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Ifyouregularlyworkwithopen-sourcecodeorproducesoftwareforalargeorganization,you'realreadyfamiliarwithmanyofthechallengesposedbycollaborativeprogrammingatscale.Andthescaleoftheproblemhasgottenmuchworse.ThisiswhatledagroupofresearchersatMSR(MicrosoftResearch)totakeonthetaskofcomplicatedmergesasagrandprogram-repairchallenge—onetheybelievedmightbeaddressedatleastinpartbymachinelearning.Tounderstandthethinkingthatledtothiseffortandthenfollowwherethatled,ErikMeijerandTerryCoattaspokewiththreeoftheleadingfiguresintheMSRresearcheffort,calledDeepMerge ForPractitioners DeterministicRecord-and-Replay ACMQueue’s"ResearchforPractice"servesupexpert-curatedguidestothebestofcomputingresearch,andrelatesthesebreakthroughstothechallengesthatsoftwareengineersfaceeveryday.Inthisinstallment,ResearchforPracticecoversthetopicofdeterministicrecord-and-replay.Deterministicrecord-and-replaytechnComputer Associationologiesenableafaithfulre-executionofaprogramthatraninthepast.Butaccomplishingthisrequiresthatanynondeterministicinputstotheprogrambeloggedduringexecution.TheselectionoftechniquespresentedhereiscuratedbyAndrewQuinn,AssistantProfessorofComputerScienceandEngineeringatUCSantaCruz. 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