Catch Me If You Can: Detecting Model-Data Inconsistencies in Low-Code Applications.
Bibliographic record
Abstract
Low-Code Development Platforms (LCDPs) offer the benefit of rapid application development, but they sometimes result in inconsistencies while the generated application is in operation.Such inconsistencies often occur despite passing technical validations, indicating that the generated application functions properly without errors.However, issues arise due to semantic discrepancies, leading to conflicting stakeholder perspectives on shared data.The inconsistencies can emerge from model and data co-evolution, but existing inconsistency management techniques, e.g., in databases, multi-view and multi-paradigm modeling, are not well suited to the particular challenges in LCDPs.These approaches are inadequate in this context as they rely on relationships and adherence, such as conformance, which are not applicable in LCDPs.We present a technique and formalization for detecting inconsistencies between various artifacts based on their corresponding rules in low-code applications.We evaluate the correctness of our approach on a domain-specific low-code platform, and assess its scalability, sensitivity to rule mapping complexity, and efficiency with experiments using synthetic data.The results show that the proposed approach is capable of detecting inconsistencies while maintaining a desirable level of efficiency, scalability, and sensitivity.
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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 | 0.006 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".