Pattern visualization for software comprehension
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
Cognitive science emphasizes the strength of visual formalisms for human learning and problem solving. In software engineering, a clear, visual presentation of a system's architecture can significantly reduce the effort of comprehension. Yet, all too often the documentation of complex software systems lacks clear identification of the architectural constituents and insufficiently relates them to the source code. It is our contention that visualization of the architectural constituents within the source code model is an indispensable aid for the guided evolution of large-scale software systems. We present a prototype tool for visualizing both published, generic design patterns as well as well-thought, ad-hoc design solutions, given the reverse-engineered source code of a system. We discuss the architecture and core functionality of this tool, addressing source code reverse engineering, design repository, design representation, and design clustering. Then, we present our visualization objectives and detail our techniques for pattern visualization. A case study example helps explicate and illustrate our work.
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 | 0.000 | 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 | 0.000 | 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