How is ATL Really Used? Language Feature Use in the ATL Zoo
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
Studies of code repositories have long been used to understand the use of programming languages and to provide insight into how they should evolve. Such studies can highlight features that are rarely used and can safely be removed to simplify the language. Conversely, combinations of features that are frequently used together can be identified and possibly replaced with new features to improve the user experience. Unfortunately, this kind of research has not been as popular in Model Driven Development (MDD). More specifically, using repositories of model transformations (in any language) to understand how the features of these languages are used has not been investigated much, despite its potential benefits. In this paper, we study the use of the ATL model transformation language in an ATL transformation repository. We identify three research questions aimed at providing insight into how ATL's features are actually used. Using the TXL source transformation language, we implement a parser-based analyzer to extract information from the ATL Zoo. We use this information to answer these research questions and provide additional observations based on manual inspection of ATL artifacts.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| 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