A Study of the Time Dependence of Code Changes
Bibliographic record
Abstract
Much of modern software development consists of building on older changes. Older periods provide the structure (e.g., functions and data types) on which changes in future periods will build. Given a particular period in the lifetime of a project, one can determine prior periods on which it builds, and future periods which build on it. Using this knowledge, managers can identify foundational periods in the lifetime of a project, which provide the structural foundation for a large number of future periods. A good understanding and detailed documentation of events and decisions in such foundational periods is essential for the smooth evolution of a project. This paper examines how changes build on older changes by measuring the time dependence between code changes. Using our approach, we can create time dependence relations between periods and study the characteristics of such dependence relations. We apply our approach on two large open source projects, PostgreSQL and FreeBSD. We find that foundational periods are periods with huge restructurings, important new features or large imports of external source code. We also find that a project, as it ages, either progressively depends on older periods or cycles between depending on old and new periods.
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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 | 0.003 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".