.png){kind=icon}
.png){kind=icon}
Return
Data Race Detection by Digest-Driven Abstract Interpretation
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-15700-3_15.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sound static analysis can prove the absence of data races by establishing that no two conflicting memory accesses can occur at the same time. We repurpose the concept of digests summaries of computational histories originally introduced to bring tunable concurrency-sensitivity to thread-modular value analysis by abstract interpretation, extending this idea to race detection: We use digests to capture the conditions under which conflicting accesses may not happen in parallel. To formalize this, we give a definition of data races in the thread-modular local trace semantics and show how exclusion criteria for potential conflicts can be expressed as digests. We report on our implementation of digest -driven data race detection in the static analyzer GOBLINT, and evaluate it on the Sv-ComP benchmark suite. Combining the lockset digest with digests reasoning on thread ids and thread joins increases the number of correctly solved tasks by more than a factor of five compared to lockset reasoning alone.
Keywords:
data races
concurrency
static program analysis
software verification
abstract interpretation
Journal
V
IF:
0
Papers:
18
Citations:
0