Semester of Graduation

Fall 2017

Degree

Master of Science in Computer Science (MSCS)

Department

Computer Science

Document Type

Thesis

Abstract

Given a program analysis problem that consists of a program and a property of interest, we use a data-driven approach to automatically construct a sequence of abstractions that approach an ideal abstraction suitable for solving that problem. This process begins with an infinite concrete domain that maps to a finite abstract domain defined by statistical procedures resulting in a clustering mixture model. Given a set of properties expressed as formulas in a restricted and bounded variant of CTL, we can test the success of the abstraction with respect to a predefined performance level. In addition, we can perform iterative abstraction-refinement of the clustering by tuning hyperparameters that determine the accuracy of the cluster representations (abstract states) and determine the number of clusters. Our methodology yields an induced abstraction and refinement procedure for property verification.

Date

8-20-2017

Committee Chair

Mukhopadhyay, Supratik

DOI

10.31390/gradschool_theses.4339

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