The Daikon system for dynamic detection of likely invariants

Download: PDF, Daikon implementation.

“The Daikon system for dynamic detection of likely invariants” by Michael D. Ernst, Jeff H. Perkins, Philip J. Guo, Stephen McCamant, Carlos Pacheco, Matthew S. Tschantz, and Chen Xiao. Science of Computer Programming, vol. 69, no. 1--3, Dec. 2007, pp. 35-45.


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 = ax+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

Download: PDF, Daikon implementation.

BibTeX entry:

   author = {Michael D. Ernst and Jeff H. Perkins and Philip J. Guo and
	Stephen McCamant and Carlos Pacheco and Matthew S. Tschantz and
	Chen Xiao},
   title = {The {Daikon} system for dynamic detection of likely invariants},
   journal = {Science of Computer Programming},
   volume = {69},
   number = {1--3},
   pages = {35--45},
   month = dec,
   year = {2007}

(This webpage was created with bibtex2web.)

Back to Michael Ernst's publications.