Mining usage data and development artifacts
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
Software repository mining techniques generally focus on analyzing, unifying, and querying different kinds of development artifacts, such as source code, version control meta-data, defect tracking data, and electronic communication. In this work, we demonstrate how adding real-world usage data enables addressing broader questions of how software systems are actually used in practice, and by inference how development characteristics ultimately affect deployment, adoption, and usage. In particular, we explore how usage data that has been extracted from web server logs can be unified with product release history to study questions that concern both users ’ detailed dynamic behaviour as well as broad adoption trends across different deployment environments. To validate our approach, we performed a study of two open source web browsers: Firefox and Chrome. We found that while Chrome is being adopted at a consistent rate across platforms, Linux users have an order of magnitude higher rate of Firefox adoption. Also, Firefox adoption has been concentrated mainly in North America, while Chrome users appear to be more evenly distributed across the globe. Finally, we detected no evidence in age-specific differences in navigation behaviour among Chrome and Firefox users; however, we hypothesize that younger users are more likely to have more up-to-date versions than more mature users.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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