Multivariate visualization in observation-based testing

Authors:
David Leon;Andy Podgurski;Lee J. White
Affiliations:
Electrical Engineering and Computer Science Department, Case Western Reserve University, Olin Building, Cleveland, Ohio;Electrical Engineering and Computer Science Department, Case Western Reserve University, Olin Building, Cleveland, Ohio;Electrical Engineering and Computer Science Department, Case Western Reserve University, Olin Building, Cleveland, Ohio
Venue:
Proceedings of the 22nd international conference on Software engineering
Year:
2000

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Abstract

We explore the use of multivariate visualization techniques to support a new approach to test data selection, called observation-based testing. Applications of multivariate visualization are described, including: evaluating and improving synthetic tests; filtering regression test suites; filtering captured operational executions; comparing test suites; and assessing bug reports. These applications are illustrated by the use of correspondence analysis to analyze test inputs for the GNU GCC compiler.