Predicting and Explaining Automatic Testing Tool Effectiveness

Daniel, Brett; Boshernitsan, Marat

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https://hdl.handle.net/2142/11448 Copy

Description

Title

Predicting and Explaining Automatic Testing Tool Effectiveness

Author(s)

• Daniel, Brett
• Boshernitsan, Marat

Issue Date

2008-04

Keyword(s)

computer science

Abstract

Automatic white-box test generation is a challenging problem. Many existing tools rely on complex code analyses and heuristics. As a result, structural features of an input program may impact tool effectiveness in ways that tool users and designers may not expect or understand. We develop a technique that uses structural program metrics to both predict and explain the test coverage achieved by three automatic test generation tools. We use coverage and structural metrics extracted from 11 software projects to train several decision-tree classifiers. These classifiers can predict high or low coverage with success rates of 82% to 94%. In addition, they show tool users and designers the program structures that impact tool effectiveness.

Type of Resource

text

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http://hdl.handle.net/2142/11448

Copyright and License Information

You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).

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