|---
| Region | Participants’ IDs (Interviews) | (total: 27) | (total: 193)
| Participants who mentioned at least one Region in Inter-personal | 26 | 162 (84%)
Quad1 | Cooperation | P2, P3, P4, P5, P7, P8, P13, P15, P17, P20, P21 | 11 | 19 (8%)
Perceived Contribution | P5, P6, P9, P11, P17, P18, P21, P22, P25 | 9 | 57 (30%)
Recognition | P1, P9, P13, P22, P23, P25 | 6 | 29 (12%)
Participants who mentioned at least one Region in Q1 | 19 | 93 (48%)
Quad2 | Advancement | P1, P10, P12, P16, P20, P21, P22, P23, P24 | 8 | 8 (4%)
Performance | P4 | 1 | 0 (0%)
Factual Contribution | P2, P6, P12, P14, P18, P19, P20, P21 | 8 | 74 (38%)
Participants who mentioned at least one Region in Q2 | 15 | 81 (42%)
| Participants who mentioned at least one Region in Intra-personal | 11 | 49 (25%)
Quad3 | Self-Development | P7, P16, P18, P19, P20, P21, P24 | 7 | 19 (8%)
Creativity | P10 | 1 | 2 (1%)
Participants who mentioned at least one Region in Q3 | 8 | 21 (11%)
Quad4 | Satisfaction | P1, P5, P10, P16, P21 | 5 | 13 (5%)
Security | P19, P24 | 2 | 17 (7%)
Participants who mentioned at least one Region in Q4 | 7 | 30 (16%)

Quad1: Interpersonal x Affect; Quad2: Interpersonal x Achievement; Quad3:
Intrapersonal x Achievement; Quad4: Intrapersonal x Affect
---
The total per quadrant is not the sum of the regions since the participants
often provided an answer that was categorized into more than one region.

####  4.1.1 Quad1: Interpersonal $\times$ Affect

The first quadrant in Dries et al.’s model [16] is defined by two dimensions:
(1) Interpersonal, which represents an individual’s relationships with the
outside world and (2) Affect, which represents internal feelings and
perceptions that characterize success. This quadrant contains three distinct
regions of meaning: Cooperation, Perceived Contribution, and Recognition.

Cooperation (Figure 3.I(a)) is defined as working with others (peers,
superiors, subordinates, clients, etc.). The collaborative nature of OSS
relates to this region as OSS contributors work together, support their
community, and help their peers. In our analysis, we identified five
categories which we explain next.

Success included building social capital, i.e., “having contacts in several
communities” as it allows identifying sources of help quickly when necessary
(P8, P17, P21). It also includes being able to contribute to “community
sustainability”, so it can be “as great as it can possibly be” (P3) and “more
diverse and more inclusive” (P13). “Bringing people together” (P8) to increase
the community’s inclusivity was also repeatedly mentioned as a factor of
success. Participants often mentioned individual success as part of the
community’s success: “having a healthy community is probably the most
important thing” (P4) and “the sign of a healthy open source project is where
everybody feels like their voice is heard and their opinion matters” (P7).

The cooperation aspect of OSS was also highlighted when participants defined
their success as the ability to support others’ success by “providing
opportunities for contributors to grow” (P7) and “become more present and
productive” (P15) by “giving everybody the opportunity [to climb] the
contributor ladder” (P7).

Participants also cited success as being a mentor who is “friendly, didactic,
and receptive to increase contributions” (P2 and P20), “who [neither] burn
themselves out, [nor act as] the hero in the situation” (P15). An OSS mentor
plays a crucial role in collaborative communities and influences the degree to
which a newcomer relates to an OSS community and identifies with it [12].
Indeed, our participants mentioned that newcomers need to “feel they are
heard” (P3), and that successful mentors develop the team by “let[ting] people
participate” (P4) and “being open to new ideas, whether that could be coding,
helping to figure out what the roadmap is, identifying features, identifying
bugs, kind of all those things coming together” (P4).

Perceived Contribution (Figure-3.I(b)), according to Dries et al. [16],
equates with serving society. In the context of OSS success, our participants
mentioned perceived contribution from the perspective of outreach—i.e.,
“impact on people in the world” (P11). Participants considered themselves as
successful when the product they contribute to has “high adoption” (P9),
“produce[s] value for the people” (P17), “leave[s] a legacy” (P25), and makes
people’s lives easier” (P5).

Recognition (Figure-3.I(c))—or being adequately rewarded and appreciated for
one’s efforts or talents [16]—was also mentioned by our participants. P13, for
example, defined success as “being recognized by the community and the
project’s stakeholders.” P1 considered recognition as awareness that “the
maintainer of these projects know that they can come to [participate] as a
subject matter expert” (P1).

####  4.1.2 Quad2: Interpersonal $\times$ Achievement

As per Dries et al. [16], this quadrant includes accomplishments external to
the actor’s “self”; and contains three distinct regions: Advancement,
Performance, and Factual Contribution.

Advancement (Figure-3.II(a)) is defined as progressing and growing in terms of
level and experience. In the OSS context, this relates to influencing
decisions about the product, being [part of] an “influential community that is
well recognized” (P21), receiving job offers, “writing the[ir] own ticket” in
their career (P12), receiving a salary increase, or achieving a top-level
position. “Money”, in some cases, was mentioned as representing growth (e.g.,
“salary going up” (P16)), which differs from some other cases in which money
represented a way of earning a living from OSS, which we classify as Security.

