Relationship-based change propagation: A case study
Bibliographic record
Abstract
Software development is an evolutionary process. Requirements of a system are often incomplete or inconsistent, and hence need to be extended or modified over time. Customers may demand new services or goals that often lead to changes in the design and implementation of the system. These changes are typically very expensive. Even if only local modifications are needed, manually applying them is time-consuming and and error-prone. Thus, it is essential to assist users in propagating changes across requirements, design, and implementation artifacts. In this paper, we take a model-based approach and provide an automated algorithm for propagating changes between requirements and design models. The key feature of our work is explicating relationships between models at the requirements and design levels. We provide conditions for checking validity of these relationships both syntactically and semantically. We show how our algorithm utilizes the relationships between models at different levels to localize the regions that should be modified. We use the IBM Trade 6 case study to demonstrate our approach.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".