A Designing Method for Function Combination Testing Based on Orthogonal Chart
Bibliographic record
Abstract
Software testing has become increasingly important in software development.Its importance has been extremely un- derlined today when software often features with multiple functions and a test is necessary to examine whether these functions could co-exist harmoniously.However,tests on any software function combinations are unpractical if no pre-condition is given beforehand as they would lead to testing staffs exhausted by endless testing projects.Orthogonal chart is an effective way in helping testing staffs to get rid of unnecessary tests due to unscientific project designs.In the paper,the author depicts in details a new testing method on software function combination based on the theory of orthogonal chart and its adoption in practice.Fur- thermore,the author introduces a tool especially on creating test cases accordingly.With the adoption of the new testing method,testing staffs could scientifically combine functions of test targets and make sure they could test as many functions of target software as possible by using just a few testing cases.It has been proved that,compared with traditional testing methods, the new method based on orthogonal chart could double the coverage of test cases on targeted software,detect bugs by three times and shorten time on test case designing by one sixth.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".