The Performance (Figure 3.I(b)) region is defined as attaining verifiable
results and meeting set goals [16]. In our context, this translated to having
a plan for project releases “depending on what the goals of the project are,
such as working on a new release every six months” (P4). Project planning
activities show the relation of the actor to the external world (interpersonal
dimension), as explained by P4: “if [one is] not making [the release], [they
are] letting a lot of people down”.

Factual contribution (Figure-3.II(c)) is about individually contributing to
the collective [16]. An indication of success in this region includes bringing
contributions to an OSS project, by “getting a change that you wrote,
accepted” (P12), including “a code change, a documentation change… [or
otherwise] getting something you made merged” (P12). Besides code
contributions, interviewees mentioned other factual contributions pertaining
to implementing ideas, changes, or any type of revisions or contributions to
the project, as well as “actively reviewing and looking at what people are
suggesting” (P2). Contributions can also represent something tangible, as
having financial gains when “selling the platform” (P6) or when having a
“ventured organization” (P6).

####  4.1.3 Quad3: Intrapersonal $\times$ Achievement

Dries et al. [16] describe this quadrant as including real accomplishments of
the actor’s “self”. It contains two distinct regions of meaning: Self-
Development and Creativity.

Self-Development (Figure-3.III(a)) is defined as realizing one’s potential
through self-management of challenges and learning experiences [16]. This has
been a classical motivation for contributing to OSS [9, 8, 34]. However,
success definitions cited by our participants go beyond “learning new skills”
(P16). The also include serving as a key project member by “being a mature
reviewer and contributor” (P2) “capable of effecting change in an open source
project, from the small to the large” (P7).

Creativity (Figure-3.III(b)) is about making something innovative and
extraordinary [16]. We found this to mean the freedom to “create new
knowledge” (P3), but also as the possibility of “propagating ideas” (P3).
Creativity is relevant to the OSS context as individuals from creative
communities have greater opportunities to express themselves and experience a
sense of accomplishment [34].

####  4.1.4 Quad4: Intrapersonal $\times$ Affect

The Intrapersonal $\times$ Affect quadrant includes feelings and perceptions
that characterize the career of an actor’s “self” [16], which contains two
regions: Satisfaction and Security.

Satisfaction (Figure-3.IV(a)) is about achieving happiness and personal
satisfaction, either in the family or in the work domain [16]. Participants
mentioned satisfaction as “being happy” (P1, P16, P26), which also included
“being able to express yourself” (P10). They talked about their sense of
belonging and “need for emotional inclusion” (P16), “participating in the
world that is being created” (P10), and having “a ton of friends and people
[who they] would hang out with or chat with, about non technical stuff” (P5).

Security (Figure-3.IV(b)) means meeting one’s financial or employment needs
[16]. Participants mentioned success as being able to make a living from OSS—
to “receive money as an OSS developer” (P24) and “prioritize what
[financially] sustains you” (P19).

Success is a multifaceted and complex concept, including both objective
metrics and subjective perceptions of accomplishments. Despite its vastly
different philosophy of contribution, OSS can provide similar avenues for
success as described by Dries et al.’s model.

###  4.2 Survey analysis

As explained in Section 3.2, we conducted a survey to triangulate the
definitions of success we identified from the interviews, expanding our
population and checking if we could find any new definition of success. We
qualitatively analyzed the 193 answers to our survey open question. Similar to
interviews, the participants often provided multiple definitions, which could
be categorized into more than one region from Dries’ model [16].

The survey analysis did not reveal any new categories. Thus, the 26 codes from
our interviews were sufficient to encompass the definition of success from the
survey respondents.

In this section, we look deeper into the survey results to understand the
prevailing definitions of success among our respondents and across different
demographics. When presenting the results, we use supplementary and
corroborative counting of the survey responses to triangulate qualitative
analysis of the definitions of success [35].

The dimensions of success. The majority of respondents defined success as the
relationships with the external world (Interpersonal) rather than within the
actor’s self (Intrapersonal), accounting for 84% vs. 25% of respondents. For
the Interpersonal dimension, respondents identified success across both ends
of the Affect and Achievement spectrum—25% were related to the Affect
dimension and 49% were related to Achievement. When considering definitions
related to Intrapersonal, none of the regions were mentioned by more than 10%
of the respondents. This preponderance of definitions related to the
Interpersonal side could be because of the collaborative nature of peer-
production sites such as OSS, where contributing to a common good and being
recognized for it have been cited as key motivation factors [10, 36, 9, 8,
37].

In fact, Factual (38%) and Perceived Contribution (30%) were the most
mentioned regions, followed by Recognition (12%). None of the other regions
across all quadrants had more than 10% responses. These responses reflect that
in OSS, while making contributions matters, the way that others (community,
peers, society) value the contributions is also an important indicator of
success.

