Design pattern rationale graphs: linking design to source
Bibliographic record
Abstract
A developer attempting to evolve a system in which design patterns have been applied can benefit from knowing which code implements which design pattern. For instance, the developer may be able to understand the purpose, or to assess the flexibility of the code, more quickly. The degree to which the developer benefits depends upon their understanding of the pattern. Achieving an in-depth understanding of even a simple pattern can be difficult as pattern descriptions span several pages of text, and discuss interrelated design concepts and choices. To enable a developer to effectively trace the design goals associated with a pattern to and from source, we have developed the Design Pattern Rationale Graph (DPRG) approach and associated tool. A DPRG makes explicit the relationships between design concepts in a design pattern, provides a graphical representation of the design pattern text, and supports the linking of those concepts to implementing code. In this paper, we introduce the DPRG approach and tool, and present case studies to show that a DPRG can, at low-cost, help a developer identify design goals in a pattern, and can improve a developer's confidence about how those goals are realized in a code base.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".