The Design of a Self-Compiling C Transpiler Targeting POSIX Shell
Bibliographic record
Abstract
Software supply chain attacks are increasingly frequent and can be hard to guard against. Reproducible builds ensure that generated artifacts (executable programs) can be reliably created from their source code. However, the tools used by the build process are also vulnerable to supply chain attacks so a complete solution must also include reproducible builds for the various compilers used. With this problem as our main motivation we explore the use of the widely available POSIX shell as the only trusted pre-built binary for the reproducible build process. We have developed pnut, a C to POSIX shell transpiler written in C that generates human-readable shell code. Because the compiler is self-applicable, it is possible to distribute a human-readable shell script implementing a C compiler that depends only on the existence of a POSIX compliant shell such as bash, ksh, zsh, etc. Together, pnut and the shell serve as the seed for a chain of builds that create increasingly capable compilers up to the most recent version of the GNU Compiler Collection (GCC) that is a convenient basis to build any other required tool in the toolchain. The end result is a complete build toolchain built only from a shell and human-readable source files. We discuss the level of C language support needed to achieve our goal, the generation of portable POSIX shell code from C, and the performance of the compiler and generated code.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.003 |
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".