Probabilistic Program Execution is a Viable Way to Find Domains from 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
Program domains are useful in many areas of software engineering including software reliability, testing, and program comprehension. Program paths provide understanding of program dynamic behaviour. In this work, we show that it is possible to extract domains and the paths they represent from software using program execution based algorithms. This thesis looks at five different execution based algorithms for identifying the domains/paths. These algorithms work differently to generate the domains from a possibly infinite set of possible paths. Two of the algorithms utilize an operational profile that describes the probability distribution of possible inputs. This allows them to generate the most important paths first. These program execution based algorithms were explored using some simple functions. The results showed that the Probabilistic Execution algorithm produces the domains in the strictly most significant order, limited only by the equality of the integration available. The Monte Carlo Execution algorithm provided almost the same accuracy but is somewhat simpler. Of the algorithms that do not utilize operational profiles, Random Execution worked the best.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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