IPA: Error Propagation Analysis of Multi-Threaded Programs Using Likely Invariants

Abstract

Error Propagation Analysis (EPA) is a technique forunderstanding how errors affect a program's execution and resultin program failures. For this purpose, EPA usually compares thetraces of a fault-free (golden) run with those from a faulty run ofthe program. This makes existing EPA approaches brittle for multithreadedprograms, which do not typically have a deterministicgolden run. In this paper, we study the use of likely invariantsgenerated by automated approaches as alternatives for goldenrun based EPA in multithreaded programs. We present InvariantPropagation Analysis (IPA), an approach and a framework forautomatically deriving invariants for multithreaded programs, and using the invariants for EPA. We evaluate the invariantsderived by IPA in terms of their coverage for different faulttypes across six representative programs through fault injectionexperiments. We find that stable invariants can be inferred in allsix programs, although their coverage of faults depends on theapplication and the fault type.