Growth, evolution, and structural change in open source software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it