Respondents who identified Factual Contribution as a definition of success
emphasized that the number, size, and frequency of contributions can be
objective concepts to quantify a significant contribution to the community.
They defined success as “finding a way to sustainably contribute” (S25), or
being “someone who is able to regularly contribute” (S11) and “spending time
on the project often” (S68). By doing so, a successful contributor is the one
who provides “a wide spectrum contributions” (S6). Moreover, respondents
identified different types of contributions for contributors in different
project-centric or community-centric roles [1], as mentioned by S2:
“Successful contributors add or change major features, and organize the
community”.

Those who considered Perceived Contribution as their meaning of success
emphasized the importance of their contribution such as, publishing and
maintaining software that is used by and useful to a lot of people. According
to S136, the perceived value of their contribution could be measured by “how
many people have used the OSS code and how much value has it created”. Some of
these definitions of success in OSS included: “someone who publishes and
maintains software that is useful for a lot of people or for the user
community” (S3) and “when the software solves and helps real world problems”
(S169).

Finally, our respondents reflected many different perceptions of success
related to Recognition in their community; which included “having a high
number of stars on the own repository in GitHub” (S58 and S109), “receiving
donations” (S21), and ”being invited for conference invites/talks” (S16).

Demographics and the meaning of success As recent literature has shown, the
OSS community is becoming more diverse in terms of the gender of contributors,
types of contributions, and financial rewards [12, 1]. We took a deeper look
into these demographic subgroups with respect to their definitions of success.
Understanding how different demographics perceive success can help us create
mechanisms to better support diverse demographics, thereby helping improve the
state of diversity in OSS. Figure 4 illustrate the definition of success by
each demographic subgroup.

Figure 4: Subgroup analysis of the meanings of success. The icons’ opacity
represent the percentage of each group for the quadrant. Darker means higher
and lighter means lower percentage. Some respondents provided answers about
success that accounted for more than one quadrant.

From the 193 survey respondents, 165 identified as men, 16 as women, and 2 as
non-binary. The gender distribution of our respondents matches that of those
reported in other OSS studies ([38, 39, 40]). We dropped from this analysis
the 10 respondents who did not disclose their gender. Comparing the
distributions of definitions reported by men and women, we could not find
statistically significant differences between the two groups neither in terms
of quadrants, nor in terms of regions. As illustrated in Figure 4, both men
and women more frequently mentioned success definitions classified in the
Interpersonal quadrants (Quad1/Quad2) than those in Intrapersonal
(Quad3/Quad4).

Our survey included answers from 163 coders and 30 non-coders, i.e., those who
work only on non-code related activities (e.g., advocacy, license management,
technical writing). We could not find statistically significant differences
between the distribution of answers from the two subgroups. We could also not
find statistically significant differences when sub-grouping based on
compensation (paid vs. unpaid). The statistical test results including the
p-values of these comparisons are in the supplementary material.

The Interpersonal dimension plays a dominant role in the definition of
success, in which factual and perceived contributions are the most referenced,
followed by recognition. Contributors across different demographic
groups—gender, contribution type (code vs non-code), and compensation (paid
vs. non-paid)—report similar perception of success.

##  5 Discussion

###  5.1 Success is multifaceted and hard to measure

Success in OSS is a complex concept with multiple dimensions. Our participants
reported different definitions for success, encompassing all the regions of
the Dries et al.’s model [16]. Even a single person’s understanding can span
multiple dimensions. Therefore, the current view of successful OSS
contributors being code “hackers” [34] is inadequate, even from the point of
view of coders. Success in OSS is a nuanced, multifaceted concept that goes
well beyond becoming a core member or a maintainer.

This variety of perspectives makes it challenging to measure success. Even
common terms, such as “contribution,” can be understood from different
perspectives. While some people consider achieving a high number of
contributions or becoming a frequent contributor as a measure of success,
others relate success to the impact of their contribution—or how it is
perceived by their users, community, or society. Current literature, tools,
and project infrastructures, unfortunately, tend to focus on the measurement
of code-centric contributions (e.g., [37, 41, 42]). However, there are
subjective perspectives of success closer to the Affect dimension that also
need to be measured. For example, how does one measure contributions for those
who mentor or work to make their communities inclusive (Quad1). The benefits
of these types of contributions are intangible and by its very nature
difficult to measure. In fact, our results show even tangible products, such
as money, can itself represent multiple meanings of success; Security when
related to making a living from OSS, and Advancement, when related to career
growth (salary increase). Therefore, it is important that researchers and
practitioners take a more nuanced approach in developing ways to evaluate
success, considering the multitude of profiles and activities that are now
part of OSS. There is no “one size fits all” measure of success.

###  5.2 Coders & non-coders look for the same pot of gold

While coders and non-coders may contribute differently to OSS, they perceive
success in similar ways. Our analyses (Figure 4) show that both coders and
non-coders often mentioned definitions that relate to the Interpersonal
dimension.

Despite this commonality in the definitions of success between coders and non-
coders, prior research has shown that they perform different roles and have
different career pathways in OSS [12, 1]. These pathways may include not only
code-centric, but also community-centric activities, including advocacy,
community building, mentorship, and technical writing. These activities are
important for projects’ sustainability and growth, but are currently not well-
recognized.

