Using origin analysis to detect merging and splitting of source code entities
Bibliographic record
Abstract
Merging and splitting source code entities is a common activity during the lifespan of a software system; as developers rethink the essential structure of a system or plan for a new evolutionary direction, so must they be able to reorganize the design artifacts at various abstraction levels as seems appropriate. However, while the raw effects of such changes may be plainly evident in the new artifacts, the original context of the design changes is often lost. That is, it may be obvious which characters of which files have changed, but it may not be obvious where or why moving, renaming, merging, and/or splitting of design elements has occurred. In this paper, we discuss how we have extended origin analysis (Q. Tu et al., 2002), (M.W. Godfrey et al., 2002) to aid in the detection of merging and splitting of files and functions in procedural code; in particular, we show how reasoning about how call relationships have changed can aid a developer in locating where merges and splits have occurred, thereby helping to recover some information about the context of the design change. We also describe a case study of these techniques (as implemented in the Beagle tool) using the PostgreSQL database system as the subject.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".