BinEq - A Benchmark of Compiled Java Programs to Assess Alternative Builds
Bibliographic record
Abstract
Incidents like xz and SolarWinds have led to an increased focus on software supply chain security. A particular concern is the detection and prevention of compromised builds. A common approach is to independently re-build projects, and compare the results. This leads to the availability of different binaries built from the same sources, and raises the question of how to compare the respective binaries (to confirm the integrity of builds, to detect compromised builds, etc). It is however not clear how to do this: naive bitwise comparison is often too strict, and establishing the behavioural equivalence of two binaries is undecidable. A pragmatic step towards a solution is to provision a benchmark that can be used to test and train equivalence relations. We present such a benchmark for Java bytecode, consisting of 622,029 pairs of binaries (compiled Java classes) labelled as to whether these classes are equivalent or not. We refer to these pairs as equivalence and non-equivalence oracles, respectively. We derive equivalence oracles from building 56 projects and project versions using 32 dockerised build environments (with different compilers, compiler versions and configurations). Non-equivalence oracles are derived from three different sources: (1) proven breaking API changes, (2) semantic code changes synthesised by means of bytecode mutations, and (3) code changes extracted from vulnerability patches. To illustrate how to use the benchmark, we describe an experiment using two equivalence relations based on locality-sensitive hashing.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".