The Daikon system for dynamic detection of likely invariants

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 speci...

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Veröffentlicht in:Science of computer programming Jg. 69; H. 1; S. 35 - 45
Hauptverfasser: Ernst, Michael D., Perkins, Jeff H., Guo, Philip J., McCamant, Stephen, Pacheco, Carlos, Tschantz, Matthew S., Xiao, Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2007
Schlagworte:
Daikon
Dynamic analysis
Dynamic invariant detection
Inductive logic programming
Inference
Invariant
Likely invariant
Program understanding
Specification
Specification mining
Invariant
Program understanding
Dynamic analysis
Dynamic invariant detection
Inference
Specification
Specification mining
Inductive logic programming
Likely invariant
Daikon
ISSN:0167-6423, 1872-7964
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Beschreibung
Zusammenfassung: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/.
ISSN:0167-6423
1872-7964
DOI:10.1016/j.scico.2007.01.015