Dynamic white-box software testing using a recursive hybrid evolutionary strategy/genetic algorithm
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
Software testing is an important and time consuming part of the software development cycle. While automated testing frameworks do help in reducing the amount of programmer time that testing requires, the onus is still upon the programmer to provide such a framework with the inputs on which the software must be tested. This requires static analysis of the source code, which is more effective when performed as a peer review exercise and is highly dependent on the skills of the programmers performing the analysis. Thus, it demands the allocation of precious time of highly skilled programmers. An algorithm that automatically generates inputs to satisfy test coverage criteria for the software being tested would therefore be valuable, as it would imply that the programmer no longer needs to analyze code to generate the relevant test cases. This paper explores a hybrid evolutionary strategy with an evolutionary algorithm to discover such test case synthesis, in an improvement over previous methods which overly focus their search without maintaining the diversity required to cover the entire search space efficiently.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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