Bibliographic record
Abstract
This dataset consists of Eclipse's static analysis performed on 10 Java projects. For each <em>.java</em> file of a test project, we ran a static analysis using Eclipse JDT Core allows us to retrieve all the possible function calls based on typing/imports for a given completion site). Each java project has three files structured as follows: <strong>*.json file. </strong>The file contains all the method declarations of the project and the function calls in their body. For each function call, the file lists all the possible function call that could have been made at that place in the source code. For practical purposes, we splitted this file into two text files. <strong>*_sequences.txt file. </strong>This file consists of all the method declaration + function call sequences in the project. The last element of each line corresponds to a completion site. <strong>*_proposals.txt file.</strong> Each line is made of the function-call suggestions retrieved by static analysis for the corresponding line in the <em>*_sequences.txt</em> file. The corpus was used for the experiments in the paper <strong>Combining Code Embedding with Static Analysis for Function-Call Completion</strong>. Github repository to replicate the experiments: https://github.com/mweyssow/cse-saner
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.047 |
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; both teacher heads agree on what is shown here.
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".