Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Grammars are traditionally used to recognize or parse sentences in a language, but they can also be used to generate sentences. In grammar‐based test generation (GBTG), context‐free grammars are used to generate sentences that are interpreted as test cases. A generator reads a grammar G and generates L ( G ), the language accepted by the grammar. Often L ( G ) is so large that it is not practical to execute all of the generated cases. Therefore, GBTG tools support ‘tags’: extra‐grammatical annotations which restrict the generation. Since its introduction in the early 1970s, GBTG has become well established: proven on industrial projects and widely published in academic venues. Despite the demonstrated effectiveness, the tool support is uneven; some tools target specific domains, e.g. compiler testing, while others are proprietary. The tools can be difficult to use and the precise meaning of the tags are sometimes unclear. As a result, while many testing practitioners and researchers are aware of GBTG, few have detailed knowledge or experience. We present YouGen, a new GBTG tool supporting many of the tags provided by previous tools. In addition, YouGen incorporates covering‐array tags, which support a generalized form of pairwise testing. These tags add considerable power to GBTG tools and have been available only in limited form in previous GBTG tools. We provide semantics for the YouGen tags using parse trees and a new construct, generation trees. We illustrate YouGen with both simple examples and a number of industrial case studies. Copyright © 2010 John Wiley & Sons, Ltd.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it