Trace Diagnostics for Signal-based Temporal Properties

Trace checking is a verification technique widely used in Cyber-physical system (CPS) development, to verify whether execution traces satisfy or violate properties expressing system requirements. Often these properties characterize complex signal behaviors and are defined using domain-specific languages, such as SB-TemPsy-DSL, a pattern-based specification language for signal-based temporal properties. Most of the trace-checking tools only yield a Boolean verdict. However, when a property is violated by a trace, engineers usually inspect the trace to understand the cause of the violation; such manual diagnostic is time-consuming and error-prone. Existing approaches that complement trace-checking tools with diagnostic capabilities either produce low-level explanations that are hardly comprehensible by engineers or do not support complex signal-based temporal properties. In this paper, we propose TD-SB-TemPsy, a trace-diagnostic approach for properties expressed using SB-TemPsy-DSL. Given a property and a trace that violates the property, TD-SB-TemPsy determines the root cause of the property violation. TD-SB-TemPsy relies on the concepts of violation cause , which characterizes one of the behaviors of the system that may lead to a property violation, and diagnoses , which are associated with violation causes and provide additional information to help engineers understand the violation cause. As part of TD-SB-TemPsy, we propose a language-agnostic methodology to define violation causes and diagnoses. In our context, its application resulted in a catalog of 34 violation causes, each associated with one diagnosis, tailored to properties expressed in SB-TemPsy-DSL. We assessed the applicability of TD-SB-TemPsy on two datasets, including one based on a complex industrial case study. The results show that TD-SB-TemPsy could finish within a timeout of 1 min for $\approx 83.66\%$ as of the trace-property combinations in the industrial dataset, yielding a diagnosis in $\approx 99.84\%$ of these cases; moreover, it also yielded a diagnosis for all the trace-property combinations in the other dataset. These results suggest that our tool is applicable and efficient in most cases.