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Automatic Debugging of Design Faults in MapReduce Applications

dc.contributor.authorMorán Barbón, Jesús 
dc.contributor.authorBertolino, Antonia
dc.contributor.authorRiva Álvarez, Claudio A. de la 
dc.contributor.authorTuya González, Pablo Javier 
dc.date.accessioned2024-04-25T07:30:59Z
dc.date.available2024-04-25T07:30:59Z
dc.date.issued2024
dc.identifier.citationIEEE Transactions on Software Engineering, 50(4), p. 956-978 (2024); doi:10.1109/TSE.2024.3369766
dc.identifier.issn0098-5589
dc.identifier.urihttps://hdl.handle.net/10651/72406
dc.description.abstractAmong the current technologies to analyse large data, the MapReduce processing model stands out in Big Data. MapReduce is implemented in frameworks such as Hadoop, Spark or Flink that are able to manage the program executions according to the resources available at runtime. The developer should design the program in order to support all possible non-deterministic executions. However, the program may fail due to a design fault. Debugging these kinds of faults is difficult because the data are executed non-deterministically in parallel and the fault is not caused directly by the code, but by its design. This paper presents a framework called MRDebug which includes two debugging techniques focused on the MapReduce design faults. A spectrum-based fault localization technique locates the root cause of these faults analysing several executions of the test case, and a Delta Debugging technique isolates the data relevant to trigger the failure. An empirical evaluation with 13 programs shows that MRDebug is effective in debugging the faults, especially when the localization is done with the reduced data. In summary, MRDebug automatically provides valuable information to understand MapReduce design faults as it helps locate their root cause and obtains a minimal data that triggers the failure.spa
dc.description.sponsorshipThis work was supported in part by the project PID2019-105455GB-C32 under Grant MCIN/AEI/10.13039/501100011033 (Spain), in part by the project PID2022-137646OB-C32 under Grant MCIN/ AEI/10.13039/501100011033/FEDER, UE, and in part by the project RDS_2022-2024_2.1_Progetto_CYBER under Grant MASE/PTR_22_24_INT_ 2_1 (Italy).spa
dc.format.extentp. 956-978spa
dc.language.isoengspa
dc.publisherIEEEspa
dc.relation.ispartofIEEE Transactions on Software Engineering, 50 (4)spa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights© 2024 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDebugging aidsspa
dc.subjectTesting and debuggingspa
dc.titleAutomatic Debugging of Design Faults in MapReduce Applicationsspa
dc.typejournal articlespa
dc.identifier.doi10.1109/TSE.2024.3369766
dc.relation.projectIDPID2019-105455GB-C3spa
dc.relation.projectIDMCIN/AEI/10.13039/501100011033spa
dc.relation.projectIDPID2022-137646OB-C32spa
dc.relation.projectIDMCIN/ AEI/10.13039/501100011033/FEDERspa
dc.relation.projectIDRDS_2022-2024_2.1_Progetto_CYBERspa
dc.relation.projectIDMASE/PTR_22_24_INT_spa
dc.relation.publisherversionhttp://dx.doi.org/10.1109/TSE.2024.3369766spa
dc.rights.accessRightsopen accessspa
dc.relation.ispartofURIhttps://hdl.handle.net/10651/72731
dc.type.hasVersionVoRspa


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