Lightweight detection of physical unit inconsistencies without program annotations
Bibliographic record
Abstract
Systems interacting with the physical world operate on quantities measured with physical units. When unit operations in a program are inconsistent with the physical units' rules, those systems may suffer. Existing approaches to support unit consistency in programs can impose an unacceptable burden on developers. In this paper, we present a lightweight static analysis approach focused on physical unit inconsistency detection that requires no end-user program annotation, modification, or migration. It does so by capitalizing on existing shared libraries that handle standardized physical units, common in the cyber-physical domain, to link class attributes of shared libraries to physical units. Then, leveraging rules from dimensional analysis, the approach propagates and infers units in programs that use these shared libraries, and detects inconsistent unit usage. We implement and evaluate the approach in a tool, analyzing 213 open-source systems containing +900,000 LOC, finding inconsistencies in 11% of them, with an 87% true positive rate for a class of inconsistencies detected with high confidence. An initial survey of robot system developers finds that the unit inconsistencies detected by our tool are 'problematic', and we investigate how and when these inconsistencies occur.
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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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".