Bibliographic record
Abstract
Reverse engineering systems hold great promise in aiding developers regain control over long-lived software projects whose architecture has been allowed to "drift". However, it is well known that these systems have relative strengths and weaknesses, and to date relatively little work has been done on integrating various subtools within other reverse engineering systems. The design of a common interchange format for data used by reverse engineering tools is therefore of critical importance.In this position paper, we describe some of our previous work with TAXFORM (Tuple Attribute eXchange FORMat) [2,6], and in integrating various "fact extractors" into the PBS reverse engineering system. For example, we have recently created translation mechanisms that enable the Acacia system's C and C++ extractors to be used within PBS, and we have used these mechanisms to create software architecture models of two large software systems: the Mozilla web browser (2.2 MLOC of C++ and C) and the VIM text editor (150 KLOC of C) [6]. We also describe our requirements for an exchange format for reverse engineering tools and some problems that must be resolved.
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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 | 0.035 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".