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 specifications. Examples include being constant $(x=a)$, non-zero $(x eq 0)$, being in a range $(a leq x leq b)$, linear relationships $(y=ax+b)$, ordering $(x leq y)$, functions from a library ($x=fn(y))$, containment $(x in y)$, 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.

References in zbMATH (referenced in 36 articles , 1 standard article )

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  1. Alpuente, María; Pardo, Daniel; Villanueva, Alicia: Symbolic abstract contract synthesis in a rewriting framework (2017)
  2. Smallbone, Nicholas; Johansson, Moa; Claessen, Koen; Algehed, Maximilian: Quick specifications for the busy programmer (2017)
  3. Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard: Active learning for extended finite state machines (2016)
  4. Lin, Fangzhen: A formalization of programs in first-order logic with a discrete linear order (2016)
  5. Sharma, Rahul; Aiken, Alex: From invariant checking to invariant inference using randomized search (2016)
  6. Kirchner, Florent; Kosmatov, Nikolai; Prevosto, Virgile; Signoles, Julien; Yakobowski, Boris: Frama-C: a software analysis perspective (2015) ioport
  7. Cavalcanti, Ana; King, Steve; O’Halloran, Colin; Woodcock, Jim: Test-data generation for control coverage by proof (2014)
  8. David R. Cok: OpenJML: Software verification for Java 7 using JML, OpenJDK, and Eclipse (2014) arXiv
  9. Ghezzi, Carlo; Mocci, Andrea; Sangiorgio, Mario: Synthesis of infinite-state abstractions and their use for software validation (2014)
  10. Isberner, Malte; Howar, Falk; Steffen, Bernhard: Learning register automata: from languages to program structures (2014)
  11. Llano, Maria Teresa; Ireland, Andrew; Pease, Alison: Discovery of invariants through automated theory formation (2014) ioport
  12. Zhang, Zhihai; Kapur, Deepak: On invariant checking (2013)
  13. Aarts, Fides; Heidarian, Faranak; Kuppens, Harco; Olsen, Petur; Vaandrager, Frits: Automata learning through counterexample guided abstraction refinement (2012)
  14. Christakis, Maria; Müller, Peter; Wüstholz, Valentin: Collaborative verification and testing with explicit assumptions (2012)
  15. da Costa, Umberto Souza; Moreira, Anamaria Martins; Musicante, Martin A.; Souza Neto, Plácido A.: JCML: A specification language for the runtime verification of Java card programs (2012)
  16. Ferrara, Pietro; Müller, Peter: Automatic inference of access permissions (2012)
  17. Ghardallou, Wided; Mraihi, Olfa; Louhichi, Asma; Jilani, Lamia Labed; Bsaies, Khaled; Mili, Ali: A versatile concept for the analysis of loops (2012)
  18. Howar, Falk; Steffen, Bernhard; Jonsson, Bengt; Cassel, Sofia: Inferring canonical register automata (2012)
  19. Ficco, Massimo; Pietrantuono, Roberto; Russo, Stefano: Bug localization in test-driven development (2011) ioport
  20. Tian, Donghai; Xiong, Xi; Hu, Changzhen; Liu, Peng: Policy-centric protection of OS kernel from vulnerable loadable kernel modules (2011)

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