An empirical study of open-source and closed-source software products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We describe an empirical study of open-source and closed-source software projects. The motivation for this research is to quantitatively investigate common perceptions about open-source projects, and to validate these perceptions through an empirical study. We investigate the hypothesis that open-source software grows more quickly, but does not find evidence to support this. The project growth is similar for all the projects in the analysis, indicating that other factors may limit growth. The hypothesis that creativity is more prevalent in open-source software is also examined, and evidence to support this hypothesis is found using the metric of functions added over time. The concept of open-source projects succeeding because of their simplicity is not supported by the analysis, nor is the hypothesis of open-source projects being more modular. However, the belief that defects are found and fixed more rapidly in open-source projects is supported by an analysis of the functions modified. We find support for two of the five common beliefs and conclude that, when implementing or switching to the open-source development model, practitioners should ensure that an appropriate metrics collection strategy is in place to verify the perceived benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it