Therefore, showing individuals that there are multiple ways to achieve success
is important, regardless of their roles. To do so, current strategies and
metrics to support contributors need to be adapted to consider the multitude
of success definitions not only for code-related activities, but also for
activities not directly related to code. For example, coders gain recognition
from having their names in a “credits” file or badges in their profiles, but
non-coders are commonly overlooked because their activities are harder to
quantify [43]. Identifying ways of showing Recognition for non-coders is
important too, not only to help them get “credit” for their work, but also to
reflect their expertise.

###  5.3 Subjective definitions of success is prevalent

In our study, both men and women mentioned success definitions related to the
Affect and Achievement dimensions at similar rates. Contrary to research in
other domains [15, 44, 45] which found that men relate success to tangible and
objective outcomes, the men in our study often provided meanings of success
related to the subjective side.

We hypothesize that the nature of OSS defines how people see success in this
context. OSS is an open collaboration community [46], in which collective work
is central to the success of projects. Additionally, altruism, reciprocity
(giving back), and maintaining high-quality social bonds are common
motivations to contribute to OSS [10, 36]. These motivations relate to working
together to create better software and for the greater good. Therefore,
individuals who are attracted to an open-collaboration community may attribute
a high value to these dimensions.

###  5.4 Implications

In addition to the implications mentioned as part of the discussion, we
highlight another set of ways that our results may inspire researchers and
communities.

The definition of success presented here can help OSS communities and
organizations better support contributors on achieving the success for which
they aim [47]. By understanding how contributors perceive success (and what
they seek), leaders can cultivate practices that highlight the success
attributes within their teams. This may guide OSS projects that face a high
turnover rate in designing retention initiatives.

For novice professionals and those joining their career in software
development, the results show that OSS offers different “pots of gold.” By
shedding light on what success means in OSS, we may show that OSS is
attractive and that it is possible for them to achieve their long-term goals.

Individuals with diverse backgrounds and definitions of success may need
different engagement strategies. By recognizing that success is multifaceted
and multiple success definitions apply to OSS, communities can leverage our
(sub-)categories to support the growth of individuals with different success
definitions. For example, one strategy that communities employ is organize
meetups with different communities to increase contributors’ social capital;
“Having contacts in several different communities”( Fig.3 I(a)) was frequently
mentioned as a success factor). Another strategy could employing different
recognition programs that value different types of contributions [1], aiming
at those who perceive success as “being recognized”(Fig.3 I(c)).

The multitude and nuances in the definitions of success can serve as input to
different research topics that have been investigated in the context of OSS.
It is important to find ways to support the growth of people whose background
is not related to software development. Their activities are harder to
quantify, given that they usually do not leave traces on project repositories.
Thus, this may pose challenges that go beyond just proposing metrics, and
toward proposing changes on how these activities are performed, logged, and
weighted. This may have additional impact on topics like mining and creating
virtual resumes for hiring purposes [48], recommending mentors [49], and
providing paths to becoming central to the project [50, 6].

##  6 Related Work

We could not find any software engineering research investigating the meaning
of success for OSS contributors, but there are several studies about being a
“great” software engineer and a “great” manager. Li et.al [51] conducted a
study to reveal the characteristics that distinguish “great” software
engineers. Authors found that the top five characteristics are writing good
code, regulating behaviors to account for future value and costs, exercising
informed decision-making, avoiding making colleagues’ work harder, and
constantly learning. Kalliamvakou et. al [52] investigated the attributes of a
“great” manager. According to their study, some level of technical skills is
necessary, but not as relevant as management skills to guide engineers to make
decisions, to motivate them, and to mediate their presence in the
organization. The focus of both studies is not on what means to be a
successful software engineer or manager—“greatness”—, but the competences and
skills to be “great.”

Kimmelmann [47] investigated the technical, social, and personal competencies
developers need according to their stage in OSS projects. The author argues
that these competences can support or hinder a successful career by regulating
professional behavior. The author investigated the competences and skills
needed to advance in an OSS career, but not what it means to be successful.

Some researchers investigated success from the point of view of the OSS
projects. Crowston et al. [53] analyzed how to assess the success of OSS
projects, based on a literature review and the opinion of SlashDot OSS
developers. Subramaniam et al. [54] also assessed the success of OSS projects
by using longitudinal data on OSS projects and examining the relationship
between OSS project success and OSS project characteristics. Midha et al. [55]
selected two measures of OSS project success, project popularity and developer
activity, to apply and investigate over 283 OSS projects over time. Sen et al.
[56] also presented a study about the definition and metrics to evaluate the
success of OSS projects.

Some research considers core developers as “elite contributors” [7] or code
heroes [6], who receive commit rights based on trust [57]. Although code
heroes are valuable for OSS projects [6], being a core developer is not the
only way to be successful. According to Zhou and Mockus [50], newcomers stay
longer and become Long Term Contributors (LTC) if they start with comments and
demonstrate a highly community-oriented attitude. While the theoretical
converging lens orients most OSS research efforts towards the project-centric
and technical side of OSS project development, our study aims to unveil other
perspectives of success beyond the traditional ways to measure success of OSS
contributor.

