TOWARDS AUTOMATIC ESTABLISHMENT OF MODEL DEPENDENCIES USING FORMAL CONCEPT ANALYSIS
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
Software evolution is an iterative and incremental process that encompasses the modification and alteration of software models at different levels of abstraction. These modifications are usually performed independently, but the objects to which they are applied to, are in most cases mutually dependent. Inconsistencies and drift among related artifacts may be created if the effects of an alteration are not properly identified, recorded, and propagated in other dependent models. For large systems, it is possible that there is a considerable number of such model dependencies, for which manual extraction is not feasible. In this paper, we introduce an approach for automating the identification and encoding of dependence relations among software models and their elements. The proposed dependency extraction technique first uses association rules to map types between models at different levels of abstraction. Formal concept analysis is then used to identify clusters of model elements that pertain to similar or associated concepts. Model elements that cluster together are considered related by a dependency relation. The technique is used to synchronize business process specifications with the underlying J2EE source code models.
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 | 0.001 | 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