CoMID: Context-Based Multiinvariant Detection for Monitoring Cyber-Physical Software

Yi Qin, Tao Xie, Chang Xu, Angello Astorga, Jian Lu

Research output: Contribution to journalArticlepeer-review


Cyber-physical software delivers context-aware services through continually interacting with its physical environment and adapting to the changing surroundings. However, when the software's assumptions on the environment no longer hold, the interactions can introduce errors for leading to unexpected behaviors and even system failures. One promising solution to this problem is to conduct runtime monitoring of invariants. Violated invariants reflect latent erroneous states (i.e., abnormal states that could lead to failures). In turn, monitoring when program executions violate the invariants can allow the software to take alternative measures to avoid danger. In this article, we present context-based Multiinvariant detection (CoMID), an approach that automatically infers invariants and detects abnormal states for cyber-physical programs. CoMID consists of two novel techniques, namely context-based trace grouping and multiinvariant detection. The former infers contexts to distinguish different effective scopes for CoMID's derived invariants, and the latter conducts ensemble evaluation of multiple invariants to detect abnormal states during runtime monitoring. We evaluate CoMID on real-world cyber-physical software. The results show that CoMID achieves a 5.7-28.2% higher true-positive rate and a 6.8-37.6% lower false-positive rate in detecting abnormal states, as compared with the existing approaches. When deployed in field tests, CoMID's runtime monitoring improves the success rate of cyber-physical software in its task executions by 15.3-31.7%.

Original languageEnglish (US)
Article number8809357
Pages (from-to)106-123
Number of pages18
JournalIEEE Transactions on Reliability
Issue number1
StatePublished - Mar 1 2020


  • Abnormal-state detection
  • cyber-physical software
  • invariant generation

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'CoMID: Context-Based Multiinvariant Detection for Monitoring Cyber-Physical Software'. Together they form a unique fingerprint.

Cite this