Monitoring aspects for the customization of automatically generated code for big-step models
Bibliographic record
Abstract
The output of a code generator is assumed to be correct and not usually intended to be read or modified; yet programmers are often interested in this, e.g., to monitor a system property. Here, we consider code customization for a family of code generators associated with big-step executable modelling languages (e.g., statecharts). We introduce a customization language that allows us to express customization scenarios for the generated code independently of a specific big-step execution semantics. These customization scenarios are all different forms of runtime monitors, which lend themselves to a principled, uniform implementation for observation and code extension. A monitor is given in terms of the enabledness and execution of the transitions of a model and a reachability relation between two states of the execution of the model during a big step. For each monitor, we generate the aspect code that is incorporated into the output of a code generator to implement the monitor at the generated-code level. Thus, we provide means for code analysis through using the vocabulary of a model, rather than the detail of the generated code. Our technique not only requires the code generators to reveal only limited information about their code generation mechanisms, but also keeps the structure of the generated code intact. We demonstrate how various useful properties of a model, or a language, can be checked using our monitors.
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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 | 0.002 |
| 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 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".