Conceptualizing Systems for Understanding: An Empirical Test of Decomposition Principles in Object-Oriented Analysis
Bibliographic record
Abstract
During the early phase of systems development, systems analysts often conceptualize the domain under study and represent it in one or more conceptual models. One of the most important, yet elusive roles of conceptual models is to increase analysts’ understanding of a domain. In this paper, we evaluate the ability of the good decomposition model (GDM) (Wand and Weber 1990) to explain the degree to which conceptual models communicate meaning about a domain to analysts. We address the question, “Do unified modeling language (UML) analysis diagrams that manifest better decompositions increase analysts’ understanding of a domain?” GDM defines five conditions (minimality, determinism, losslessness, weak coupling, and strong cohesion) deemed necessary to decompose a domain in such a way that the resulting model communicates meaning about the domain effectively. In our evaluation, we operationalized each of these conditions in a set of UML diagrams and tested participants’ understanding of those diagrams. Our results lend support to GDM across measures of actual understanding. However, the impact on participants’ perceptions of their understanding was equivocal.
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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 | 0.065 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".