An Experience Report on Producing Verifiable Builds for Large-Scale Commercial Systems
Bibliographic record
Abstract
Build verifiability is a safety property for a software system which can be used to check against various security-related issues during the build process. In summary, a verifiable build generates equivalent build artifacts for every build instance, allowing independent auditors to verify that the generated artifacts correspond to their source code. Producing a verifiable build is a very challenging problem, as non-equivalences in the build artifacts can be caused by non-determinsm from the build environment, the build toolchain, or the system implementation. Existing research and practices on build verifiability mainly focus on remediating sources of non-determinism. However, such a process does not work well with large-scale commercial systems (LSCSs) due to their stringent security requirements, complex third party dependencies, and large volumes of code changes. In this paper, we present an experience report on using a unified process and a toolkit to produce verifiable builds for LSCSs. A unified process contrasts with the existing practices in which recommendations to mitigate sources of non-determinism are proposed on a case-by-case basis and are not codified in a comprehensive tool. Our approach supports the following three strategies to systematically mitigate non-equivalences in the build artifacts: remediation, controlling, and interpretation. Case study on three LSCSs within${{\sf Huawei}}$shows that our approach is able to increase the proportion of verified build artifacts from less than 50 to 100 percent. To cross-validate our approach, we successfully applied our approach to build 2,218 open source packages distributed under${{\sf CentOS}}$7.8, increasing the proportion of verified build artifacts from 85 to 99 percent with minimal human intervention. We also provide an overview of our mitigation guideline, which describes the recommended strategies to mitigate various non-equivalences. Finally, we present some discussions and open research problems in this area based on our experience and lessons learned in the past few years of applying our approach within the company. This paper will be useful for practitioners and software engineering researchers who are interested in build verifiability.
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.
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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".