Towards build verifiability for Java-based systems
Bibliographic record
Abstract
Build verifiability refers to the property that the build of a software system can be verified by independent third parties and it is crucial for the trustworthiness of a software system. Various efforts towards build verifiability have been made to C/C++-based systems, yet the techniques for Java-based systems are not systematic and are often specific to a particular build tool (e.g., Maven). In this study, we present a systematic approach towards build verifiability on Java-based systems. Our approach consists of three parts: a unified build process, a tool that dynamically controls non-determinism during the build process, and another tool that eliminates non-equivalences by post-processing the build artifacts. We apply our approach on 46 unverified open source projects from Reproducible Central and 13 open source projects that are widely used by Huawei commercial products. As a result, 91% of the unverified Reproducible Central projects and 100% of the commercially adopted OSS projects are successfully verified with our approach. In addition, based on our experience in analyzing thousands of builds for both commercial and open source Java-based systems, we present 14 patterns that introduce non-equivalences in generated build artifacts and their respective mitigation strategies. Among these patterns, 11 (78%) are unique for Java-based system, whereas the remaining 3 (22%) are common with C/C++-based systems. The approach and the findings of this paper are useful for both practitioners and researchers who are interested in build verifiability.
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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 | 0.032 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".