A testing methodology for a dataflow based visual programming language
Bibliographic record
Abstract
Dataflow based visual programming languages have become an important topic of research in recent years, yielding a variety of research systems and commercial applications. As with any programming language, visual or textual, dataflow programs may contain faults. Thus, to ensure the coma functioning of dataflow programs, and increase confidence in the quality of these programs, testing is required. Despite this valid observation, we find that the casting criteria found in the literature mainly addressed imperative, declarative, and form-based languages. However, we did not find any discussion that specifically addressed testing criteria for dataflow programs. In this paper, we investigate, from a testing perspective, differences between dataflow and imperative languages. The results reveal opportunities for adapting code-based control-flow testing criteria to test dataflow languages. We show that our proposed testing methodology is well suited for dataflow programs. In particular, the "all-branches" criterion provides important error detection ability, and can be applied to dataflow programs. We implemented a testing system that allows users to visually and empirically investigate the testability of programs written in the visual programming language Prograph. Our empirical results confirm that, analogous to imperative languages, the all-branches criterion cannot detect all the errors in a dataflow program. Thus, to catch those undetected errors, more rigorous testing should be applied.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".