Traceability Modeling for the Engineering of Heterogeneous Systems
Bibliographic record
Abstract
Capturing traceability information among artifacts helps ensure product quality and assists tracking functional and non-functional requirements, and performing system validation and impact analysis.Although literature provides many techniques for modeling traceability, existing solutions are either tailored to specific domains or not complete enough (e.g., lack support to specify traceability link semantics).This research examines the current traceability solutions and identifies the drawbacks that hinder capturing some traceability information of heterogeneous artifacts.In this context, heterogeneous artifacts refer to artifacts that come from widely different modeling notations (e.g., UML, Simulink, natural language text, source code).In this thesis, our contribution comprises a traceability framework that can accommodate the traceability of system engineering artifacts which come from different domains of expertise.The framework includes the followings: First, a set of requirements for a traceability model that are necessary to build a generic traceability model.Second, a generic traceability model that is not domain specific and which, therefore, provides a solution for modeling traceability links among heterogeneous models, that is, models for which traceability links need to be established between artifacts in widely different modeling languages (e.g., UML, block diagrams, informal documents).We argue that the proposed requirements are sufficient to build a traceability model oblivious of the heterogeneity of the models whose artifacts need to be traced.We also argue that our traceability model is extensible in the sense that it can adapt to new modeling languages, new ways of characterizing traceability information for instance, without requiring changes to the model itself; Third, a trace links taxonomy that encompasses semantically well-defined trace links that can be utilized along with the traceability model.The design of our framework is validated through a set of validation methods.Also, it is supported by our findings from a survey and a systematic literature review.vi 11.8.3 Remaining Threats to our Taxonomy 133 11.8.4 Remaining Threats to the Literature Review 133 11.8.5 Remaining Threats of the Case Study 134 11.9 Summary......
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".