A Comparison of Three Algorithms for Computing Truck Factors

Ferreira, Mívian; Valente, M.; Ferreira, Kecia A. M.

doi:10.1109/icpc.2017.35

“…Truck-Factor. Originally formulated as "The number of people on your team who have to be hit with a truck before the project is in serious trouble" 3 and established in software engineering literature as well [81], [82], [83]. We operationalise truck-factor based on core and peripheral community structures identified by CODEFACE, as the degree of ability of the community to remain connected without its core part.…”

Section: Community-related Control Variablesmentioning

confidence: 99%

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Palomba

¹

,

Tamburri

²

,

Fontana

³

et al. 2021

IIEEE Trans. Software Eng.

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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“…For example, Lin et al (2017) studied why some developers are more likely to continue their contributions than others. Others focused on understanding the potential issues surrounding developers leaving OSS projects, including the so-called truck factor (Ricca and Marchetto, 2010;Avelino et al, 2016;Ferreira et al, 2017;Cosentino et al, 2015), which is defined as the number of people who have to be hit by a truck (i.e., leave the project) before the project itself is at risk (Williams and Kessler, 2002). Avelino et al (2019) found that truck factor is a real concern, which may affect project evolution.…”

Section: Developers' Turnover and Disengagementmentioning

confidence: 99%

“…So far, research has focused on the developers' life cycle: how people join the projects (Von Krogh et al, 2003), including the barriers they face (Steinmacher et al, 2015;Hannebauer and Gruhn, 2017;Balali et al, 2018); how they are attracted (Yamashita et al, 2016;Santos et al, 2013;Fronchetti et al, 2019); and how they become long-term contributors or core members (Ducheneaut, 2005;Nakakoji et al, 2002;Zhou and Mockus, 2012). The limited research about understanding developers' disengagement has focused on the risks that projects incur when they lose developers (Ricca and Marchetto, 2010;Avelino et al, 2019;Ferreira et al, 2017) and factors related to the developers' abandonment, using the survival analysis technique (Lin et al, 2017). To avoid developers' disengagement, researchers analyzed the factors related to developers' engagement and retention (Schilling, 2014;Midha and Palvia, 2007;Zhou et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Will You Come Back to Contribute? Investigating the Inactivity of OSS Core Developers in GitHub

Calefato

¹

,

Gerosa

²

,

Iaffaldano

³

et al. 2021

Preprint

1

0

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Several Open-Source Software (OSS) projects depend on the continuity of their development communities to remain sustainable. Understanding how developers become inactive or why they take breaks can help communities prevent abandonment and incentivize developers to come back. In this paper, we propose a novel method to identify developers’ inactive periods by analyzing the individual rhythm of contributions to the projects. Using this method, we quantitatively analyze the inactivity of core developers in 18 OSS organizations hosted on GitHub. We also survey core developers to receive their feedback about the identified breaks and transitions. Our results show that our method was effective for identifying developers’ breaks. About 94% of the surveyed core developers agreed with our state model of inactivity; 71% and 79% of them acknowledged their breaks and state transition, respectively. We also show that all core developers take breaks (at least once) and about a half of them (~ 45%) have completely disengaged from a project for at least one year. We also analyzed the probability of transitions to/from inactivity and found that developers who pause their activity have a ~ 35 to ~ 55% chance to return to an active state; yet, if the break lasts for a year or longer, then the probability of resuming activities drops to ~ 21–26%, with a ~ 54% chance of complete disengagement. These results may support the creation of policies and mechanisms to make OSS community managers aware of breaks and potential project abandonment.

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“…• Truck-Factor: The truck factor measures the minimum number of members of a community that have to quit before the project will fail [27]- [29]. In our work, we operationalized truck factor based on core and peripheral community structures identified by CODEFACE4SMELLS, i.e., as the degree of ability of the community to remain connected without its core part.…”

Section: E Rq 2 -Building a Statistical Modelmentioning

confidence: 99%

Gender Diversity and Women in Software Teams: How Do They Affect Community Smells?

Catolino

¹

,

Palomba

²

,

Tamburri

³

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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A Comparison of Three Algorithms for Computing Truck Factors

Cited by 34 publications

References 18 publications

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Will You Come Back to Contribute? Investigating the Inactivity of OSS Core Developers in GitHub

Gender Diversity and Women in Software Teams: How Do They Affect Community Smells?

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