Lossless Comparison of Nested Software Decompositions
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
Reverse engineering legacy software systems often involves the employment of clustering algorithms that automatically decompose a software system into subsystems. The decompositions created by existing software clustering algorithms are often nested, i.e. subsystems may contain other finer-grained subsystems as well as system resources, such as source files. It is rather surprising then, that almost all existing methods for decomposition comparison assume flat decompositions, i.e. subsystems only contain system resources. In this paper, we introduce UpMoJo, a novel comparison method for software decompositions that can be applied to both nested and flat decompositions. The benefits of utilizing this method are presented in both analytical and experimental fashion. We also compare UpMoJo to the END framework, the only other existing method for nested decomposition comparison.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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