Search-based Trace Diagnostic

TL;DR

This paper introduces a novel search-based technique for diagnosing trace violations in cyber-physical systems, leveraging evolutionary algorithms to efficiently identify causes of requirement breaches.

Contribution

It proposes SBTD, a new evolutionary search-based method for trace diagnosis in CPS, and implements a tool called Diagnosis for signal-based temporal logic requirements.

Findings

Diagnosis produces informative diagnoses in most experiments

SBTD is effective in identifying causes of trace violations

The approach is efficient in practical time for most cases

Abstract

Cyber-physical systems (CPS) development requires verifying whether system behaviors violate their requirements. This analysis often considers system behaviors expressed by execution traces and requirements expressed by signal-based temporal properties. When an execution trace violates a requirement, engineers need to solve the trace diagnostic problem: They need to understand the cause of the breach. Automated trace diagnostic techniques aim to support engineers in the trace diagnostic activity. This paper proposes search-based trace-diagnostic (SBTD), a novel trace-diagnostic technique for CPS requirements. Unlike existing techniques, SBTD relies on evolutionary search. SBTD starts from a set of candidate diagnoses, applies an evolutionary algorithm iteratively to generate new candidate diagnoses (via mutation, recombination, and selection), and uses a fitness function to determine the qualities of these solutions. Then, a diagnostic generator step is performed to explain the cause of the trace violation. We implemented Diagnosis, an SBTD tool for signal-based temporal logic requirements expressed using the Hybrid Logic of Signals (HLS). We evaluated Diagnosis by performing 34 experiments for 17 trace-requirements combinations leading to a property violation and by assessing the effectiveness of SBTD in producing informative diagnoses and its efficiency in generating them on a time basis. Our results confirm that Diagnosis can produce informative diagnoses in practical time for most of our experiments (33 out of 34).