Finally, success perceptions and motivation behind contributing to OSS are
interrelated, complement each other, and can play different roles. Past work
has studied what motivates people to contribute to OSS (e.g., [9, 36, 10]).
However, when considering the comprehensive motivation model by Von Krogh et
al. [10], some motivation factors do not map to our model (e.g.,”Own-use”)
while some of our success definitions do not map to motivation factors (e.g.,
”Have a plan for project releases (Performance)”. On the other hand, there is
an intersection between definitions of success and motivation factors. For
example, money, ideology, and reputation can be found both in the OSS
motivation literature [10] and in our model of success perceptions. Our
results can be used in future work to investigate how OSS contributors with
different motivations perceive success.

##  7 Limitations

Internal validity. The characteristics of our sample may have influenced our
results. A great part of our interviewees (11 out of 27) were speakers at an
OSS conference and half (13 out of 27) of the interviewee identified as women,
even though we did not push toward having an equal gender split. This
diversity of profiles helped bring a more diverse perspective about the
phenomenon. Our survey, which received almost 200 answers, corroborated our
results. The distribution of our survey demographics is similar to the larger
OSS population as reported elsewhere [58, 39, 59, 38].

Construct validity. The main threat to construct validity in this work relates
to the question about success, which explicitly asks how the respondent
defines a successful person in OSS. While the question refers to the
individual’s perspective, respondents could interpret the question differently
and answer from the perspective of a typical contributor. This was not a
problem for the interviews, since we would have been able to clarify the
question (none of the interviewees misinterpreted this question). We believe
this threat to be minimal in the survey too based on our pilot studies.
Moreover, individuals’ perceptions about typical and prominent participants in
the OSS ecosystem are also relevant in creating a broad understanding of
success.

Survival bias. Our results reflect the opinion of current contributors, who
joined OSS and made it past the initial contribution barriers [60]. Therefore,
to promote diversity in OSS, we acknowledge that additional research is
necessary to understand success from the perspective of those who do not make
it past the initial barriers and those who are currently not attracted to OSS.

Recall bias. Moreover, as our survey question was open ended, our results
could be impacted by either salience bias, where respondents focus on
definitions that are prominent or emotionally striking and not necessarily all
the factors that matter; or by memory bias, where participants answered
questions based on what they can first recall and not necessarily what’s most
important to them.

Data Consistency. Consistency refers to ensuring that the results consistently
follow from the data and there is no inference that cannot be supported after
the data analysis [61]. To increase consistency, we performed data analysis in
a group of three researchers. We had weekly meetings to discuss and adjust
codes and categories until we reached agreement. In the meetings, we also
checked the consistency of our interpretations, continually discussing our
results based on definitions of Dries et al.’s model [16]. All analysis was
thoroughly grounded in the data collected and exhaustively discussed amongst
the whole team to reach an agreement. The team includes researchers with
extensive experience in qualitative methods.

Theoretical saturation. A potential limitation in qualitative studies regards
reaching theoretical saturation. In this study, we interviewed 27 participants
with different backgrounds and perceptions about the studied phenomenon. The
participants are diverse in terms of gender, number of years involved with
OSS, and highest achieved academic degree. We kept inviting participants until
we could not find any new concept for five consecutive interviews. Moreover,
we collected answers from 193 respondents about what it means to be a
successful OSS contributor, and we could not find any new meanings. Therefore,
although theoretical saturation cannot be claimed, we believe that we obtained
a consistent and comprehensive account of the phenomenon.

##  8 Conclusion

In this paper, we investigate how OSS contributors define success. OSS has
considerably changed over the last 20 years, from a generation of code-
oriented volunteers to an ecosystem in which industry consortia push OSS
projects forward with a significant amount of professional and paid
contributors [62]. Our results show that OSS contributors have a broader
perspective on success than the narrow focus on code-related activities—which
is better supported by current tools and practices.

Our study of 27 interviews with well-recognized OSS contributors and a follow-
up survey of 193 OSS contributors reveal a multi-faceted definition of
success. We found 26 categories of definitions through our interviews and
framed them through the theoretical lens of an existing success model by Dries
et al. [16].

Our analysis shows that success is a multi-faceted complex concept, including
both objective and subjective accomplishments. Even tangible items such as
“money” can mean different things to different people (e.g., a way to advance
in career or a way to secure a living). Despite its vastly different
philosophy of contribution, OSS actually provides similar avenues for success
as in more traditional career models. A key difference from other traditional
models however was the predominance of success definitions in the
interpersonal dimension. This was common across all respondents irrespective
of gender, role, or employment status in OSS.

In conclusion, we hope our work in revealing the nuanced definitions of
success that OSS contributors have can help us find out how to support diverse
individuals with diverse backgrounds. Let us work together to support the
different contribution pathways to help individuals reach that elusive pot of
gold at the end of the rainbow.

## Acknowledgments

We would like to thank all our interviewees and survey participants for their
time and insights. This work is partially supported by the National Science
Foundation under Grant numbers 1815486, 1815503, 1900903, and 1901031, CNPq
grant #313067/2020-1.

