An Analysis and Extension of Category Partition Testing in the Presence of Constraints
Bibliographic record
Abstract
Category Partition (CP) is a Black Box testing technique that formalizes the specification of the input domain of the system under test.A CP specification is driven by the tester's expertise and comprises of parameters, categories (characteristics of parameters) and choices (acceptable values for categories) required for extensively testing the system.To ensure completeness, choices not only correspond to permitted input values but also correspond to values that account for boundaries or robustness.These choices are combined on the basis of various selection criterion (e.g., Base Choice, Pairwise) to form test frames which given input values form test cases.To ensure that the combinations of choices are feasible and account for valid sets of user requirements, constraints are introduced.A constraint can be a specification for permitted choice combination or a choice annotation as Error or Single.In a typical development environment where testing is driven by stringent deadlines, a tester might have to decide how many constraints (constraints among choices, Error or Single annotation) are enough to attain the maximum level of test completeness.The present work will assist a test engineer in making this decision.This thesis contributes by concluding, based on experimental evaluation of academic and industrial case studies, that in case of limited resources an equally effective test suite can be attained by meticulously defining Error and Single annotations in a CP specification.The present work also contributes by challenging the notion that introducing constraints reduces the cost of the test suite by restricting the combination of choices.This thesis asserts, based on experimental evaluation, that introducing constraints does not always reduce cost and that the cost of the test suite depends on various other factors.xvi A.2 Tools/algorithms found with no technical information .....
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".