Antirandom Testing: A Distance-Based Approach

Abstract

Random testing requires each test to be selected randomly regardless of the tests previously applied. This paper introduces the concept of antirandom testing where each test applied is chosen such that its total distance from all previous tests is maximum. This spans the test vector space to the maximum extent possible for a given number of vectors. An algorithm for generating antirandom tests is presented. Compared with traditional pseudorandom testing, antirandom testing is found to be very effective when a high-fault coverage needs to be achieved with a limited number of test vectors. The superiority of the new approach is even more significant for testing bridging faults.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 17, 2008
Source ID
10.1155/2008/165709

Entities

People

  • Anura P. Jayasumana
  • Shen Hui Wu
  • Sridhar Jandhyala
  • Yashwant K. Malaiya

Organizations

  • Colorado State University
  • Office of Naval Research

Tags

Fields of Study

  • Engineering

Readers

  • Neural Network Machine Learning.
  • Software Engineering
  • Statistical inference.

Technology Areas

  • Space