mCUTE: A Model-Level Concolic Unit Testing Engine for UML State Machines
Bibliographic record
Abstract
Model Driven Engineering (MDE) techniques raise the level of abstraction at which developers construct software. However, modern cyber-physical systems are becoming more prevalent and complex and hence software models that represent the structure and behavior of such systems still tend to be large and complex. These models may have numerous if not infinite possible behaviors, with complex communications between their components. Appropriate software testing techniques to generate test cases with high coverage rate to put these systems to test at the model-level (without the need to understand the underlying code generator or refer to the generated code) are therefore important. Concolic testing, a hybrid testing technique that benefits from both concrete and symbolic execution, gains a high execution coverage and is used extensively in the industry for program testing but not for software models. In this paper, we present a novel technique and its tool mCUTE1, an open source 2 model-level concolic testing engine. We describe the implementation of our tool in the context of Papyrus-RT, an open source Model Driven Engineering (MDE) tool based on UML-RT, and report the results of validating our tool using a set of benchmark models.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".