Experiments with clustering as a software remodularization method
Bibliographic record
Abstract
As valuable software systems get old, reverse engineering becomes more and more important to the companies that have to maintain the code. Clustering is a key activity in reverse engineering to discover a better design of the systems or to extract significant concepts from the code. Clustering is an old activity, highly sophisticated, offering many methods to answer different needs. Although these methods have been well documented in the past, these discussions may not apply entirely to the reverse engineering domain. We study some clustering algorithms and other parameters to establish whether and why they could be used for software remodularization. We study three aspects of the clustering activity: abstract descriptions chosen for the entities to cluster; metrics computing coupling between the entities; and clustering algorithms. The experiments were conducted on three public domain systems (gcc, Linux and Mosaic) and a real world legacy system (2 million LOC). Among other things, we confirm the importance of a proper description scheme of the entities being clustered, we list a few good coupling metrics to use and characterize the quality of different clustering algorithms. We also propose novel description schemes not directly based on the source code and we advocate better formal evaluation methods for the clustering results.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".