Give me some REST: A Controlled Experiment to Study Effects and Perception of Model-Driven Engineering with a Domain-Specific Language
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
Domain-Specific Languages (DSLs) are an efficient means to counter accidental complexity and are therefore a key technology for Model-Driven Engineering (MDE). Despite DSLs' potential, there is a lack of empirical research regarding the practical effects and developer perception of DSL-driven tools. In this paper, we present a controlled experiment with 28 participants around a previously developed DSL-based toolchain, which assists the migration of legacy software to REST. A direct comparison of developer performance for a) "DSL toolchain" and b) "classic manual software migration" allowed for analysis, quantification of effects and developer perception, as well as reasoning on general advantages, and DSL-related challenges. In certain cases, we measured a significant correlation between toolchain use and performance gains for developers. Detailed analysis of developer activities suggests the DSL toolchain alleviates tasks which show error-prone or time-consuming in the manual alternative. We then extracted acceptance-hindering factors from participant feedback and derived a series of recommendations for MDE practitioners who seek to develop DSL-based tools.
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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 | 0.000 |
| 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 | 0.000 |
| 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