Combining experiments and grounded theory to evaluate a research prototype: lessons from the Umple model-oriented programming technology
Bibliographic record
Abstract
Research prototypes typically lack the level of quality and readiness required for industrial deployment. Hence, conducting realistic experimentation with professional users that reflect real life tasks is challenging. Experimentation with toy examples and tasks suffers from significant threats to external validity. Consequently, results from such experiments fail to gain confidence or mitigate risks, a prerequisite for industrial adoption. This paper presents two empirical studies conducted to evaluate a model-oriented programming language called Umple; a grounded theory study and a controlled experiment of comprehension. Evaluations of model-oriented programming is particularly challenging. First, there is a need to provide for highly sophisticated development environments for realistic evaluation. Second, the scarcity of experienced users poses additional challenges. In this paper we discuss our experiences, lessons learned, and future considerations in the evaluation of a research prototype tool.
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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 | 0.142 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".