Why do projects join the Apache Software Foundation?
Bibliographic record
Abstract
While numerous open source projects operate on their own, others decide to join well-established foundations such as the Apache Software Foundation (ASF) and the Eclipse Foundation. Although many studies have investigated the motivations of individuals and companies contributing to open source, it remains unknown why projects decide to join software foundations. In this paper, we study the motivators behind the projects‘ decision to join the ASF, the geographical and organizational characteristics of these projects, and the differences between projects in terms of their motivations. To this aim, we analyzed 292 proposals submitted to ASF, and we found that there is an increasing number of company-based and Asia-based projects joining the ASF in recent years. Furthermore, we found that more than half of the projects are motivated by the desire to foster their community, strengthen the outcome of the project, increase interaction with other communities, and boost technical development. Our work shed some light on projects‘ expectations from the ASF. Having this understanding can help foundations to identify ways of supporting newly joined projects, while the prospective joiners can better decide on whether ASF is the right place for them by checking the alignment of their motivations and motivations of projects that have joined in the past. Open Source Software (OSS) is free to be used and modified by any-one in the world for any purpose. Nowadays, OSS is widely used in all kinds of products and hence plays an important role in our daily life. For example, as the most used OSS, the Linux kernel runs on 85% of all smartphones. To create a better environment for OSS de-velopers to collaborate and for OSS projects to grow, many software foundations have been established, such as the Apache Software Foundation (ASF). We have seen that many OSS projects joined the ASF over the years. To join the ASF, OSS projects have to donate all their assets to the foundation, adhering to the foundation's rules and culture. In this paper, we study why these projects decide to join the ASF to understand their expectations from such foundations so that the expected technical and non-technical support can be provided to them. We identified their motivations by analyzing 292 proposals that these projects submitted when applying for joining the ASF. We observed that more than half of the projects try to join the ASF with motivations related to fostering a community, strengthening the project's outcome, increasing interactions with other OSS projects in the ASF, and boosting technical development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".