Refactoring Dataset for SOEN 6491 Software Refactoring, Winter 2024 - Team Alpha
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 dataset collected by Team Alpha in SOEN 6491 Software Refactoring course, Winter 2024 Content Description File name File type Description vertx.db, spring-boot.db, mina-sshd.db, guava.db SQLite Database Fully collected refactoring dataset: Refactoring data (via RefactoringMiner) Code smells (via Organic) Code duplications (via PMD) Code churn (via Git) release_analysis.xlsx Excel spreadsheet Timeline and statistics of releases in relation to refactoring commits. Releases are collected via GitHub API. flink.db, rocketmq.db, ant.db, dubbo.db, elasticsearch.db, neo4j.db, rxjava.db, zookeeper.db SQLite Database Partially collected refactoring dataset: Refactoring data (via RefactoringMiner)
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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