Learning for test prioritization: An industrial case study

Benjamin Busjaeger, Tao Xie

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

Abstract

Modern cloud-software providers, such as Salesforce.com, increasingly adopt large-scale continuous integration envi-ronments. In such environments, assuring high developer productivity is strongly dependent on conducting testing efficiently and effectively. Specifically, to shorten feedback cycles, test prioritization is popularly used as an optimiza-tion mechanism for ranking tests to run by their likelihood of revealing failures. To apply test prioritization in indus-trial environments, we present a novel approach (tailored for practical applicability) that integrates multiple existing techniques via a systematic framework of machine learning to rank. Our initial empirical evaluation on a large real-world dataset from Salesforce.com shows that our approach significantly outperforms existing individual techniques.

Original languageEnglish (US)
Title of host publicationFSE 2016 - Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
EditorsZhendong Su, Thomas Zimmermann, Jane Cleland-Huang
PublisherAssociation for Computing Machinery
Pages975-980
Number of pages6
ISBN (Electronic)9781450342186
DOIs
StatePublished - Nov 1 2016
Event24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016 - Seattle, United States
Duration: Nov 13 2016Nov 18 2016

Publication series

NameProceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering
Volume13-18-November-2016

Other

Other24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016
CountryUnited States
CitySeattle
Period11/13/1611/18/16

Keywords

  • Learning to rank
  • Regression testing
  • Test prioritization

ASJC Scopus subject areas

  • Software

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