Model-based Fault Classification for Automotive Software

TL;DR

This paper introduces a model-based fault classification method for automotive software that helps engineers identify and group bugs in failing tests using formal methods like symbolic execution and Hoare logic.

Contribution

It presents a novel fault classification algorithm that leverages formal methods to assist engineers in diagnosing bugs in automotive software testing.

Findings

Effective fault localization through divergence analysis

Automated fault explanation identifies minimal cause sets

Test grouping based on similar failure reasons

Abstract

Intensive testing using model-based approaches is the standard way of demonstrating the correctness of automotive software. Unfortunately, state-of-the-art techniques leave a crucial and labor intensive task to the test engineer: identifying bugs in failing tests. Our contribution is a model-based classification algorithm for failing tests that assists the engineer when identifying bugs. It consists of three steps. (i) Fault localization replays the test on the model to identify the moment when the two diverge. (ii) Fault explanation then computes the reason for the divergence. The reason is a subset of actions from the test that is sufficient for divergence. (iii) Fault classification groups together tests that fail for similar reasons. Our approach relies on machinery from formal methods: (i) symbolic execution, (ii) Hoare logic and a new relationship between the intermediary assertions constructed for a test, and (iii) a new relationship among Hoare proofs. A crucial aspect in automotive software is timing requirements, for which we develop appropriate Hoare logic theory. We also briefly report on our prototype implementation for the CAN bus Unified Diagnostic Services in an industrial project.