A linked data platform for mining software repositories
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
The mining of software repositories involves the extraction of both basic and value-added information from existing software repositories. The repositories will be mined to extract facts by different stakeholders (e.g. researchers, managers) and for various purposes. To avoid unnecessary pre-processing and analysis steps, sharing and integration of both basic and value-added facts are needed. In this research, we introduce SeCold, an open and collaborative platform for sharing software datasets. SeCold provides the first online software ecosystem Linked Data platform that supports data extraction and on-the-fly inter-dataset integration from major version control, issue tracking, and quality evaluation systems. In its first release, the dataset contains about two billion facts, such as source code statements, software licenses, and code clones from 18 000 software projects. In its second release the SeCold project will contain additional facts mined from issue trackers and versioning systems. Our approach is based on the same fundamental principle as Wikipedia: researchers and tool developers share analysis results obtained from their tools by publishing them as part of the SeCold portal and therefore make them an integrated part of the global knowledge domain. The SeCold project is an official member of the Linked Data dataset cloud and is currently the eighth largest online dataset available on the Web.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.002 |
| 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