Light-Weight Techniques for Improving the Controllability and Efficiency of ISA-Level Fault Injection Tools
Paper in proceeding, 2017

ISA-level fault injection, i.e. the injection of bit-flip faults in Instruction Set Architecture (ISA) registers and main memory words, is widely used for studying the impact of transient and intermittent hardware faults. ISA-level fault injection tools can be characterized by different properties such as repeatability, observability, reachability, intrusiveness, efficiency and controllability. This paper presents two pre-injection analysis techniques that improve controllability and efficiency using object code analysis. To improve controllability, we propose a technique for identifying the type of data that is stored in a potential target location. This allows the user to selectively direct fault injections to addresses, data and/or control information. Experimental results show that the data type of 84-100% of the targets locations in 8 programs were successfully identified by this technique. The second technique improves efficiency by fault pruning, i.e., by avoiding injection of faults that is known a priori to be detected by the tested system. This technique leverage the fact that faults in certain bits in the program counter and the stack pointer are always detected by machine exceptions. We show that exclusion of these bits from the fault space could significantly prune the fault space and reduce the time it takes to conduct a fault injection campaign.

data type identification

ISA-level fault injection

controllability

fault space optimization

efficiency

Author

Behrooz Sangchoolie

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Roger Johansson

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Johan Karlsson

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC

15410110 (ISSN)

68-77 7920598
978-1-5090-5652-1 (ISBN)

Subject Categories

Computer Engineering

DOI

10.1109/PRDC.2017.18

ISBN

978-1-5090-5652-1

More information

Created

10/8/2017