A Manifestation of Model-Code Duality: Facilitating the Representation of State Machines in the Umple Model-Oriented Programming Language
Bibliographic record
Abstract
I wish to thank foremost my supervisor Dr. Timothy C. Lethbridge. Tim has been my supervisor throughout the PhD years and has provided guidance and deep insights that helped shape my understanding of the software engineering field. A very special, and well-deserved, thank you to the following: a) The Complexity Reduction in Software Engineering (CRUISE) research group. I have benefited from our weekly meetings and discussions. Particular thanks to Andrew Forward, Garzon Miguel, and Hamoud Ajman. My family and friends. Thank you to my mom, Sameha, for her unconditional support, my father, Bahy, for his reviews and input. b) The Natural Sciences and Engineering Research Council of Canada (NSERC), IBM, and University of Ottawa for their collaboration and funding. These institutions have made available an environment through which I was able to conduct research while staying in touch with the industry. c) Software professionals around the world. Sincere thanks to the individuals that participated in my research, published valuable references for my writings, as well as to those in the various news-groups about software engineering that I follow. Your knowledge and insight
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".