Online Efficient Predictive Safety Analysis of Multithreaded Programs

Abstract

An automated and configurable technique for runtime safety analysis of multithreaded programs is presented, which is able to predict safety violations from successful executions. Based on a user provided safety formal specification, the program is automatically instrumented to emit relevant state update events to an observer, which further checks them against the safety specification. The events are stamped with dynamic vector clocks, enabling the observer to infer a causal partial order on the state updates. All event traces that are consistent with this partial order, including the actual execution trace, are analyzed on-line and in parallel, and a warning is issued whenever there is a trace violating the specification. This technique can be therefore seen as a bridge between testing and model checking. To further increase scalability, a window in the state space can be specified, allowing the observer to infer the most probable runs. If the size of the window is 1 then only the received execution trace is analyzed, like in testing; if the size of the window is then all the execution traces are analyzed, such as in model checking.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA483547

Entities

People

  • Grigore Rosu
  • Gul Agha
  • Koushik Sen

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Automata
  • Computations
  • Computer Science
  • Construction
  • Information Operations
  • Language
  • Monitoring
  • Multithreading
  • Observers
  • Permutations
  • Safety
  • Safety Analysis
  • Sequences
  • Specifications
  • Standards

Fields of Study

  • Computer science
  • Engineering

Readers

  • Aviation Safety Risk Assessment.
  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space