Extending Category Partition's <scp>B</scp>ase <scp>C</scp>hoice criterion to better support constraints
Bibliographic record
Abstract
Abstract To ensure software is performing as intended, it can be black‐box or white‐box tested. Category partition is a black‐box, specification‐based testing technique that begins by identifying the parameters, categories (characteristics of parameters), and choices (acceptable values for categories). These choices are then combined to form test frames on the basis of various criteria such as Base Choice and Each Choice. To ensure that the combinations of choices are feasible, constraints on choices are introduced. Combining choices, while accounting for constraints, to form an each choice adequate test set is feasible (eg, using constrained covering arrays from combinatorial testing). However, the Base Choice criterion has not been defined to specifically account for constraints on choices, resulting in adverse consequences. In this paper, we introduce two extensions to the Base Choice criterion, namely, Constrained Base Choice and Extended Constrained Base Choice to specifically account for (complex) constraints on choices. We use a number of academic and industrial case studies to compare different adequacy criteria, including the new ones, in terms of cost and effectiveness at finding faults. Results show the performance of the new criteria equivalent to a 3‐way combination criterion with a much smaller cost.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".