STARTS: STAtic regression test selection

Owolabi Legunsen, August Shi, Darko Marinov

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

Abstract

Regression testing is an important part of software development, but it can be very time consuming. Regression test selection (RTS) aims to speed up regression testing by running only impacted tests - the subset of tests that can change behavior due to code changes. We present STARTS, a tool for STAtic Regression Test Selection. Unlike dynamic RTS, STARTS requires no code instrumentation or runtime information to find impacted tests; instead, STARTS uses only compile-time information. Specifically, STARTS builds a dependency graph of program types and finds, as impacted, tests that can reach some changed type in the transitive closure of the dependency graph. STARTS is a Maven plugin that can be easily integrated into any Maven-based Java project. We find that STARTS selects on average 35.2% of tests, leading to an end-to-end runtime that is on average 81.0% of running all the tests. A video demo of STARTS can be found at https://youtu.be/PCNtk8jphrM.

Original languageEnglish (US)
Title of host publicationASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering
EditorsTien N. Nguyen, Grigore Rosu, Massimiliano Di Penta
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages949-954
Number of pages6
ISBN (Electronic)9781538626849
DOIs
StatePublished - Nov 20 2017
Event32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017 - Urbana-Champaign, United States
Duration: Oct 30 2017Nov 3 2017

Publication series

NameASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering

Other

Other32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017
Country/TerritoryUnited States
CityUrbana-Champaign
Period10/30/1711/3/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Optimization

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