By Topic

Inference graphs: a computational structure supporting generation of customizable and correct analysis components

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
L. K. Dillon ; Dept. of Comput. Sci. & Eng., Michigan State Univ., USA ; R. E. K. Stirewalt

Amalia is a generator framework for constructing analyzers for operationally defined formal notations. These generated analyzers are components that are designed for customization and integration into a larger environment. The customizability, and efficiency of Amalia analyzers owe to a computational structure called an inference graph. This paper describes this structure, how inference graphs enable Amalia to generate analyzers for operational specifications, and how we build in assurance. On another level, this paper illustrates how to balance the need for assurance, which typically implies a formal proof obligation, against other design concerns, whose solutions leverage design techniques that are not (yet) accompanied by mature proof methods. We require Amalia-generated designs to be transparent with respect to the formal semantic models upon which they are based. Inference graphs are complex structures that incorporate many design optimizations. While not formally verifiable, their fidelity with respect to a formal operational semantics can be discharged by inspection.

Published in:

IEEE Transactions on Software Engineering  (Volume:29 ,  Issue: 2 )