Bibliographic record
Abstract
Model-based testing (MBT) is a quality assurance technique where a test suite is generated from an abstract model.There are a number of different approaches to accomplish model-based testing.While state-based techniques dominate, they have a number of inherent issues.These issues have led to the pursuit of alternatives such as scenario-based approaches.ACL/VF is one such scenario-based approach.Developed by Dr. Corriveau and his students, the ACL/VF system provides both a language to specify an implementation-independent testable model of a specification and the tool to validate an implementation against this model.However, the current implementation of ACL/VF has a number of issues that prevent it from being a usable solution.In particular, the current version of ACL/VF is extremely .NET3.5 specific.Unfortunately, upgrading it to a more recent version of .NET essentially amounts to a complete rewrite.Given the widespread use of Java, a most immediate research question is to determine whether or not it is feasible to reimplement ACL/VF on that platform.Our claim is that this reimplementation can be accomplished through a mapping from ACL specifications to JavaMOP monitor specifications.The following thesis provides two case studies supporting this claim as well as an element-by-element proposed mapping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".