Software Reliability in Nuclear Systems Arsen Papisyan Anthony Gwyn

Introduction Therac-25 – delivery of high radiation to patients Slammer worm – disabled safety parameter system at nuclear power system Edwin I. Hatch nuclear power plant – computer resets the control system Stuxnet – worm in Iran nuclear power plants

Introduction Cont’d Not always feasible to ensure complete software verification Not possible to test for every possibility Software testing only indicates the presence of faults and not its absence Goal: Estimate software reliability in critical systems Approach: Combines results of software verification and mutation testing

Critical Systems Smaller and focused Rugged and have fault tolerant features Designed with defense in mind Expected to have lower failure rates Meant to fail in fail-safe mode Not rely on human judgment or interaction to initiate safety action Written in stable programming languages

Software in Nuclear Reactors Safety critical: systems important to safety – ie safe shutdown and heat removal from core Safety related: systems which are required for the normal functioning of the safety systems Non-nuclear safety: no nuclear safety function Safety Systems in Power plants are categorized in levels from 1 to 4 – probability of failure – Level 1: 10^-2 – 10^-1 – Level 4: 10^-5 – 10^-4

The Need for a New Approach Reliability depends on structure and runtime information – Simulation or executions of software provide the runtime characteristics Traditional models assume availability of accurate and adequate software failure data – Difficult to collect Newly built plants with no failure history – Reliability estimation methods do not apply

Proposed Approach - Assumption

Assumptions Cont’d ROM to prevent malware modification Output depends only on the current inputs

Prerequisites for approach Precise and Verified Test Cases

Prerequisites for approach cont’d Mutation testing: fault injection technique – First order mutants are single faults K = number of mutants killed by test cases G = number of generated mutants E = equivalent mutants Test Adequacy Computation

Reliability estimation approach 1 Randomly induced faults 3 possible outcomes Reliability = Simple but results could be biased – If mutation testing is not effective enough, the large number of verified test cases may lead to higher reliability estimate

Reliability estimation approach 2 Pseudo code - allows for integration of operational profile in the reliability estimate – Ensures that un-verified test cases fail during mutation testing eliminating bias due to large number of verified test cases

Results

Conclusion Need common ways to demonstrate safety of computer bases systems in nuclear plants Results suggest that test adequacy is major factor in determining software reliability – Systems must have a high test coverage and mutation score

The End