An Extensible Compiler for Implementing Software Design Patterns as Concise Language Constructs
Bibliographic record
Abstract
Design patterns are generic solutions to common programming problems. Design patterns represent a typical example of design reuse. However, implementing design patterns can lead to several problems, such as programming overhead and traceability. Existing research introduced several approaches to alleviate the implementation issues of design patterns. Nevertheless, existing approaches pose different implementation restrictions and require programmers to be aware of how design patterns should be implemented. Such approaches make the source code more prone to faults and defects. In addition, existing design pattern implementation approaches limit programmers to apply specific scenarios of design patterns (e.g. class-level), while other approaches require scattering implementation code snippets throughout the program. Such restrictions negatively impact understanding, tracing, or reusing design patterns. In this paper, we propose a novel approach to support the implementation of software design patterns as an extensible Java compiler. Our approach allows developers to use concise, easy-to-use language constructs to apply design patterns in their code. In addition, our approach allows the application of design patterns in different scenarios. We illustrate our approach using three commonly used design patterns, namely Singleton, Observer and Decorator. We show, through illustrative examples, how our design pattern constructs can significantly simplify implementing design patterns in a flexible, reusable and traceable manner. Moreover, our design pattern constructs allow class-level and instance-level implementations of design patterns.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".