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 43 articles , 1 standard article )

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  1. Peleg, Hila; Itzhaky, Shachar; Shoham, Sharon; Yahav, Eran: Programming by predicates: a formal model for interactive synthesis (2020)
  2. Gupta, Shubhani; Saxena, Aseem; Mahajan, Anmol; Bansal, Sorav: Effective use of SMT solvers for program equivalence checking through invariant-sketching and query-decomposition (2018)
  3. Kiefer, Moritz; Klebanov, Vladimir; Ulbrich, Mattias: Relational program reasoning using compiler IR (2018)
  4. Kojima, Kensuke; Imanishi, Akifumi; Igarashi, Atsushi: Automated verification of functional correctness of race-free GPU programs (2018)
  5. Alpuente, María; Pardo, Daniel; Villanueva, Alicia: Symbolic abstract contract synthesis in a rewriting framework (2017)
  6. Smallbone, Nicholas; Johansson, Moa; Claessen, Koen; Algehed, Maximilian: Quick specifications for the busy programmer (2017)
  7. Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard: Active learning for extended finite state machines (2016)
  8. Lin, Fangzhen: A formalization of programs in first-order logic with a discrete linear order (2016)
  9. Sharma, Rahul; Aiken, Alex: From invariant checking to invariant inference using randomized search (2016)
  10. Kirchner, Florent; Kosmatov, Nikolai; Prevosto, Virgile; Signoles, Julien; Yakobowski, Boris: Frama-C: a software analysis perspective (2015) ioport
  11. Pedro Pinto, Rui Abreu, João M. P. Cardoso: Fault Detection in C Programs using Monitoring of Range Values: Preliminary Results (2015) arXiv
  12. Cavalcanti, Ana; King, Steve; O’Halloran, Colin; Woodcock, Jim: Test-data generation for control coverage by proof (2014)
  13. David R. Cok: OpenJML: Software verification for Java 7 using JML, OpenJDK, and Eclipse (2014) arXiv
  14. Ghezzi, Carlo; Mocci, Andrea; Sangiorgio, Mario: Synthesis of infinite-state abstractions and their use for software validation (2014) ioport
  15. Isberner, Malte; Howar, Falk; Steffen, Bernhard: Learning register automata: from languages to program structures (2014)
  16. Konur, Savas: Towards light-weight probabilistic model checking (2014)
  17. Llano, Maria Teresa; Ireland, Andrew; Pease, Alison: Discovery of invariants through automated theory formation (2014) ioport
  18. Zhang, Zhihai; Kapur, Deepak: On invariant checking (2013)
  19. Aarts, Fides; Heidarian, Faranak; Kuppens, Harco; Olsen, Petur; Vaandrager, Frits: Automata learning through counterexample guided abstraction refinement (2012)
  20. Christakis, Maria; Müller, Peter; Wüstholz, Valentin: Collaborative verification and testing with explicit assumptions (2012)

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