Bibliographic record
Abstract
The business models of software/platform as a service have contributed to developers dependence on the Internet. Developers can rapidly point each other and consumers to the newest software changes with the power of the hyper link. But, developers are not limited to referencing software changes to one another through the web. Other shared hypermedia might include links to: Stack Overflow, Twitter, and issue trackers. This work explores the software traceability of Uniform Resource Locators (URLs) which software developers leave in commit messages and software repositories. URLs are easily extracted from commit messages and source code. Therefore, it would be useful to researchers if URLs provide additional insight on project development. To assess traceability, manual topic labelling is evaluated against automated topic labelling on URL data sets. This work also shows differences between URL data collected from commit messages versus URL data collected from source code. As well, this work explores outlying software projects with many URLs in case these projects do not provide meaningful software relationship information. Results from manual topic labelling show promise under evaluation while automated topic labelling did not yield precise topics. Further investigation of manual and automated topic analysis would be useful.
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.
How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".