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39 Demand-Driven Pointer Analysis with Strong Updates via Value-Flow Refinement YuleiSui,SchoolofComputerScienceandEngineering,UNSWAustralia JinglingXue,SchoolofComputerScienceandEngineering,UNSWAustralia We present a new demand-driven flow- and context-sensitive pointer analysis with strong updates for C programs, called SUPA, that enables computing points-to information via value-flow refinement, in envi- ronments with small time and memory budgets such as IDEs. We formulate SUPA by solving a graph- reachabilityproblemonaninter-proceduralvalue-flowgraphrepresentingaprogram’sdef-usechains,which are pre-computed efficiently but over-approximately. To answer a client query (a request for a variable’s 7 points-toset),SUPAreasonsabouttheflowofvaluesalongthepre-computeddef-usechainssparsely(rather 1 thanacrossallprogrampoints),byperformingonlytheworknecessaryforthequery(ratherthananalyz- 0 ingthewholeprogram).Inparticular,strongupdatesareperformedtofilteroutspuriousdef-usechains 2 throughvalue-flowrefinementaslongasthetotalbudgetisnotexhausted.SUPAfacilitatesefficiencyand n precisiontradeoffsbyapplyingdifferentpointeranalysesinahybridmulti-stageanalysisframework. a WehaveimplementedSUPAinLLVM(3.5.0)andevaluateitbychoosinguninitializedpointerdetection J asamajorclienton18open-sourceCprograms.Astheanalysisbudgetincreases,SUPAachievesimproved precision,withitssingle-stageflow-sensitiveanalysisreaching97.4%ofthatachievedbywhole-program 0 flow-sensitiveanalysisbyconsumingabout0.18secondsand65KBofmemoryperquery,onaverage(witha 2 budgetofatmost10000value-flowedgesperquery).Withcontext-sensitivityalsoconsidered,SUPA’stwo- ] stageanalysisbecomesmorepreciseforsomeprogramsbutalsoincursmoreanalysistimes.SUPAisalso L amenabletoparallelization.Aparallelimplementationofitssingle-stageflow-sensitiveanalysisachievesa P speedupofupto6.9xwithanaverageof3.05xa8-coremachinewithrespectitssequentialversion. . CCSConcepts:•SoftwareanditsengineeringÑSoftwareverificationandvalidation;Softwaredefect s c analysis;•TheoryofcomputationÑProgramanalysis; [ AdditionalKeyWordsandPhrases:strongupdates,valueflow,pointeranalysis,flowsensitivity 1 1. INTRODUCTION v 0 Pointer analysis is one of the most fundamental static program analyses, on which 5 virtuallyallothersarebuilt.Thegoalofpointeranalysisistocomputeanapproxima- 6 tion of the set of abstract objects that a pointer can refer to. A pointer analysis is (1) 5 flow-sensitive if it respects control flow and flow-insensitive otherwise and (2) context- 0 sensitiveifitdistinguishesdifferentcallingcontextsandcontext-insensitiveotherwise. 1. Strong updates, where stores overwrite, i.e., kill the previous contents of their ab- 0 stract destination objects with new values, is an important factor in the precision of 7 pointer analysis [Hardekopf and Lin 2009; Lhota´k and Chung 2011]. In the case of 1 weak updates, these objects are assumed conservatively to also retain their old con- : tents. Strong updates are possible only if flow-sensitivity is maintained. In addition, v a flow-sensitive analysis can strongly update an abstract object written at a store if i X andonlyifthatobjecthasexactlyoneconcretememoryaddress,knownasasingleton. r By applying strong updates where needed, a pointer analysis can improve precision, a thereby providing significant benefits to many clients, such as change impact anal- ysis [Acharya and Robinson 2011], bug detection [Yan et al. 2016; Ye et al. 2014a], security analysis [Arzt et al. 2014], type state verification [Fink et al. 2008], compiler optimization[Suietal.2016b,2013,2014b],andsymbolicexecution[Blackshearetal. 