'Invariants' presentation slideshows

Using Types For Software Verification

983 views • 86 slides

Invariants to convolution

992 views • 82 slides

A Specification Language

A Specification Language. UML: www.rational.com/uml Model architecture Object Constraint Language A part of UML 1.1. Testing and OCL. A class model is not enough for a precise and unambiguous specification. Useful to express specifications, test requirements and test specifications

962 views • 79 slides

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816 views • 58 slides

Dynamically Discovering Likely Program Invariants to Support Program Evolution

Dynamically Discovering Likely Program Invariants to Support Program Evolution. Presented By: Wes Toland, Geoff Gerfin. Michael D. Ernst, Jake Cockrell, William G. Griswold, David Notkin. Outline. Introduction Overview Code Instrumentation Data Trace Generation Inferring Invariants

711 views • 51 slides

SYSE 802

510 views • 41 slides

Invariants to affine transform

Invariants to affine transform. What is affine transform ?. Why is affine transform important?. Affine transform is a good approximation of projective transform Projective transform describes a perspective projection of 3-D objects onto 2-D plane by a central camera. Affine moment invariants.

518 views • 31 slides

Using Likely Program Invariants to Detect Hardware Errors

Using Likely Program Invariants to Detect Hardware Errors. Swarup Kumar Sahoo , Man-Lap Li, Pradeep Ramachandran, Sarita Adve, Vikram Adve, Yuanyuan Zhou Department of Computer Science University of Illinois, Urbana-Champaign swat@cs.uiuc.edu. Motivation.

404 views • 30 slides

Using Likely Program Invariants to Detect Hardware Errors

Using Likely Program Invariants to Detect Hardware Errors. Swarup Kumar Sahoo , Man-Lap Li, Pradeep Ramachandran, Sarita Adve, Vikram Adve, Yuanyuan Zhou Department of Computer Science University of Illinois, Urbana-Champaign swat@cs.uiuc.edu. Motivation.

305 views • 30 slides

Invariant-Based Face Recognition

Invariant-Based Face Recognition. Nigel Boston Departments of Mathematics and Electrical and Computer Engineering University of Wisconsin, Madison. Credits. Professor Yu Hen Hu Wei-Yang Lin Ryan Kin Hong Wong UW Face Recognition Group www.ece.wisc.edu/~facerec/

307 views • 29 slides

Contracts for Concurrency - Contracts & Inheritance

Contracts for Concurrency - Contracts & Inheritance. Aryabrata Basu University of Georgia. Preconditions. In sequential context: precondition is correctness condition In concurrent context: feature call and feature application do not usually coincide

401 views • 27 slides

Lecture 3: Camera Rotations and Homographies

Lecture 3: Camera Rotations and Homographies. Camera Center of Projection. (0,0,0). Recap: Normalized Camera. A camera is normalized if the units are chosen so that the focal length is 1. Then image coordinates (x,y) = X/Z,Y/Z

441 views • 27 slides

Automatic Inference and Enforcement of Kernel Data Structure Invariants

Automatic Inference and Enforcement of Kernel Data Structure Invariants. Arati Baliga, Vinod Ganapathy and Liviu Iftode Department of Computer Science Rutgers University. Rootkits, the growing threat !. Computer systems today face a realistic and growing threat from rootkits.

303 views • 22 slides

Linear Theory of Ionization Cooling in 6D

Linear Theory of Ionization Cooling in 6D. Kwang-Je Kim & Chun-xi Wang University of Chicago and Argonne National Laboratory Cooling Theory/Simulation Day Illinois Institute of Technology February 5, 2002. Theory development . . . . . . . . . . . . . . . . . . . Kwang-Je Kim

428 views • 21 slides

CSE 331 Software Design & Implementation midterm review

289 views • 18 slides

Dynamically Discovering Likely Program Invariants to Support Program Evolution

Dynamically Discovering Likely Program Invariants to Support Program Evolution. Michael D. Ernst, Jake Cockrell, William G. Griswold, David Notkin Presented by: Nick Rutar. Program Invariants. Useful in software development Protect programmers from making errant changes

289 views • 18 slides

Aspects and Modularity: The Hope and the Challenge

Aspects and Modularity: The Hope and the Challenge. Jonathan Aldrich Institute for Software Research International School of Computer Science Carnegie Mellon University. Why Modularity?. Protect client code from changes to design decisions encapsulated within a module [Parnas, others]

248 views • 14 slides

DDEC: Data-Driven Equivalence Checking

Rahul Sharma, Eric Schkufza , Berkeley Churchill, Alex Aiken. DDEC: Data-Driven Equivalence Checking. Equivalence checking. Prove two programs are equivalent Compiler optimizations Validate refactorings Cross checking different implementations Old and well studied problem

318 views • 12 slides

ESCJ 15: Design issues for ESC/Java April 2nd, 1997

172 views • 4 slides

PRECIS: Inferring Invariants Using Program Path Guided Clustering

PRECIS: Inferring Invariants Using Program Path Guided Clustering. Parth Sagdeo , Viraj Athavale , Sumant Kowshik , Shobha Vasudevan Coordinated Sciences Laboratory, University of Illinois at Urbana-Champaign. Test cases. Clusters with output functions. Trace data. Data generation.

123 views • 1 slides