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
OpenMP is a widely used API for parallel programming in C/C++ and Fortran. Its flexibility and simplicity have made its usage popular in many numerical or scientific applications. The prevalence of OpenMP programs in such important areas makes its respective compiler’s correctness significant. Unfortunately, OpenMP compilers are not tested as thoroughly as regular C/C++ compilers. More importantly, it is difficult to apply previous mutation-based testing techniques like EMI because of the parallelism in seed programs. \n \nThis thesis introduces new fuzz testing approaches specifically for OpenMP compilers. For existing OpenMP programs, we de-parallelize and mutate them with dead code injection and false parallelization. We also transform existing regular C programs into OpenMP programs with template-based mutations. Two test suites were used for the evaluation, the OpenMP Offloading Validation & Verification Suite (SOLLVE VV) and programs generated from Csmith. For SOLLVE VV and with GCC and LLVM, the proposed techniques have been shown to increase coverage by at least 4.60% and 1.81% respectively. Compared to Csmith programs, coverage is improved by at least 3.90% for GCC and 1.85% for LLVM.
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 | 0.001 | 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