Do software libraries evolve differently than applications?
Bibliographic record
Abstract
More and more, developers use reusable components like libraries to produce high quality software systems. These systems need to satisfy not only the initial demands of their stakeholders, but they need to also offer support for future, changing requirements. While several studies have looked at the cost of modifying systems, there exists no work comparing if libraries evolve differently than applications. This study attempts to verify this assumption quantitatively. In this paper, we define design changes metrics to estimate the amount of high-level change required of individual classes and use metrics to describe their structure. These measures are then used as inputs in models capable of predicting code change. We used machine learning techniques to build these models and tested them on the evolution of industrial open-source systems. Two of the systems were libraries, and two were standalone applications. We found that while design changes are systematically correlated with code changes, structure metrics are better predictors of code change in libraries with well developed class hierarchies. With the two applications without this characteristic, structure alone was a poor predictor. 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".