The Daikon system for dynamic detection of likely invariants

Michael D. Ernst, Jeff H. Perkins, Philip J. Guo, Stephen McCamant, Carlos Pacheco, Matthew S. Tschantz, Chen Xiao

Research output: Contribution to journalArticlepeer-review

591 Scopus citations

Abstract

Daikon is an implementation of dynamic detection of likely invariants; that is, the Daikon invariant detector reports likely program invariants. An invariant is a property that holds at a certain point or points in a program; these are often used in assert statements, documentation, and formal specifications. Examples include being constant (x = a), non-zero (x ≠ 0), being in a range (a ≤ x ≤ b), linear relationships (y = a x + b), ordering (x ≤ y), functions from a library (x = fn (y)), containment (x ∈ y), sortedness (x is sorted), and many more. Users can extend Daikon to check for additional invariants. Dynamic invariant detection runs a program, observes the values that the program computes, and then reports properties that were true over the observed executions. Dynamic invariant detection is a machine learning technique that can be applied to arbitrary data. Daikon can detect invariants in C, C++, Java, and Perl programs, and in record-structured data sources; it is easy to extend Daikon to other applications. Invariants can be useful in program understanding and a host of other applications. Daikon's output has been used for generating test cases, predicting incompatibilities in component integration, automating theorem proving, repairing inconsistent data structures, and checking the validity of data streams, among other tasks. Daikon is freely available in source and binary form, along with extensive documentation, at http://pag.csail.mit.edu/daikon/.

Original languageEnglish (US)
Pages (from-to)35-45
Number of pages11
JournalScience of Computer Programming
Volume69
Issue number1-3
DOIs
StatePublished - Dec 1 2007

Keywords

  • Daikon
  • Dynamic analysis
  • Dynamic invariant detection
  • Inductive logic programming
  • Inference
  • Invariant
  • Likely invariant
  • Program understanding
  • Specification
  • Specification mining

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