Test Generation Tool for Modified Condition/Decision Coverage
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-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.
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 | 0.001 |
| 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 | 0.000 | 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