Identification of behavioural and creational design motifs through dynamic analysis
Bibliographic record
Abstract
Abstract Design patterns offer design motifs, solutions to object‐oriented design problems. Design motifs lead to well‐structured designs and thus are believed to ease software maintenance. However, after use, they are often ‘lost’ and are consequently of little help during program comprehension and other maintenance activities. Therefore, several works proposed design pattern identification approaches to recover occurrences of the motifs. These approaches mainly used the structure and organization of classes as input. Consequently, they have a low precision when considering behavioural and creational motifs, which pertain to the assignment of responsibilities and the collaborations among objects at runtime. We propose MoDeC, an approach to describe behavioural and creational motifs as collaborations among objects in the form of scenario diagrams. We identify these motifs using dynamic analysis and constraint programming. Using a proof‐of‐concept implementation of MoDeC and different scenarios for five other Java programs andBuilder,Command, andVisitor, we show that MoDeC has a better precision than the state‐of‐the‐art static approaches. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".