Are Two Heads Better Than One for Computer-Aided Design?
Bibliographic record
Abstract
Abstract With the availability of cloud-based software, ubiquitous internet, and advanced digital modeling capabilities, a new potential has emerged to design physical products with methods previously embraced by the software engineering community. One such example is pair programming, where two coders work together synchronously to develop one piece of code. Pair programming has been shown to lead to higher-quality code and user satisfaction. Cutting-edge collaborative computer-aided design (CAD) technology affords the possibility to apply synchronous collaborative access in mechanical design. We test the generalizability of findings from the pair programming literature to the same dyadic configuration of work in CAD, which we call pair CAD. We performed human subject experiments with 60 participants to test three working styles: individuals working by themselves, pairs sharing control of one model instance and input, and pairs able to edit the same model simultaneously from two inputs. We compare the working styles on speed and quality and propose mechanisms for our observations via interpretation of patterns of communication, satisfaction, and user cursor activity. We find that on a per-person basis, individuals were faster than pairs due to coordination and overhead inefficiencies. We find that pair work, when done with a single shared input, but not in a parallel mode, leads to higher-quality models. We conclude that it is not software capabilities alone that influence designer output; choices regarding work process have a major effect on design outcomes, and we can tailor our process to suit project requirements.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".