Bibliographic record
Abstract
Automated program understanding tools have the potential to make important contributions to reducing the very substantial costs of program maintenance. We formulate a particular approach to program understanding within the context of the object-oriented simulation language, MODSIM. The software tool that has been developed is called PUMP (Program Understanding of MODSIM Programs). The input to this tool is the syntactically correct MODSIM program code for a simulation project which typically is distributed over several files. The main presentation of information about the program is via a user interface that is organized along hierarchical lines that correspond to the organizational structure of MODSIM programs. The hierarchical approach permits examination of program features in increasing levels of detail. The main thrust of the analysis is to identify entity types (e.g., objects, methods, variables) used in the program together with their interrelationships. The key design aspects of PUMP are outlined in the paper and an overview of the user interface is presented. Emphasis is given to the mechanisms specifically developed for handling the object-oriented features of MODSIM.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".