Experiments in automatic programming for general purposes
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
Although the generation and application of software clones is relatively unexplored, it is believed that this is a fundamental technology that can have many different applications within a software engineering environment. For example, software clones could be used in software fault tolerance. Clearly, for these clones to be usable, their production needs to be automated. An interesting approach to this automatic production or generation problem is the application of evolutionary-based genetic programming (GP). Using the paradigms of best fit, selection, crossover and mutation a number of clones, satisfying specific requirements, can be automatically generated. In general, GP is a flexible and powerful algorithm suitable for solving variety of different problems. The paper presents the results of studies that have been conducted in order to answer questions related to feasibility of using GP for clone generation: what features of GP are important? What works and what does not? How GP can be "tuned" for the problem? The results have been used to draw a set of suggestions and conclusions that indicate possible usability of GP-based approach to automatic generation of clones.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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