9.4.1 Lessons Learned From Industrial Validation of COSYSMO
Bibliographic record
Abstract
Abstract The development of COSYSMO has been an ongoing collaboration between industry, government, and academia since 2001. INCOSE provided expertise as well as a forum for collaboration between stakeholders that led to the eventual development of the model. In 2004, we provided eleven lessons learned from experiences collecting systems engineering data from six companies in collaboration with the INCOSE Measurement Working Group and the Practical Software and Systems Measurement (PSM). These lessons were focused on the development of COSYSMO that was motivated by a similar model from the software domain, COCOMO II, but was a first of its kind for systems engineering. Now that the development phase of the model is completed we take a retrospective view of lessons learned during the ongoing validation phase of the model and present new lessons learned that should help cost model developers, academic researchers, and practitioners develop and validate similar approaches. These lessons include the need for more specific counting rules, an approach to account for reuse in systems engineering, and strategies for model adoption in organizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.084 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| 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".