Assessing, Comparing, and Combining Statechart- based testing and Structural testing: An Experiment
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
Although models have been proven to be helpful in a number of software engineering activities there is still significant resistance to model-driven development. This paper investigates one specific aspect of this larger problem. It addresses the impact of using statecharts for testing class clusters that exhibit a state-dependent behavior. More precisely, it reports on a controlled experiment that investigates their impact on testing fault-detection effectiveness. Code-based, structural testing is compared to statechart-based testing and their combination is investigated to determine whether they are complementary. Results show that there is no significant difference between the fault detection effectiveness of the two test strategies but that they are significantly more effective when combined. This implies that a cost-effective strategy would specify statechart-based test cases early on, execute them once the source code is available, and then complete them with test cases based on code coverage analysis.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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