Context mining-based fault analysis of collaboration failures in cyber-physical system-of-systems

Abstract

and (4) an absence of an end-to-end solution from pattern analysis to the fault identification. To overcome these limitations, we define a context model for CPSoS logs and propose an FII pattern mining algorithm covering the main features of the sequential analysis, an overlapping clustering technique for multiple pattern mining, and a pattern-based fault localization method. In experiments conducted on several CPSoS examples, we found that the proposed approach achieved the highest context mining accuracy and clustering precision. We also checked that the proposed localization method presented the highest fault localization efficacy. We newly detected undiscovered failure scenarios and bugs in this study. The findings of this study can facilitate the accurate analysis of collaboration failures.

A cyber-physical system-of-systems (CPSoS) tries to achieve prominent goals, such as increasing road capacity in platooning that groups driving vehicles in proximity, through interactions between constituent systems (CSs). However, during the collaboration of CSs, unintended interference in interactions causes collaboration failures that may lead to catastrophic damage, particularly for the safety-critical CPSoS. It is necessary to analyze the failure-inducing interactions (FII) during the collaboration and resolve the root causes of failures. Existing studies have utilized pattern-mining techniques to analyze system failures from logs. However, they have four limitations when applied to collaboration failures: (1) limited data model to handle discrete and continuous logs generated from CPSoS

(2) limited coverage of main features required to sequentially analyze the logs

(3) limitations on identifying multiple failure patterns in a single log