Effects of Pointers on Data Dependences(15)

Data dependences, which relate statements that compute data values to statements that use those values, are useful for automating a variety of program-comprehension-related activities, such as reverse engineering, impact analysis, and debugging. Unfortunat

evaluatedthee ectsoftheprecisionofthepointeranalysisonsubsequentanalyses,suchasthecomputationofdef-useassociations(e.g.[25])andprogramslicing(e.g.[5,16,22]).Noneofthatresearch,however,distinguishesdef-useassociationsbasedonthetypesofde nitions,uses,anddef-useassociations—theyviewuniformlyeachdef-useassociationthatarisesinthepresenceofpointers.

Tonellaandcolleagues[26]analyzethee ectsoftheprecisionofthereaching-de nitioncomputationondef-useassociations,buttheydonotconsiderhowsuchprecisiona ectstheclassi cationofdef-useassociations.

Otherresearchers(e.g.[8,11])haveinvestigatedvariouswaystoreducethesizeofslices.However,theyhavenotconsideredclassifyingdatadependencesandcomputingslicesbasedondi erenttypesofdatadependencesasameansofreducingthesizeofslices.

6SummaryandFutureWork

Inthispaper,wepresentedatechniqueforcomputingandclassifyingdatadependencesinprogramsthatusepointers.Theclassi cationthatweproposeis nergrainedwithrespecttopreviouslypresentedclassi- cations,andallowsforapartitioningofdatadependencesinto24sets,basedontheir“strength.”Wealsopresentedthe rstsetofexperimentalresultsthatillustratesthedistributionofdatadependencesforasetofCsubjects.Althoughwecannotdrawanyconclusiveinference,thedatagatheredsofarshowtrendsthatareworthfurtherinvestigation.

Weillustratedapotentialapplicationoftheproposedclassi cationforprogramslicing.Ourslicingtechniqueletstheuser rstfocusonasmaller,thuseasiertounderstand,subsetoftheprogram,andthenconsiderincreasinglybiggerpartsofthecode.Wehavealsopresentedacasestudy,forwhichtheadditionof“weak”datadependencescausedonlyasmallgrowthofthesizeoftheslices,butisolatedandidenti edsubtledatadependenceswithintheprogram.

Wewillconductfurtherempiricalstudiestoevaluateboththedistributionofthedatadependencesandthee ectivenessoftheincrementalslicingtechnique.Ourfutureworkalsoincludestheextensionsofourprototypetouseadi erent,moree cient,kindofaliasanalysis.Thisimprovementwillallowus(1)toperformexperimentsonsubjectsofbiggersize,and(2)tostudytherelationbetweenthedistributionofdatadependencesandtheprecisionoftheunderlyingaliasanalysis.Wealsoplantoperformastudyofthesourcecodeofthesubjectstryingtoidentifypatternsinthatcodethatcancausespeci ctypesofdatadependences.Weareconvincedthatsuchpatternscouldbeofgreathelptotuneanalysisalgorithmsandprovideguidelinesfortheprogrammers.

Acknowledgments

ThisworkwassupportedinpartbyagrantfromBoeingCommercialAirplanestoGeorgiaTech,byNationalScienceFoundationawardsCCR-9707792,CCR-9988294,andCCR-0096321toGeorgiaTech,andbytheStateofGeorgiatoGeorgiaTechundertheYamacrawMission.