A new approach to test case generation based on real-time process algebra (RTPA)
Bibliographic record
Abstract
In order to detect and fix errors and bugs in software design and implementation, testing is a vital process in software engineering. It is recognized that testing a large-scale software system needs more intelligence and effort than code design and implementation do. The paper presents a new approach to specification-based test generation that enables test cases to be generated before the implementation of code. We adopt real-time process algebra (RTPA) to describe software system architectures, static and dynamic behaviors. Based on RTPA, a method of least completed set of tests (LCST) is developed, which reveals that the sufficient number of tests for a given software is O(4/sup n/), where n is the number of the input variables. The LCST method provides a new way to predict how many independent test cases exist for a given program, and how the tests may be generated on the basis of its RTPA specifications. Experimental case studies on applications of the LCST method are reported that demonstrate the usage and efficiency of this new method.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".