Secrets from the Monster: Extracting Mozilla’s Software Architecture
Bibliographic record
Abstract
As large systems evolve, their architectural integrity tends to decay. Reverse engineering tools, such as PBS [7, 19], Rigi [15], and Acacia [5], can be used to acquire an understanding of a system’s “as-built” architecture and in so doing regain control over the system. A problem that has impeded the widespread adoption of reverse engineering tools is the tight coupling of their subtools, including source code “fact” extractors, visualization engines, and querying mechanisms; this coupling has made it difficult, for example, for users to employ alternative extractors that might have different strengths or understand different source languages. The TAXFORM project has sought to investigate how different reverse engineering tools can be integrated into a single framework by providing mappings to and from common data schemas for program “facts” [2]. In this paper, we describe how we successfully integrated the Acacia C and C++ fact extractors into the PBS system, and how we were then able to create software architecture models for two large software systems: the Mozilla web browser (over two million lines of C++ and C) and the VIM text editor (over 160,000 lines of C).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".