Coverage rewarded: Test input generation via adaptation-based programming

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

13 Scopus citations

Abstract

This paper introduces a new approach to test input generation, based on reinforcement learning via easy to use adaptation-based programming. In this approach, a test harness can be written with little more effort than is involved in naïve random testing. The harness will simply map choices made by the adaptation-based programming (ABP) library, rather than pseudo-random numbers, into operations and parameters. Realistic experimental evaluation over three important fine-grained coverage measures (path, shape, and predicate coverage) shows that ABP-based testing is typically competitive with, and sometimes superior to, other effective methods for testing container classes, including random testing and shape-based abstraction.

Original languageEnglish (US)
Title of host publication2011 26th IEEE/ACM International Conference on Automated Software Engineering, ASE 2011, Proceedings
Pages380-383
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 26th IEEE/ACM International Conference on Automated Software Engineering, ASE 2011 - Lawrence, KS, United States
Duration: Nov 6 2011Nov 10 2011

Publication series

Name2011 26th IEEE/ACM International Conference on Automated Software Engineering, ASE 2011, Proceedings

Conference

Conference2011 26th IEEE/ACM International Conference on Automated Software Engineering, ASE 2011
Country/TerritoryUnited States
CityLawrence, KS
Period11/6/1111/10/11

Keywords

  • reinforcement learning
  • software testing

ASJC Scopus subject areas

  • Software

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