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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".