## References

  * [1] B. Trinkenreich, M. Guizani, I. Wiese, A. Sarma, and I. Steinmacher, “Hidden figures: Roles and pathways of successful oss contributors,” _Computer Supported Cooperative Work (CSCW)_ , vol. 4, no. 180, 2020.
  * [2] M. AlMarzouq, V. Grover, and J. B. Thatcher, “Taxing the development structure of open source communities: An information processing view,” _Decision Support Systems_ , vol. 80, pp. 27–41, 2015.
  * [3] J. Colazo, “Performance implications of stage-wise lead user participation in software development problem solving,” _Decision support systems_ , vol. 67, pp. 100–108, 2014.
  * [4] Y. Ye and K. Kishida, “Toward an understanding of the motivation open source software developers,” in _25th international conference on software engineering_. IEEE Computer Society, 2003, pp. 419–429.
  * [5] C. Jergensen, A. Sarma, and P. Wagstrom, “The onion patch: migration in open source ecosystems,” in _19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering_. ACM, 2011, pp. 70–80.
  * [6] A. Agrawal, A. Rahman, R. Krishna, A. Sobran, and T. Menzies, “We don’t need another hero,” in _40th International Conference on Software Engineering Software Engineering in Practice-ICSE-SEIP_ , vol. 18, 2018.
  * [7] Z. Wang, Y. Feng, Y. Wang, J. A. Jones, and D. Redmiles, “Unveiling elite developers’ activities in open source projects,” _Transactions on Software Engineering and Methodology (TOSEM)_ , vol. 29, no. 3, pp. 1–35, 2020\.
  * [8] S. O. Alexander Hars, “Working for free? motivations for participating in open-source projects,” _International journal of electronic commerce_ , vol. 6, no. 3, pp. 25–39, 2002.
  * [9] G. Hertel, S. Niedner, and S. Herrmann, “Motivation of software developers in open source projects: an internet-based survey of contributors to the linux kernel,” _Research policy_ , vol. 32, no. 7, pp. 1159–1177, 2003.
  * [10] G. Von Krogh, S. Haefliger, S. Spaeth, and M. W. Wallin, “Carrots and rainbows: Motivation and social practice in open source software development,” _MIS quarterly_ , pp. 649–676, 2012.
  * [11] R. A. Ghosh, “Understanding free software developers: Findings from the floss study,” _Perspectives on free and open source software_ , vol. 28, pp. 23–47, 2005.
  * [12] K. Carillo, S. Huff, and B. Chawner, “What makes a good contributor? understanding contributor behavior within large free/open source software projects–a socialization perspective,” _The Journal of Strategic Information Systems_ , vol. 26, no. 4, pp. 322–359, 2017.
  * [13] D. Nafus, “‘patches don’t have gender’: What is not open in open source software,” _New Media & Society_, vol. 14, no. 4, pp. 669–683, 2012.
  * [14] E. H. Trainer, C. Chaihirunkarn, A. Kalyanasundaram, and J. D. Herbsleb, “From personal tool to community resource: What’s the extra work and who will do it?” in _18th ACM Conference on Computer Supported Cooperative Work & Social Computing_, 2015, pp. 417–430.
  * [15] L. S. Dyke and S. A. Murphy, “How we define success: A qualitative study of what matters most to women and men,” _Sex Roles_ , vol. 55, no. 5-6, pp. 357–371, 2006.
  * [16] N. Dries, R. Pepermans, and O. Carlier, “Career success: Constructing a multidimensional model,” _Journal of Vocational Behavior_ , vol. 73, no. 2, pp. 254–267, 2008.
  * [17] M. B. Arthur, S. N. Khapova, and C. P. Wilderom, “Career success in a boundaryless career world,” _Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior_ , vol. 26, no. 2, pp. 177–202, 2005.
  * [18] M. L. Savickas, “The theory and practice of career construction,” _Career development and counseling: Putting theory and research to work_ , vol. 1, pp. 42–70, 2005.
  * [19] U. E. Gattiker and L. Larwood, “Subjective career success: A study of managers and support personnel,” _Journal of business and psychology_ , vol. 1, no. 2, pp. 78–94, 1986.
  * [20] E. Hennequin, “What “career success” means to blue-collar workers,” _Career development international_ , 2007.
  * [21] J. Sturges, “What it means to succeed: Personal conceptions of career success held by male and female managers at different ages,” _British journal of management_ , vol. 10, no. 3, pp. 239–252, 1999.
  * [22] E. H. Schein _et al._ , “Career anchors and job/role planning: The links between career pathing and career development,” 1990.
  * [23] M. D. Lee, P. Lirio, F. Karakas, S. M. MacDermid, M. L. Buck, and E. E. Kossek, “Exploring career and personal outcomes and the meaning of career success among part-time professionals in organizations,” _Research companion to work hours and work addiction_ , pp. 284–309, 2006.
  * [24] B. Parker and L. H. Chusmir, “Motivation needs and their relationship to life success,” _Human Relations_ , vol. 44, no. 12, pp. 1301–1312, 1991.
  * [25] G. R. Nabi, “The relationship between hrm, social support and subjective career success among men and women,” _International journal of manpower_ , 2001.
  * [26] D. T. Hall, _Careers in organizations_. Goodyear Pub. Co., 1976.
  * [27] ——, “Career dynamics: Matching individual and organizational needs; self-assessment and career development; teacher” s manual for self-assessment and career development,” _Journal of Applied Behavioral Science_ , vol. 15, no. 2, pp. 233–238, 1979.
  * [28] J. Akkermans and S. Kubasch, “# trending topics in careers: a review and future research agenda,” _Career Development International_ , 2017.
  * [29] C. B. Seaman, “Qualitative methods in empirical studies of software engineering,” _IEEE Transactions on software engineering_ , vol. 25, no. 4, pp. 557–572, 1999.
  * [30] D. Spencer, _Card sorting: Designing usable categories_. Rosenfeld Media, 2009.
  * [31] A. L. Strauss and J. M. Corbin, _Basics of qualitative research : techniques and procedures for developing grounded theory_. Thousand Oaks: Sage Publications, 1998.
  * [32] S. Easterbrook, J. Singer, M.-A. Storey, and D. Damian, “Selecting empirical methods for software engineering research,” in _Guide to advanced empirical software engineering_. Springer, 2008, pp. 285–311.
  * [33] C. Wohlin and A. Aurum, “Towards a decision-making structure for selecting a research design in empirical software engineering,” _Empirical Software Engineering_ , vol. 20, no. 6, pp. 1427–1455, 2015.
