Using mutation analysis for a model-clone detector comparison framework
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
Model-clone detection is a relatively new area and there are a number of different approaches in the literature. As the area continues to mature, it becomes necessary to evaluate and compare these approaches and validate new ones that are introduced. We present a mutation-analysis based model-clone detection framework that attempts to automate and standardize the process of comparing multiple Simulink model-clone detection tools or variations of the same tool. By having such a framework, new research directions in the area of model-clone detection can be facilitated as the framework can be used to validate new techniques as they arise. We begin by presenting challenges unique to model-clone tool comparison including recall calculation, the nature of the clones, and the clone report representation. We propose our framework, which we believe addresses these challenges. This is followed by a presentation of the mutation operators that we plan to inject into our Simulink models that will introduce variations of all the different model clone types that can then be searched for by each respective model-clone detector.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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