Stronger Together: On Combining Relationships in Architectural Recovery Approaches
Bibliographic record
Abstract
Architecture recovery is the process of obtaining the intended architecture of a software system by analyzing its implementation. Most existing architectural recovery approaches rely on extracting information about relationships between code entities and then use the extracted information to group closely related entities together. The approaches differ by the type of relationships they consider, e.g., method calls, data dependencies, and class name similarity. Prior work shows that combining multiple types of relationships during the recovery process is often beneficial as it leads to a better result than the one obtained by using the relationships individually. Yet, most, if not all, academic and industrial architecture recovery approaches simply unify the combined relationships to produce a more complete representation of the analyzed systems. In this paper, we propose and evaluate an alternative approach to combining information derived from multiple relationships, which is based on identifying agreements/disagreements between relationship types. We discuss advantages and disadvantages of both approaches and provide suggestions for future research in this area.
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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 | 0.036 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.038 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".