  * [34] K. R. Lakhani and R. G. Wolf, “Why hackers do what they do: Understanding motivation and effort in free/open source software projects,” MIT, Tech. Rep., 2003.
  * [35] D. R. Hannah and B. A. Lautsch, “Counting in qualitative research: Why to conduct it, when to avoid it, and when to closet it,” _Journal of Management Inquiry_ , vol. 20, no. 1, pp. 14–22, 2011.
  * [36] M. Gerosa, I. Wiese, B. Trinkenreich, G. Link, G. Robles, C. Treude, I. Steinmacher, and A. Sarma, “The shifting sands of motivation: Revisiting what drives contributors in open source,” in _Proceedings of the 43rd International Conference on Software Engineering (ICSE)_ , 2021.
  * [37] J. A. Roberts, I.-H. Hann, and S. A. Slaughter, “Understanding the motivations, participation, and performance of open source software developers: A longitudinal study of the apache projects,” _Management science_ , vol. 52, no. 7, pp. 984–999, 2006.
  * [38] B. Vasilescu, D. Posnett, B. Ray, M. G. van den Brand, A. Serebrenik, P. Devanbu, and V. Filkov, “Gender and tenure diversity in github teams,” in _33rd annual ACM conference on human factors in computing systems_ , 2015, pp. 3789–3798.
  * [39] G. Robles, L. A. Reina, J. M. González-Barahona, and S. D. Domínguez, “Women in free/libre/open source software: The situation in the 2010s,” in _IFIP International Conference on Open Source Systems_. Springer, 2016, pp. 163–173.
  * [40] V. Singh, “Women participation in open source software communities,” in _13th European Conference on Software Architecture-Volume 2_ , 2019, pp. 94–99.
  * [41] W. Huang, W. Mo, B. Shen, Y. Yang, and N. Li, “Cpdscorer: Modeling and evaluating developer programming ability across software communities.” in _International Conference on Software Engineering and Knowledge Engineering (SEKE)_ , 2016, pp. 87–92.
  * [42] G. R. Bergersen, D. I. Sjøberg, and T. Dybå, “Construction and validation of an instrument for measuring programming skill,” _IEEE Transactions on Software Engineering_ , vol. 40, no. 12, pp. 1163–1184, 2014.
  * [43] A. Barcomb, A. Kaufmann, D. Riehle, K.-J. Stol, and B. Fitzgerald, “Uncovering the periphery: A qualitative survey of episodic volunteering in free/libre and open source software communities,” _IEEE Transactions on Software Engineering_ , 2018.
  * [44] Y. Cho, J. Park, S. Jeoung, B. Ju, J. You, A. Ju, C. K. Park, H. Y. Park _et al._ , “How do south korean female executives’ definitions of career success differ from those of male executives?” _European Journal of Training and Development_ , 2017.
  * [45] A. M. Porter, “Physics phds ten years later: Success factors and barriers in career paths. results from the phd plus 10 study.” _AIP Statistical Research Center_ , 2019.
  * [46] A. Forte and C. Lampe, “Defining, understanding, and supporting open collaboration: Lessons from the literature,” _American behavioral scientist_ , vol. 57, no. 5, pp. 535–547, 2013.
  * [47] N. Kimmelmann, “Career in open source? relevant competencies for successful open source developers,” _it–Information Technology it–Information Technology_ , vol. 55, no. 5, pp. 204–212, 2013.
  * [48] A. Sarma, X. Chen, S. Kuttal, L. Dabbish, and Z. Wang, “Hiring in the global stage: Profiles of online contributions,” in _11th International Conference on Global Software Engineering (ICGSE)_. IEEE, 2016, pp. 1–10.
  * [49] G. Canfora, M. Di Penta, R. Oliveto, and S. Panichella, “Who is going to mentor newcomers in open source projects?” in _ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering_ , 2012, pp. 1–11.
  * [50] M. Zhou and A. Mockus, “What make long term contributors: Willingness and opportunity in oss community,” in _2012 34th International Conference on Software Engineering (ICSE)_. IEEE, 2012, pp. 518–528.
  * [51] P. L. Li, A. J. Ko, and A. Begel, “What distinguishes great software engineers?” _Empirical Software Engineering_ , vol. 25, no. 1, pp. 322–352, 2020.
  * [52] E. Kalliamvakou, C. Bird, T. Zimmermann, A. Begel, R. DeLine, and D. M. German, “What makes a great manager of software engineers?” _IEEE Transactions on Software Engineering_ , vol. 45, no. 1, pp. 87–106, 2017.
  * [53] K. Crowston, H. Annabi, and J. Howison, “Defining open source software project success,” _ICIS 2003 Proceedings_ , p. 28, 2003.
  * [54] C. Subramaniam, R. Sen, and M. L. Nelson, “Determinants of open source software project success: A longitudinal study,” _Decision Support Systems_ , vol. 46, no. 2, pp. 576–585, 2009.
  * [55] V. Midha and P. Palvia, “Factors affecting the success of open source software,” _Journal of Systems and Software_ , vol. 85, no. 4, pp. 895–905, 2012.
  * [56] R. Sen, S. S. Singh, and S. Borle, “Open source software success: Measures and analysis,” _Decision Support Systems_ , vol. 52, no. 2, pp. 364–372, 2012\.
  * [57] V. S. Sinha, S. Mani, and S. Sinha, “Entering the circle of trust: developer initiation as committers in open-source projects,” in _8th Working Conference on Mining Software Repositories_ , 2011, pp. 133–142.
  * [58] F. Zlotnick, “Github open source survey 2017,” http://opensourcesurvey.org/2017/, Jun. 2017.
  * [59] G. Robles, L. Arjona Reina, A. Serebrenik, B. Vasilescu, and J. M. González-Barahona, “Floss 2013: A survey dataset about free software contributors: Challenges for curating, sharing, and combining,” in _11th Working Conference on Mining Software Repositories_ , ser. MSR 2014, 2014, p. 396–399.
  * [60] I. Steinmacher, T. Conte, M. A. Gerosa, and D. Redmiles, “Social barriers faced by newcomers placing their first contribution in open source software projects,” in _18th ACM conference on Computer supported cooperative work & social computing_, 2015, pp. 1379–1392.
  * [61] W. A. Babchuk, “Book review: Qualitative research: A guide to design and implementation (4th ed.), by s. b. merriam and e. j. tisdell,” _Adult Education Quarterly_ , vol. 67, no. 1, pp. 71–73, 2017.
  * [62] G. Robles, I. Steinmacher, P. Adams, and C. Treude, “Twenty years of open source software: From skepticism to mainstream,” _IEEE Software_ , vol. 36, no. 6, pp. 12–15, 2019.

