Test Generation Tool for Modified Condition/Decision Coverage
Bibliographic record
Abstract
Model-Based Testing (MBT) approaches are becoming an attractive prospect for safety-critical software testing due to their efficiency and the flexibility. Requirements based testing and structural testing are used for safety-critical systems software assessment. Structural testing criteria such as Modified Condition/Decision Coverage (MC/DC) satisfaction are required by DO-178C standard. Existing tools and techniques use MC/DC coverage criterion on the code. We propose to use model-based testing that integrates several coverage criteria such as du-path and MC/DC to enhance testing efficiency. We propose an approach that starts with requirements modeled as an Extended Finite State Machine (EFSM) that will be transformed into graphs, we add special "coverage element" data structures that are integrated into the different models via graph labeling. The resulting transformation facilitates the traceability of testing information when moving from dataflow testing to control-flow testing and vice versa, therefore making the combination of both approaches efficient for specification structural testing. The process view and the architecture of a supporting tool are given as well as the steps needed to generate MC/DC test sequences.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".