Growth, evolution, and structural change in open source software
Bibliographic record
Abstract
Our recent work has addressed how and why software systems evolve over time, with a particular emphasis on software architecture and open source software systems [2, 3, 6]. In this position paper, we present a short summary of two recent projects.First, we have performed a case study on the evolution of the Linux kernel [3], as well as some other open source software (OSS) systems. We have found that several OSS systems appear not to obey some of "Lehman's laws" of software evolution [5, 7], and that Linux in particular is continuing to grow at a geometric rate. Currently, we are working on a detailed study of the evolution of one of the subsystems of the Linux kernel: the SCSI drivers subsystem. We have found that cloning, which is usually considered to be an indicator of lazy development and poor process, is quite common and is even considered to be a useful practice.Second, we are developing a tool called Beagle to aid software maintainers in understanding how large systems have changed over time. Beagle integrates data from various static analysis and metrics tools and provides a query engine as well as navigable visualizations. Of particular note, Beagle aims to provide help in modelling long term evolution of systems that have undergone architectural and structural change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.003 |
| 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".