State generation and automated class testing
Bibliographic record
Abstract
The maturity of object-oriented methods has led to the wide availability of container classes: classes that encapsulate classical data structures and algorithms. Container classes are included in the C++ and Java standard libraries, and in many proprietary libraries. The wide availability and use of these classes makes reliability important, and testing plays a central role in achieving that reliability. The large number of cases necessary for thorough testing of container classes makes automated testing essential. This paper presents a novel approach for automated testing of container classes based on combinatorial algorithms for state generation. The approach is illustrated with black-box and white-box test drivers for a class implemented with the red–black tree data structure, used widely in industry and, in particular, in the C++ Standard Template Library. The white-box driver is based on a new algorithm for red–black tree generation. The drivers are evaluated experimentally, providing quantitative measures of their effectiveness in terms of block and path coverage. The results clearly show that the approach is affordable in terms of development cost and execution time, and effective with respect to coverage achieved. The results also provide insight into the relative advantages of black-box and white-box drivers, and into the difficult problem of infeasible paths. Copyright © 2000 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".