LSRepair: Live Search of Fix Ingredients for Automated Program Repair

Kui Liu, Anil Koyuncu, Kisub Kim, Dongsun Kim, Tegawende F. Bissyande

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

65 Scopus citations

Abstract

Automated program repair (APR) has extensively been developed by leveraging search-based techniques, in which fix ingredients are explored and identified in different granularities from a specific search space. State-of-the approaches often find fix ingredients by using mutation operators or leveraging manually-crafted templates. We argue that the fix ingredients can be searched in an online mode, leveraging code search techniques to find potentially-fixed versions of buggy code fragments from which repair actions can be extracted. In this study, we present an APR tool, LSRepair, that automatically explores code repositories to search for fix ingredients at the method-level granularity with three strategies of similar code search. Our preliminary evaluation shows that code search can drive a faster fix process (some bugs are fixed in a few seconds). LSRepair helps repair 19 bugs from the Defects4J benchmark successfully. We expect our approach to open new directions for fixing multiple-lines bugs.

Original languageEnglish
Title of host publicationProceedings - 25th Asia-Pacific Software Engineering Conference, APSEC 2018
PublisherIEEE Computer Society
Pages658-662
Number of pages5
ISBN (Electronic)9781728119700
DOIs
StatePublished - 2 Jul 2018
Event25th Asia-Pacific Software Engineering Conference, APSEC 2018 - Nara, Japan
Duration: 4 Dec 20187 Dec 2018

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
Volume2018-December
ISSN (Print)1530-1362

Conference

Conference25th Asia-Pacific Software Engineering Conference, APSEC 2018
Country/TerritoryJapan
CityNara
Period4/12/187/12/18

Keywords

  • code search
  • fix ingredients
  • Program repair

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