Comparing software architecture recovery techniques using accurate dependencies
Bibliographic record
Abstract
Abstract—Many techniques have been proposed to automati-cally recover software architectures from software implementa-tions. A thorough comparison among the recovery techniques is needed to understand their effectiveness and applicability. This study improves on previous studies in two ways. First, we study the impact of leveraging more accurate symbol dependencies on the accuracy of architecture recovery techniques. Previous studies have not seriously considered how the quality of the input might affect the quality of the output for architecture recovery techniques. Second, we study a system (Chromium) that is substantially larger (9.7 million lines of code) than those included in previous studies. Obtaining the ground-truth architecture of Chromium involved two years of collaboration with its developers. As part of this work we developed a new submodule-based technique to recover preliminary versions of ground-truth architectures. The other systems that we study have been examined pre-viously. In some cases, we have updated the ground-truth architectures to newer versions, and in other cases we have corrected newly discovered inconsistencies. Our evaluation of nine variants of six state-of-the-art ar-chitecture recovery techniques shows that symbol dependencies generally produce architectures with higher accuracies than include dependencies. Despite this improvement, the overall accuracy is low for all recovery techniques. The results suggest that (1) in addition to architecture recovery techniques, the accuracy of dependencies used as their inputs is another factor to consider for high recovery accuracy, and (2) more accurate recovery techniques are needed. Our results show that some of the studied architecture recovery techniques scale to the 10M lines-of-code range (the size of Chromium), whereas others do not. I.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".