Stress and burnout in open source

Raman, Naveen; Cao, Minxuan; Tsvetkov, Yulia; Kästner, Christian; Vasilescu, Bogdan

doi:10.1145/3377816.3381732

Cited by 40 publications

(16 citation statements)

References 20 publications

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“…We collected comments from four different SE communities. The datasets for GitHub, Gitter and Slack were obtained from prior work of Raman et al [30], Parra et al [29] and Chatterjee et al [6], respectively. We downloaded the publicly available SO data-dump of comments from the SO archive 6 .…”

Section: Methodsmentioning

confidence: 99%

“…Cheriyan et al [7] report norm violations in SO and provide manual analysis of comments to show that offence and unfriendliness also exist in SO [7]. Stress owing to toxicity in open source communities has been investigated by Raman et al [30]. The performance of different tools to detect toxicity has been investigated by Sarker et al [34].…”

Section: Related Work 21 Offensive Language Detectionmentioning

confidence: 99%

“…In summary, in SE communities, it has been shown that the use of offensive language creates friction among members and the victims may leave SE project teams by getting demotivated [34]. Moreover, offensive conversation may demotivate developers and waste valuable resources like working time [30]. Therefore, addressing and reducing conflict among SE communities is crucial.…”

Section: Related Work 21 Offensive Language Detectionmentioning

confidence: 99%

“…Although plenty of works have investigated abuse in social networking sites like Twitter and Facebook [21,42], abuse in SE communities is seldom addressed. Raman et al [30] have investigated the presence of toxicity in GitHub comments and the performance of various offensive content filtering tools was evaluated by Sarker et al [34]. However, no prior work has investigated the prevalence of offensive language causing conflict among community members across SE communities.…”

Section: Introductionmentioning

confidence: 99%

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Towards offensive language detection and reduction in four Software Engineering communities

Cheriyan

Savarimuthu

Cranefield

2021

Evaluation and Assessment in Software Engineering

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Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members' use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms -GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE communities.

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Section: Methodsmentioning

confidence: 99%

Section: Related Work 21 Offensive Language Detectionmentioning

confidence: 99%

Section: Related Work 21 Offensive Language Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards offensive language detection and reduction in four Software Engineering communities

Cheriyan

Savarimuthu

Cranefield

2021

Evaluation and Assessment in Software Engineering

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“…In this paper, we build in top of our previous work published in the 15th IEEE/ACM International Conference on Global Software Engineering 25 for community smells detection and extend it in the following ways: We extend our study with two additional and common types of community smells, namely, unhealthy interaction (UI), 33,34 and unfriendly communication (UC) 35–37 We extend our developers social network with common communication channels from ( i ) pull requests and ( ii ) issues reports to better capture direct communications between developers in a project. We extend our metrics suite to better capture the different symptoms of community smells by considering additional ( i ) category of socio‐technical characteristics based on sentiments analysis such as sentiments polarity, politeness, and anger emotions to better capture community smell symptoms and ( ii ) developers communication and productivity metrics based on pull requests and issues tracking related information. We extend our experimental setup and dataset with ( i ) the newly studied community smells, ( ii ) new developers social network based on pull requests and issue reports, and ( iii ) the new features based on sentiments analysis 38 …”

Section: Introductionmentioning

confidence: 99%

Improving the detection of community smells through socio‐technical and sentiment analysis

Almarimi

Ouni

Chouchen

et al. 2022

J Software Evolu Process

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Open source software development is regarded as a collaborative activity in which developers interact to build a software product. Such a human collaboration is described as an organized effort of the "social" activity of organizations, individuals, and stakeholders, which can affect the development community and the open source project health. Negative effects of the development community manifest typically in the form of community smells, which represent symptoms of organizational and social issues within the open source software development community that often lead to additional project costs and reduced software quality. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational, social, and emotional aspects to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizationalsocial and emotional symptoms that characterize the existence of community smell instances in a software project. We build a multi-label learning model to detect 10 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems, which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 143 open source projects. The statistical tests of our results show that our approach can detect community smells with an average Fmeasure of 93%, achieving a better performance compared to different state-of-theart techniques. Furthermore, we investigate the most influential community-related metrics to identify each community smell type.human factors in software engineering, software organization and properties | INTRODUCTIONOpen source software (OSS) development is a collaborative activity in which developers interact between each other to develop and maintain a software system. 1,2 Nowadays, OSS is becoming an essential part in the software economy. The number of OSS projects and their size has been growing at exponential rate. 3 The success of OSS development leads to building sustainable software systems. 2,4,5 Unlike proprietary software, OSS projects can be developed and maintained by the whole community, providing more transparency on the source code, development processes, and technologies as well as developers communications. As there exist a substantial number of OSS projects, there is a continuous

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GitHub Discussions: An exploratory study of early adoption

et al. 2021

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Discussions is a new feature of GitHub for asking questions or discussing topics outside of specific Issues or Pull Requests. Before being available to all projects in December 2020, it had been tested on selected open source software projects. To understand how developers use this novel feature, how they perceive it, and how it impacts the development processes, we conducted a mixed-methods study based on early adopters of GitHub discussions from January until July 2020. We found that: (1) errors, unexpected behavior, and code reviews are prevalent discussion categories; (2) there is a positive relationship between project member involvement and discussion frequency; (3) developers consider GitHub Discussions useful but face the problem of topic duplication between Discussions and Issues; (4) Discussions play a crucial role in advancing the development of projects; and (5) positive sentiment in Discussions is more frequent than in Stack Overflow posts. Our findings are a first step towards data-informed guidance for using GitHub Discussions, opening up avenues for future work on this novel communication channel.

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Stress and burnout in open source

Cited by 40 publications

References 20 publications

Towards offensive language detection and reduction in four Software Engineering communities

Towards offensive language detection and reduction in four Software Engineering communities

Improving the detection of community smells through socio‐technical and sentiment analysis

GitHub Discussions: An exploratory study of early adoption

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