Author(s)

M. Darricau & H. Hadj-Mabrouk

Abstract

This paper presents a mock-up of a tool for storing and assessing Software Error Effect Analysis (SEEA) for the automatic devices safety of terrestrial guided transport systems. SEEA is an inductive process which attempts to determine the impacts and severity of software failures. The purpose of our work is to exploit historical SEEA, which have already been carried out on approved safety-critical software, in order to assess SEEA of a new software. The production of this mock-up, in the process of validation, involves the use of Case-Based Reasoning (CBR). This is one of the reasoning types used in artificial intelligence for machine learning. The basic principle of CBR is to deal with a new problem by remembering similar experienc

Keywords