Black-Box String Test Case Generation through a Multi-Objective Optimization
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
String test cases are required by many real-world applications to identify defects and security risks. Random Testing (RT) is a low cost and easy to implement testing approach to generate strings. However, its effectiveness is not satisfactory. In this research, black-box string test case generation methods are investigated. Two objective functions are introduced to produce effective test cases. The diversity of the test cases is the first objective, where it can be measured through string distance functions. The second objective is guiding the string length distribution into a Benford distribution based on the hypothesis that the population of strings is right-skewed within its range. When both objectives are applied via a multi-objective optimization algorithm, superior string test sets are produced. An empirical study is performed with several real-world programs indicating that the generated string test cases outperform test cases generated by other methods.
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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 | 0.000 |
| 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 | 0.000 | 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