Grading on a Curve: How Rust can Facilitate New Contributors while Decreasing Vulnerabilities
Bibliographic record
Abstract
New contributors are critical to open source projects. Without them, the project will eventually atrophy and become inactive, or its experienced contributors will bias the future directions the project takes. However, new contributors can also bring a greater risk of introducing vulnerable code. For projects that have a need for both secure implementations and a strong, diverse contributor community, this conflict is a pressing issue. One avenue being pursued that could facilitate this goal is rewriting components of C or C++ code in Rust– a language designed to apply to the same domains as C and C++, but with greater safety guarantees. Seeking to answer whether Rust can help keep new contributors from introducing vulnerabilities, and therefore ease the burden on maintainers, we examine the Oxidation project from Mozilla, which has replaced components of the Firefox web browser with equivalents written in Rust. We use the available data from these projects to derive parameters for a novel application of learning curves, which we use to estimate the proportion of commits that introduce vulnerabilities from new contributors in a manner that is directly comparable. We find that despite concerns about ease of use, first-time contributors to Rust projects are about 70 times less likely to introduce vulnerabilities than first-time contributors to C++ projects. We also found that the rate of new contributors increased overall after switching to Rust, implying that this decrease in vulnerabilities from new contributors does not result from a smaller pool of more skilled developers, and that Rust can in fact facilitate new contributors. In the process, we also qualitatively analyze the Rust vulnerabilities in these projects, and measure the efficacy of the common SZZ algorithm for identifying bug-inducing commits from their fixes.
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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 | 0.022 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".