Feature Model Debugging based on Description Logic Reasoning.
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".