An Extensible Compiler for Implementing Software Design Patterns as Concise Language Constructs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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 | 0.001 |
| 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)
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 it