|  Bianca Trinkenreich is a PhD student at the Northern Arizona University.
She holds a Master of Science in Computer Science from the Federal University
of the State of Rio de Janeiro (UNIRIO) and has 20 years of experience in
industry as IT Project and Service Delivery manager. She researches Software
Engineering, CSCW, Software and Service Quality. Recent projects include the
career pathways and motivations of Open Source contributors.
---|---
|  Mariam Guizani is a PhD student in Computer Science at Oregon State
University. Her research area is in Human-Computer Interaction and Software
Engineering. More specifically her research focuses on improving diversity in
open source. She holds a Master of Science in Computer Science from Oregon
State University and was the recipient of the Fulbright scholarship in 2016,
2017, and 2018.
---|---
|  Igor Wiese is an Associate Professor in the Department of Computing at the
Federal University of Technology – Parana, Brazil, where he is interested in
Mining Software Repositories, Human Aspects of Software Engineering, and
related topics. Wiese received a PhD degree in computer science from the
University of São Paulo - Brazil. More information is available at
http://www.igorwiese.com.
---|---
|  Tayana Conte received her PhD degree in Systems Engineering and Computer
from the Federal University of Rio de Janeiro (UFRJ) in 2009. She is an
associate professor at the Institute of Computing (IComp) of Federal
University of Amazonas, heading the Usability and Software Engineering (USES)
lab. Her research interests include the intersection between Software
Engineering and Human-Computer Interaction, Software Quality, Human-Centered
Computing, and Empirical Software Engineering.
---|---
|  Marco Gerosa is an Associate Professor at the Northern Arizona University.
He holds a PhD in Computer Science from the Pontifical Catholic University of
Rio de Janeiro. He researches Software Engineering and CSCW. Recent projects
include the development of strategies to support newcomers onboarding to open
source communities and the design of chatbots for tourism. He published more
than 200 papers and served on the program committee (PC) of important
conferences, such as FSE, CSCW, SANER, and MSR.
---|---
|  Anita Sarma is an Associate Professor in the School of Electrical
Engineering and Computer Science, at Oregon State University. She holds a PhD
in Computer Science from the University of California, Irvine. Her research
interests lie primarily in the intersection of software engineering and human
computer interaction, focusing on understanding and supporting end users and
software developers become more effective and efficient. She has over 100
papers in journals and conferences. Her work has been recognized by an NSF
CAREER award as well as several best paper awards.
---|---
|  Igor Steinmacher is an Assistant Professor in the School of Informatics,
Computing, and Cyber Systems at the Northern Arizona University. He received a
PhD in computer science from the University of São Paulo. His topics of
interest include human aspects of software engineering, behavior in open
source software communities, mining software repositories, and software
engineering education & training.
---|---