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ASE 2002

What Makes Finite-State Models More (or Less) Testable?

David Owen, Tim Menzies, Bojan Cukic

DOI 10.1109/ase.2002.1115019dblp

Abstract

This paper studies how details of a particular model can effect the efficacy of a search for detects. We find that if the test method is fixed, we can identity classes of software that are more or less testable. Using a combination of model mutators and machine learning, we find that we can isolate topological features that significantly change the effectiveness of a defect detection tool. More specifically, we show that for one defect detection tool (a stochastic search engine) applied to a certain representation (finite state machines), we can increase the average odds of finding a defect from 69% to 91%. The method used to change those odds is quite general and should apply to other defect detection tools being applied to other representations.

BibTeX
@inproceedings{Owen-al:ASE02,
  author    = {David Owen and
               Tim Menzies and
               Bojan Cukic},
  title     = {What Makes {Finite-State} Models More (or Less) Testable?},
  booktitle = {ASE},
  pages     = {237--240},
  publisher = {{IEEE} Computer Society},
  year      = {2002},
}

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