Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - IPA
T2 - Error Propagation Analysis of Multi-Threaded Programs Using Likely Invariants
AU - Chan, A.
AU - Winter, S.
AU - Saissi, H.
AU - Pattabiraman, K.
AU - Suri, Neeraj
PY - 2017/3/13
Y1 - 2017/3/13
N2 - Error Propagation Analysis (EPA) is a technique for understanding how errors affect a program's execution and result in program failures. For this purpose, EPA usually compares the traces of a fault-free (golden) run with those from a faulty run of the program. This makes existing EPA approaches brittle for multithreaded programs, which do not typically have a deterministic golden run. In this paper, we study the use of likely invariants generated by automated approaches as alternatives for golden run based EPA in multithreaded programs. We present Invariant Propagation Analysis (IPA), an approach and a framework for automatically deriving invariants for multithreaded programs, and using the invariants for EPA. We evaluate the invariants derived by IPA in terms of their coverage for different fault types 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. © 2017 IEEE.
AB - Error Propagation Analysis (EPA) is a technique for understanding how errors affect a program's execution and result in program failures. For this purpose, EPA usually compares the traces of a fault-free (golden) run with those from a faulty run of the program. This makes existing EPA approaches brittle for multithreaded programs, which do not typically have a deterministic golden run. In this paper, we study the use of likely invariants generated by automated approaches as alternatives for golden run based EPA in multithreaded programs. We present Invariant Propagation Analysis (IPA), an approach and a framework for automatically deriving invariants for multithreaded programs, and using the invariants for EPA. We evaluate the invariants derived by IPA in terms of their coverage for different fault types 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. © 2017 IEEE.
KW - Concurrency
KW - Error Propagation Analysis
KW - Fault Injection
KW - Multithreading
KW - Concurrency control
KW - Errors
KW - Gold
KW - Multiprocessing programs
KW - Multitasking
KW - Verification
KW - Automated approach
KW - Error propagation analysis
KW - Fault injection
KW - Fault types
KW - Likely invariants
KW - Multi-threaded programs
KW - Multi-threading
KW - Software testing
U2 - 10.1109/ICST.2017.24
DO - 10.1109/ICST.2017.24
M3 - Conference contribution/Paper
SN - 9781509060320
SP - 184
EP - 195
BT - 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)
PB - IEEE
ER -