Guiding random test generation with program analysis

Lei Ma, Cyrille Artho, Cheng Zhang, Hiroyuki Sato, Johannes Gmeiner, Rudolf Ramler

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

1 Citation (Scopus)


Random test generation is effective in creating method sequences for exercising the software under test. However, black-box approaches for random testing are known to suffer from low code coverage and limited defect detection ability. Analyzing the software under test and using the extracted knowledge to guide test generation can help to overcome these limitations. We developed a random test case generator augmented by a combination of six static and dynamic program analysis techniques. Our tool GRT (Guided Random Testing) has been evaluated on realworld software systems as well as Defects4J benchmarks. It outperformed related approaches in terms of code coverage, mutation score and detected faults. The results show a considerable improvement potential of random test generation when combined with advanced analysis techniques.

Original languageEnglish
Title of host publicationSoftware Engineering 2016
EditorsJens Knoop, Uwe Zdun
PublisherGesellschaft fur Informatik (GI)
Number of pages2
ISBN (Electronic)9783885796466
Publication statusPublished - 2016
Externally publishedYes
EventSoftware Engineering-Konferenz, SE 2016 - Software Engineering Conference, SE 2016 - Wien, Austria
Duration: Feb 23 2016Feb 26 2016

Publication series

NameLecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
ISSN (Print)1617-5468


ConferenceSoftware Engineering-Konferenz, SE 2016 - Software Engineering Conference, SE 2016

All Science Journal Classification (ASJC) codes

  • Computer Science Applications


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