Fingerprinting design patterns
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Other designConsensus signal: none
- Genre
- Candidate signal: MethodsConsensus signal: none
- Teacher disagreement score
- 0.866
- Threshold uncertainty score
- 0.461
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.236 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Design patterns describe good solutions to common and recurring problems in program design. The solutions are design motifs which software engineers imitate and introduce in the architecture of their program. It is important to identify the design motifs used in a program architecture to understand solved design problems and to make informed changes to the program. The identification of micro-architectures similar to design motifs is difficult because of the large search space, i.e., the many possible combinations of classes. We propose an experimental study of classes playing roles in design motifs using metrics and a machine learning algorithm to fingerprint design motifs roles. Fingerprints are sets of metric values characterising classes playing a given role. We devise fingerprints experimentally using a repository of micro-architectures similar to design motifs. We show that fingerprints help in reducing the search space of micro-architectures similar to design motifs efficiently using the Composite design motif and the JHotDraw framework.
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.
The record
- Venue
- Topic
- Software Engineering Research
- Field
- Computer Science
- Canadian institutions
- Université de Montréal
- Funders
- not available
- Keywords
- Computer scienceArchitectureMotif (music)SoftwareSoftware design patternIdentification (biology)Theoretical computer scienceMetric (unit)Artificial intelligenceData miningSoftware engineeringMachine learningProgramming languageEngineering
- Has abstract in OpenAlex
- yes