Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs

Cited 23 time in webofscience Cited 22 time in scopus
  • Hit : 391
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Yunhoko
dc.contributor.authorMun, Seokhyunko
dc.contributor.authorYoo, Shinko
dc.contributor.authorKim, Moonzooko
dc.date.accessioned2019-12-13T08:20:08Z-
dc.date.available2019-12-13T08:20:08Z-
dc.date.created2019-11-07-
dc.date.created2019-11-07-
dc.date.issued2019-10-
dc.identifier.citationACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, v.28, no.4, pp.1 - 34-
dc.identifier.issn1049-331X-
dc.identifier.urihttp://hdl.handle.net/10203/269046-
dc.description.abstractFinding the root cause of a bug requires a significant effort from developers. Automated fault localization techniques seek to reduce this cost by computing the suspiciousness scores (i.e., the likelihood of program entities being faulty). Existing techniques have been developed by utilizing input features of specific types for the computation of suspiciousness scores, such as program spectrum or mutation analysis results. This article presents a novel learn-to-rank fault localization technique called PRecise machiNe-learning-based fault loCalization tEchnique (PRINCE). PRINCE uses genetic programming (GP) to combine multiple sets of localization input features that have been studied separately until now. For dynamic features, PRINCE encompasses both Spectrum Based Fault Localization (SBFL) and Mutation Based Fault Localization (MBFL) techniques. It also uses static features, such as dependency information and structural complexity of program entities. All such information is used by GP to train a ranking model for fault localization. The empirical evaluation on 65 real-world faults from CoREBench, 84 artificial faults from SIR, and 310 real-world faults from Defects4J shows that PRINCE outperforms the state-of-the-art SBFL, MBFL, and learn-to-rank techniques significantly. PRINCE localizes a fault after reviewing 2.4% of the executed statements on average (4.2 and 3.0 times more precise than the best of the compared SBFL and MBFL techniques, respectively). Also, PRINCE ranks 52.9% of the target faults within the top ten suspicious statements.-
dc.languageEnglish-
dc.publisherASSOC COMPUTING MACHINERY-
dc.titlePrecise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs-
dc.typeArticle-
dc.identifier.wosid000496207000006-
dc.identifier.scopusid2-s2.0-85074644798-
dc.type.rimsART-
dc.citation.volume28-
dc.citation.issue4-
dc.citation.beginningpage1-
dc.citation.endingpage34-
dc.citation.publicationnameACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY-
dc.identifier.doi10.1145/3345628-
dc.contributor.localauthorYoo, Shin-
dc.contributor.localauthorKim, Moonzoo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorFault localization-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthormutation analysis-
dc.subject.keywordAuthorsource file characteristics-
dc.subject.keywordPlusSOFTWARE-
dc.subject.keywordPlusMETRICS-
dc.subject.keywordPlusSUITE-
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 23 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0