Unit-level test adequacy criteria for visual dataflow languages and a testing methodology
Bibliographic record
Abstract
Visual dataflow languages (VDFLs), which include commercial and research systems, have had a substantial impact on end-user programming. Like any other programming languages, whether visual or textual, VDFLs often contain faults. A desire to provide programmers of these languages with some of the benefits of traditional testing methodologies has been the driving force behind our effort in this work. In this article we introduce, in the context of prograph, a testing methodology for VDFLs based on structural test adequacy criteria and coverage. This article also reports on the results of two empirical studies. The first study was conducted to obtain meaningful information about, in particular, the effectiveness of our all-Dus criteria in detecting a reasonable percentage of faults in VDFLs. The second study was conducted to evaluate, under the same criterion, the effectiveness of our methodology in assisting users to visually localize faults by reducing their search space. Both studies were conducted using a testing system that we have implemented in Prograph's IDE.
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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 | 0.011 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".