The Safety and Performance of Prominent Programming Languages
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
Background: The current primary focus of programming language benchmarking studies in the literature is performance with less attention on safety. However, this context has a research gap because the software industry has focused more on software safety than performance to safeguard clients. This paper attempts to address this research gap by benchmarking languages in both safety and performance. Furthermore, this study includes Rust, a relatively new language with promising safety and performance features. Methods: This paper compares six prominent programming languages (in alphabetical order: C, C[Formula: see text], Go, Java, Python and Rust) to determine which is the best in terms of safety and performance using quantitative and qualitative methods through actual testing of code and analysis of existing information. Results: The comparisons show that Rust was the safest language, outperforming all the other languages. Regarding performance, Rust, C and C[Formula: see text] performed comparably to each other and generally outperformed Go, Java and Python. Conclusion: It is possible to achieve a superior balance of software safety and performance with, at worst, a minimal performance drop; as Rust clearly demonstrates.
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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 | 0.001 | 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