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Quantitative reactive modeling and verification.

Thomas A Henzinger1

  • 1IST Austria, Klosterneuburg, Austria.

Computer Science (Berlin, Germany)
|April 13, 2016
PubMed
Summary
This summary is machine-generated.

Formal verification needs better methods beyond simple correctness. This research introduces quantitative fitness measures for assessing software function, performance, and robustness, especially for reactive systems.

Keywords:
Embedded systemsFormal methodsProgram verificationSystems biology

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Area of Science:

  • Computer Science
  • Systems Biology
  • Formal Methods

Background:

  • Formal verification traditionally uses binary correctness, classifying software as either correct or incorrect.
  • This binary approach is insufficient for nuanced assessment of complex software behavior against multiple criteria.
  • Reactive programs, like concurrent processes, require more sophisticated evaluation methods.

Purpose of the Study:

  • To introduce and develop a theory of quantitative fitness measures for reactive models.
  • To generalize successful qualitative reactive modeling paradigms (compositionality, abstraction, model checking, synthesis) into a quantitative framework.
  • To evaluate the theory in software/hardware engineering and systems biology.

Main Methods:

  • Developing quantitative fitness measures for function, performance, and robustness of reactive programs.
  • Generalizing established qualitative reactive modeling techniques to a quantitative setting.
  • Applying the developed theory and measures to systems biology for hypothesis testing.

Main Results:

  • Proposes a novel framework for quantitative assessment of software, moving beyond binary correctness.
  • Lays the groundwork for a theory of quantitative fitness measures for reactive systems.
  • Demonstrates potential applications in both engineering and biological systems.

Conclusions:

  • Quantitative fitness measures offer a more practical and nuanced approach to software quality assessment than traditional binary correctness.
  • The proposed theory aims to extend successful formal verification paradigms to a quantitative domain.
  • The research has implications for improving software engineering and advancing systems biology research.