Feature Model Debugging based on Description Logic Reasoning.
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 product line engineering refers to the concept of sharing commonalities and variabilities of a set of software products in a target domain of interest. Feature models are one of the prominent representation formalisms for software product lines. Given the fact that feature models cover all possible applications and products of a target domain, it is possible that the artifacts are not necessarily and always consistent. Therefore, identifying and resolving inconsistencies in feature models is a significant task; especially, due to the fact that a large number of possible products and complex interactions between the software product line features need to be checked. To address these challenges, in this paper, we propose a framework with an automated tool to find and fix the inconsistencies of feature models based on Description Logic (DL) reasoning. The basic idea of our approach is to first transform and represent a feature model using Description Logics. The second step is to identify the possible inconsistencies of the feature model using DL reasoning and then recommend appropriate solutions to a domain analyst for resolving existing inconsistencies.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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