Impacts of software community patterns on process and product: An empirical study

Stefano, Manuel De; Iannone, Emanuele; Pecorelli, Fabiano; Tamburri, Damian A.

doi:10.1016/j.scico.2021.102731

Cited by 11 publications

(6 citation statements)

References 64 publications

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“…We categorized 24 studies as empirical and one as theoretical according to Appendix A. Empirical studies conducted by Tamburri [5,11,20,42] and Palomba [10] offer information about the identification of the causes and effects of community smells in practice, information about the impact on work settings, and management approaches. The remaining included studies provide further information on community smells in relationship with development communities and processes [14,32,33,34,43,44,45,46] and research contributions such as models [12,13,16,47,48,49,50,51,52,53], organizational strategies [15], and tools [54]. We organized our results around the research questions described in Section 3.…”

Section: Reporting the Resultsmentioning

confidence: 99%

“…The second feature is the lifecycle phase. Other features that influence the occurrence and intensity of community smells are the team size [11], team structure or team composition [14,54,46,43], and gender diversity [12].…”

Section: The 11 Properties Of Community Smellsmentioning

confidence: 99%

“…Technical debt effect NA Negative impact on a software artifacts, software products, and projects. and product: An empirical study [43] [10] No causes provided by or found in the literature Modular structure and refactored classes are more difficult to understand and contribute for newcomers Code red [10] No causes provided by or found in the literature…”

Section: Culturementioning

confidence: 99%

See 2 more Smart Citations

Community smells—The sources of social debt: A systematic literature review

Caballero-Espinosa

Carver

Stowers³

2023

Information and Software Technology

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Section: Reporting the Resultsmentioning

confidence: 99%

Section: The 11 Properties Of Community Smellsmentioning

confidence: 99%

Section: Culturementioning

confidence: 99%

See 1 more Smart Citation

Community smells—The sources of social debt: A systematic literature review

Caballero-Espinosa

Carver

Stowers³

2023

Information and Software Technology

View full text Add to dashboard Cite

“…In addition, we manually assign a weight to each project to serve for the sampling. The more frequently the project has been studied previously (e.g., [20,33,64]), the higher the weight. Finally, our dataset includes ten application software (e.g., Atom/atom), two system software (e.g., kubernetes/kubernetes), sixteen web libraries and frameworks (e.g., angular/angular.js), nine non-web libraries and frameworks (e.g., tensorflow/tensorflow), and thirteen software tools (e.g., Microsoft/vscode).…”

Section: Data Collectionmentioning

confidence: 99%

Measure the Diversity of Open Source Software Projects' Forks with Fork Entropy

Zheng¹,

Wang²,

Zhang³

et al. 2022

Preprint

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Forks play a central role in modern pull-based OSS development.Although rich empirical results on the participants, challenges, and features of forks have been announced, there is little discussion on quantitatively measuring the population of forks around OSS projects. In this paper, we take a step toward enriching the set of metrics about forks by proposing the fork entropy to measure the diversity of fork populations around OSS projects. We operationalize the proposed fork entropy based on Rao's quadratic entropy with a distance function defined on the forks' modifications to project files. After verifying the construct validity of fork entropy, we show the usefulness of fork entropy in understanding and predicting OSS development in terms of external productivity, the acceptance rate of external pull-requests, and code quality using a dataset consisting of fifty popular OSS projects hosted on GitHub. By conducting regression analyses, we find that fork entropy significantly and positively affects external productivity, the acceptance rate of external pull-requests, and code quality, even though sometimes with a small effect. However, as expected, fork entropy at a high level sometimes plays a negative role in OSS development. We also observe fork entropy can magically moderate other factors' effect on some project outcomes. We believe our new metric of fork entropy is helpful to guide practices of OSS development. CCS CONCEPTS• Software and its engineering → Open source model; • General and reference → Metrics.

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“…Our work aims to help researchers and practitioners identify relevant repositories for their use cases, offering a trade-off between accuracy and volume to many who have manually annotated repositories using Borges et al (2016)'s classification (Bi et al, 2021(Bi et al, , 2022Coelho and Valente, 2017;De Stefano et al, 2020Hata et al, 2022;Hayashi et al, 2019;Rehman et al, 2022;Tamburri et al, 2019) or intend to use it in future work (Bayati and Heidary, 2019;De Stefano et al, 2022;Wu, 2020;Wu et al, 2020a,b;Zhang et al, 2020). For example, Bi et al (2022) manually annotated 1,000 repositories in terms of their application domain using the Borges et al taxonomy to investigate release note production and usage of release notes in practice, and Hayashi et al (2019) manually annotated 969 repositories in terms of their application domain to investigate the impacts of daylight saving time on software development.…”

Section: Introductionmentioning

confidence: 99%

Automatically Categorising GitHub Repositories by Application Domain

2022

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GitHub is the largest host of open source software on the Internet. This large, freely accessible database has attracted the attention of practitioners and researchers alike. But as GitHub's growth continues, it is becoming increasingly hard to navigate the plethora of repositories which span a wide range of domains. Past work has shown that taking the application domain into account is crucial for tasks such as predicting the popularity of a repository and reasoning about project quality. In this work, we build on a previously annotated dataset of 5,000 GitHub repositories to design an automated classifier for categorising repositories by their application domain. The classifier uses state-of-the-art natural language processing techniques and machine learning

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Impacts of software community patterns on process and product: An empirical study

Cited by 11 publications

References 64 publications

Community smells—The sources of social debt: A systematic literature review

Community smells—The sources of social debt: A systematic literature review

Measure the Diversity of Open Source Software Projects' Forks with Fork Entropy

Automatically Categorising GitHub Repositories by Application Domain

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