SafeType: detecting type violations for type‐basedalias analysis of C
Bibliographic record
Abstract
Summary To improve the ability of compilers to determine alias relations in a program, the C standard restricts the types of expressions that may access objects in memory. In practice, however, many existing C programs do not conform to these restrictions, making type‐based alias analysis unsound for those programs. As a result, type‐based alias analysis is frequently disabled. Existing approaches for verifying type safety exist within larger frameworks designed to verify overall memory safety, requiring both static analysis and runtime checks. This paper describes the motivation for analyzing the safety of type‐based alias analysis independently; presents SafeType, a purely static approach to detection of violations of the C standard's restrictions on memory accesses; describes an implementation of SafeType in the IBM XL C compiler, with flow‐sensitive and context‐sensitive queries to handle variables with typevoid *; evaluates that implementation, showing that it scales to programs with hundreds of thousands of lines of code; and uses SafeType to identify a previously unreported violation in the470.lbmbenchmark in SPEC CPU2006. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".