Derivation of Failure Mode and Effects Analysis (FMEA) Table from UML Software Model by Epsilon Model Transformation
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
Discovering and documenting potential failures and irregular user behavior that can interrupt the normal system behaviour is very important during the development of critical systems. Failure Mode and Effects Analysis (FMEA) is a bottom-up inductive analysis method that helps to identify potential failure modes based on experience with similar products and processes. Model-Driven Development (MDD) is a software development paradigm that raises the level of abstraction of software development by changing the focus from code to models and automating the generation of code from models. MDD also eases the derivation of analysis models for different software nonfunctional properties (NFPs) in the early stage of software development. The objective of this thesis is to develop a model transformation process that takes as input a UML software model with failure mode annotations and generates a FMEA model. The transformation is developed in Epsilon, a new family of languages specialized in model transformations, refinement and management.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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