Classification of randomly generated test cases

Cyrille Artho, Lei Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Random test case generation produces relatively diverse test sequences, but the validity of the test verdict is always uncertain. Because tests are generated without taking the specification and documentation into account, many tests are invalid. To understand the prevalent types of successful and invalid tests, we present a classification of 56 issues that were derived from 208 failed, randomly generated test cases. While the existing workflow successfully eliminated more than half of the tests as irrelevant, half of the remaining failed tests are false positives. We show that the new @NonNull annotation of Java 8 has the potential to eliminate most of the false positives, highlighting the importance of machine-readable documentation.

Original languageEnglish
Title of host publication2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-32
Number of pages4
ISBN (Electronic)9781509018550
DOIs
Publication statusPublished - May 20 2016
Externally publishedYes
Event1st International Workshop on Validating Software Tests, VST 2016 - Osaka, Japan
Duration: Mar 15 2016 → …

Publication series

Name2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016
Volume2016-January

Conference

Conference1st International Workshop on Validating Software Tests, VST 2016
Country/TerritoryJapan
CityOsaka
Period3/15/16 → …

All Science Journal Classification (ASJC) codes

  • Software

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