Life and death of software packages: an evolutionary study of Debian
Bibliographic record
Abstract
Software evolution has been studied at a variety of granularities. The evolution of code, classes, groups of classes, programs and finally large scale applications have been examined in detail. What lies beyond is the study of the evolution of software collections that group together many individual applications. Collecting software and distributing it via a central repository has been popular in the open source world, and only recently caught on commercially with Apple's Mac app store and Microsoft's Windows store. Similar to other papers on evolution, the value lies in our observations. We extract facts and patterns about the system which have not been documented before. Our study will focus on the Debian software collection because it is widely used, extremely large and easily accessibility. Debian is a software collection based off the Linux kernel with a large number of packages spread over multiple hardware platforms. In this paper we ask: How is Debian evolving and can our observations influence the future design of large software collections? This paper describes the life cycle of a package from inception to end by carrying out a twelve year longitudinal study using the Ultimate Debian Database (UDD). The birth of packages is examined to see how Debian is growing. Conversely, package death is also analyzed to determine the lifespan of these packages. Moreover, four different package attributes are examined. They are package age, package bugs, package maintainers and package popularity. These four attributes combine together to give us the overall biography of Debian packages.
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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 | 0.001 | 0.017 |
| 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 | 0.001 |
| 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".