Software Safety Demonstration and Idemnification

TL;DR

This paper introduces a statistical, risk-based approach for verifying safety-critical software, aligning software safety assurance with hardware practices through demonstration and indemnification techniques.

Contribution

It develops a quantitative, risk-based product assurance method for safety-critical software, resolving conflicts in the definition of risk within safety standards.

Findings

Demonstration generates random tests for software validation.

Indemnification converts test results into statistical parameters.

The approach achieves high confidence in software safety compliance.

Abstract

Computers may control safety-critical operations in machines having embedded software. This memoir proposes a regimen to verify such algorithms at prescribed levels of statistical confidence. The United States Department of Defense standard for system safety engineering (MIL-STD-882E) defines development procedures for safety-critical systems. However, a problem exists: the Standard fails to distinguish quantitative product assurance technique from categorical process assurance method for software development. Resulting is conflict in the technical definition of the term risk. The primary goal here is to show that a quantitative risk-based product assurance method exists and is consistent with hardware practice. Discussion appears in two major parts: theory, which shows the relationship between automata and software; and application, which covers demonstration and indemnification. Demonstration is a technique for generating random tests; indemnification converts pass/fail test results to compound Poisson parameters (severity and intensity). Together, demonstration and indemnification yield statistical confidence that safety-critical code meets design intent. Statistical confidence is the keystone of quantitative product assurance. A secondary goal is resolving the conflict over the term risk. The first meaning is an accident model known in mathematics as the compound Poisson stochastic process, and so is called statistical risk. Various of its versions underlie the theories of safety and reliability. The second is called developmental risk. It considers software autonomy, which considers time until manual recovery of control. Once these meanings are separated, MIL-STD-882 can properly support either formal quantitative safety assurance or empirical process robustness, which differ in impact.