Rediscovery Datasets: Connecting Duplicate Reports Of Apache, Eclipse, And Kde
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 present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects. <strong>File Descriptions</strong> apache.csv - Apache Defect Rediscovery dataset eclipse.csv - Eclipse Defect Rediscovery dataset kde.csv - KDE Defect Rediscovery dataset apache.relations.csv - Inter-relations of rediscovered defects of Apache eclipse.relations.csv - Inter-relations of rediscovered defects of Eclipse kde.relations.csv - Inter-relations of rediscovered defects of KDE create_and_populate_neo4j_objects.cypher - Populates Neo4j graphDB by importing all the data from the CSV files. Note that you have to set dbms.import.csv.legacy_quote_escaping configuration setting to false to load the CSV files as per https://neo4j.com/docs/operations-manual/current/reference/configuration-settings/#config_dbms.import.csv.legacy_quote_escaping create_and_populate_mysql_objects.sql - Populates MySQL RDBMS by importing all the data from the CSV files rediscovery_db_mysql.zip - For your convenience, we also provide full backup of the MySQL database neo4j_examples.txt - Sample Neo4j queries mysql_examples.txt - Sample MySQL queries rediscovery_eclipse_6325.png - Output of Neo4j example #1 distinct_attrs.csv - Distinct values of bug_status, resolution, priority, severity for each project
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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