Context As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective This study investigates the effects of the pandemic on developers’ wellbeing and productivity. Method A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers’ wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees’ home offices. Women, parents and disabled persons may require extra support.

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To survive and succeed, FLOSS projects need contributors able to accomplish critical project tasks. However, such tasks require extensive project experience of long term contributors (LTCs). Aim: We measure, understand, and predict how the newcomers' involvement and environment in the issue tracking system (ITS) affect their odds of becoming an LTC. Method: ITS data of Mozilla and Gnome, literature, interviews, and online documents were used to design measures of involvement and environment. A logistic regression model was used to explain and predict contributor's odds of becoming an LTC. We also reproduced the results on new data provided by Mozilla. Results: We constructed nine measures of involvement and environment based on events recorded in an ITS. Macro-climate is the overall project environment while micro-climate is person-specific and varies among the participants. Newcomers who are able to get at least one issue reported in the first month to be fixed, doubled their odds of becoming an LTC. The macro-climate with high project popularity and the micro-climate with low attention from peers reduced the odds. The precision of LTC prediction was 38 times higher than for a random predictor. We were able to reproduce the results with new Mozilla data without losing the significance or predictive power of the previously published model. We encountered unexpected changes in some attributes and suggest ways to make analysis of ITS data more reproducible. Conclusions: The findings suggest the importance of initial behaviors and experiences of new participants and outline empirically-based approaches to help the communities with the recruitment of contributors for long-term participation and to help the participants contribute more effectively. To facilitate the reproduction of the study and of the proposed measures in other contexts, we provide the data we retrieved and the scripts we wrote at https://www.passion-lab. org/projects/developerfluency.html.

Motivation: Open-source projects are often supported by companies, but such involvement often affects the robust contributor inflow needed to sustain the project and sometimes prompts key contributors to leave. To capture user innovation and to maintain quality of software and productivity of teams, these projects need to attract and retain contributors. Aim: We want to understand and quantify how inflow and retention are shaped by policies and actions of companies in three application server projects. Method: We identified three hybrid projects implementing the same JavaEE specification and used published literature, online materials, and interviews to quantify actions and policies companies used to get involved. We collected project repository data, analyzed affiliation history of project participants, and used generalized linear models and survival analysis to measure contributor inflow and retention. Results: We identified coherent groups of policies and actions undertaken by sponsoring companies as three models of community involvement and quantified tradeoffs between the inflow and retention each model provides. We found that full control mechanisms and high intensity of commercial involvement were associated with a decrease of external inflow and with improved retention. However, a shared control mechanism was associated with increased external inflow contemporaneously with the increase of commercial involvement. Implications: Inspired by a natural experiment, our methods enabled us to quantify aspects of the balance between community and private interests in open- source software projects and provide clear implications for the structure of future open-source communities.