A Case Study in Incremental Architecture-Based Re-engineering of a Legacy Application
Why this work is in the frame
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Bibliographic record
Abstract
Without rigorous software development and maintenance, software tends to lose its original architectural structure and become more difficult to understand and modify. ArchJava, a recently proposed implementation language which embeds a component-and-connector architectural specification within Java implementation code, offers the promise of preventing the loss of architectural structure. We describe a case study in which we incrementally re-engineer an existing implementation with an eroded architecture to obtain an ArchJava implementation that more closely matches an idealized architecture. Building on results from similar case studies, we chose an application consisting of over 16,000 source lines of Java code and 80 classes that exhibited many characteristics of real-world legacy applications. We describe our process, some lessons learned, as well as some perceived limitations with the tools, techniques and languages we used.
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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