Learning techniques for software verification and validation

  • Authors:

  • Corina S. Păsăreanu;Mihaela Bobaru

  • Affiliations:

  • Carnegie Mellon Silicon Valley, NASA Ames, Moffett Field, CA, USA,NASA Jet Propulsion Laboratory, Pasadena, CA;Carnegie Mellon Silicon Valley, NASA Ames, Moffett Field, CA, USA,NASA Jet Propulsion Laboratory, Pasadena, CA

  • Venue:
  • ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
  • Year:
  • 2012

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Abstract

Learning techniques are being used increasingly to improve software verification and validation activities. For example, automata learning techniques have been used for extracting behavioral models of software systems, e.g. [8]. These models can serve as formal documentation of the software and they can be verified using automated tools or used for model-based testing. Automata learning techniques have also been used for automating compositional verification, e.g. [3], for building abstractions of software behavior in the context of symbolic or parameterized model checking, e.g. [9] or for the automatic inference and security analysis of botnet protocols, e.g. [1]. This Special Track aims at bringing together researchers and practitioners working on the integration of learning techniques in verification and validation activities for software systems. The Special Track is part of the 2012 International Symposium on Leveraging Applications of Formal Methods, Verification, and Validation (ISoLA).