Software evolution: background, theory and practice
Bibliographic record
Abstract
This paper opens with a brief summary of some 30 years of study of the software evolution phenomenon. The results of those studies include the SPE program classification, a principle of software uncertainty and laws of E-type software evolution. The laws were termed so because they encapsulate phenomena largely independent of the people, the organisations and the domains involved in the evolution of the E-type systems studied. Recent studies have refined earlier conclusions, yielded practical guidelines for software evolution management and provide a basis for the formation of a theory of software evolution. Given the volume of published material and the extent of recent discussions on the topic (see, e.g., [Proc. ICSM, Montreal, 2002, p. 66]), this paper is restricted to an overview that exposes the significance of the evolution phenomenon and its study to the wider community, providing a basis for the future and, in particular, development of a theory of software evolution.
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 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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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; both teacher heads agree on what is shown here.
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".