Supporting maintenance tasks on transformational code generation environments
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
At the core of model-driven software development, model-transformation compositions enable automatic generation of executable artifacts from models. Although the advantages of transformational software development have been explored by numerous academics and industry practitioners, adoption of the paradigm continues to be slow, and limited to specific domains. The main challenge to adoption is the fact that maintenance tasks, such as analysis and management of model-transformation compositions and reflecting code changes to model transformations, are still largely unsupported by tools. My dissertation aims at enhancing the field's understanding around the maintenance issues in transformational software development, and at supporting the tasks involved in the synchronization of evolving system features with their generation environments. This paper discusses the three main aspects of the envisioned thesis: (a) complexity analysis of model-transformation compositions, (b) system feature localization and tracking in model-transformation compositions, and (c) refactoring of transformation compositions to improve their qualities.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 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