Validation Against Actual Behavior: Still a Challenge for Testing Tools.
Bibliographic record
Abstract
A quality-driven approach to software development and testing demands that, ultimately, the requirements of stakeholders be validated against the actual behavior of an implementation under test (IUT). Current approaches and tools for testing fall into one of two categories: code-centric or model-centric. In this paper we review typical tools offered in each of these two categories, in order to establish the ability of such tools to support validation against actual behavior. We postulate that such support requires that test cases be both a) traceable back to the requirements of stakeholders and b) executed using an actual IUT in order to determine their outcome based on the actual behavior of an IUT. We observe that, in general, code-based testing tools fail to offer traceability between stakeholders' requirements and test cases. In contrast, in model-based testing, tests are generated from and traceable back to models, but they are typically disconnected from an actual IUT. Thus, we argue that validation against actual behavior remains a challenge for most existing code-centric and model-centric testing tools. We conclude by suggesting some essential functionality for a testing tool that could support the validation of the requirements of stakeholders against the actual behavior of an
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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 | 0.056 | 0.239 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".