An approach to clone detection in behavioural models
Bibliographic record
Abstract
In this paper we present an approach for identifying near-miss interaction clones in reverse-engineered UML behavioural models. Our goal is to identify patterns of interaction (“conversations”) that can be used to characterize and abstract the run-time behaviour of web applications and other interactive systems. In order to leverage robust near-miss code clone technology, our approach is text-based, working on the level of XMI, the standard interchange serialization for UML. Behavioural model clone detection presents several challenges - first, it is not clear how to break a continuous stream of interaction between lifelines into meaningful conversational units. Second, unlike programming languages, the XMI text representation for UML is highly non-local, using attributes to reference information in the model file remotely. In this work we use a set of contextualizing source transformations on the XMI text representation to reveal the hidden hierarchical structure of the model and granularize behavioural interactions into conversational units. Then we adapt NiCad, a near-miss code clone detection tool, to help us identify conversational clones in reverse-engineered behavioural models.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".