Evaluating the Evolution of Small Scale Open Source Software Systems
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
For real-world software to remain satisfactory to its stakeholders requires its continual enhancement and adaptation. Acceptance of this phenomenon, termed software evolution, as intrinsic to real world software has led to an increasing interest in disciplined and systematic planning, management and improvement of the evolution process. Almost all of the previous work on software evolution has been concerned with the evolution of large scale real-world software systems developed within a single company using traditional management techniques, or with the large scale open source software systems (LSOSSS). However, there is to our knowledge little or no work that has considered small scale open source software systems (SSOSSS). This paper presents an analysis of the evolution behavior of two small size open source software systems, the Barcode Library and Zlib. Surprisingly, unlike large scale open source software systems, the evolution behavior of these small size open source software systems appears to follow Lehman's laws for software evolution.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.003 | 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