PIE: A Tool for Visualizing the Life Cycle of Design Patterns in Open Source Software Projects
Bibliographic record
Abstract
Design patterns are employed in source code to solve commonly occurring programming tasks using understood best practices. Object-oriented design patterns usually span multiple classes and objects and play an integral role in the way object-oriented software is built. One challenge with using object-oriented design patterns is that over the life of a project, these patterns can undergo both planned and unplanned changes. Unplanned changes are often the result of bug fixes or code maintenance tasks that modify a design pattern as a side effect. Furthermore, these unplanned changes can result in increased brittleness of the code and can compromise the overall stability of the software. Over the lifetime of a software project, developers may only become aware of these unplanned changes when the code brittleness results in a software bug. To improve developers' understanding of object-oriented design pattern evolution, we introduce the design Pattern Instance Explorer (PIE) _ an exploratory visualization tool that enable developers to visualize a git repository's object-oriented design patterns and their life cycles. In addition to discussing the PIE tool, we provide examples of how this tool can be used to identify and understand design pattern changes. Tool demonstration video: https://www.youtube.com/watch?v=Gkn_5q8_Awg
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".