2013]. Inthispaper,weintroduceademand-drivenpointeranalysisforCbyinvestigating howtoperformstrongupdateseffectivelyinaflow-andcontext-sensitiveframework. For C programs, flow-sensitivity is important in achieving the precision required by the afore-mentioned client applications due to strong updates performed. If context- sensitivity is also considered, some more strong updates are possible for some pro- 39:2 YuleiSuiandJinglingXue gramsattheexpenseofmoreanalysistimes.Forobject-orientedlanguageslikeJava, context-sensitivity (without strong updates) is widely used in achieving useful preci- sion [Lhota´k and Hendren 2003; Li et al. 2014; Milanova et al. 2002, 2005; Smarag- dakisetal.2011;Sunetal.2011;XiaoandZhang2011]. Ideally, strong updates at stores should be performed by analyzing all paths inde- pendentlybysolvingameet-over-all-paths(MOP)problem.However,evenwithbranch conditions being ignored, this problem is intractable due to potentially unbounded numberofpathsthatmustbeanalyzed[Landi1992;Ramalingam1994]. Instead,traditionalflow-sensitivepointeranalysis(FS)forC[HindandPioli1998; KamandUllman1977]computesthemaximal-fixed-pointsolution(MFP)asanover- approximation of MOP by solving an iterative data-flow problem. Thus, the data-flow facts that reach a confluence point along different paths are merged. Improving on this, sparse flow-sensitive pointer analysis (SFS) [Hardekopf and Lin 2011; Li et al. 2011; Oh et al. 2012; Ye et al. 2014b; Yu et al. 2010] boosts the performance of FS in analyzing large C programs while maintaining the same strong updates done by FS. The basic idea is to first conduct a pre-analysis on the program to over-approximate itsdef-usechainsandthenperformFSbypropagatingthedata-flowfacts,i.e.,points- to information sparsely along only the pre-computed def-use chains (aka value-flows) insteadofallprogrampointsintheprogram’scontrol-flowgraph(CFG). Recently,anapproach[Lhota´kandChung2011]forperformingstrongupdatesinC programs is introduced. It sacrifices the precision of FS to gain efficiency by applying strongupdatesatstoreswhereflow-sensitivesingletonpoints-tosetsareavailablebut fallsbacktotheflow-insensitivepoints-toinformationotherwise. By nature, the challenge of pointer analysis is to make judicious tradeoffs between efficiencyandprecision.VirtuallyalloftheprioranalysesforCthatconsidersomede- greeofflow-sensitivityarewhole-programanalyses.Preciseonesareunscalablesince theymusttypicallyconsiderbothflow-andcontext-sensitivity(FSCS)inordertomax- imize the number of strong updates performed. In contrast, faster ones like [Lhota´k andChung2011]arelessprecise,duetobothmissingstrongupdatesandpropagating thepoints-toinformationflow-insensitivelyacrosstheweakly-updatedlocations. In practice, a client application of a pointer analysis may require only parts of the program to be analyzed. In addition, some points-to queries may demand precise an- swerswhileotherscanbeansweredaspreciselyaspossiblewithsmalltimeandmem- ory budgets. In all these cases, performing strong updates blindly across the entire programiscost-ineffectiveinachievingprecision. For C programs, how do we develop precise and efficient pointer analyses that are focused and partial, paying closer attention to the parts of the programs relevant to on-demand queries? Demand-driven analyses for C [Heintze and Tardieu 2001; Zhang et al. 2014a; Zheng and Rugina 2008] and Java [Lu et al. 2013; Shang et al. 2012; Sridharan and Bod´ık 2006; Su et al. 2016; Yan et al. 2011] are flow-insensitive and thus cannot perform strong updates to produce the precision needed by some clients.BOOMERANG[Spa¨thetal.2016]representsarecentflow-andcontext-sensitive demand-driven pointer analysis for Java. However, its access-path-based approach performs strong updates at a store a.f “ ... only partially, by updating a.f strongly and the aliases of a.f.˚ weakly. Elsewhere, advances in whole-program flow-sensitive analysisforChaveexploitedsomeformofsparsitytoimproveperformance[Hardekopf and Lin 2011; Li et al. 2011; Oh et al. 2012; Ye et al. 2014b; Yu et al. 2010]. However, howtoreplicatethissuccessfordemand-drivenflow-sensitiveanalysisforCisunclear. Finally,itremainsopenastowhethersparsestrongupdateanalysiscanbeperformed bothflow-andcontext-sensitivelyon-demandtoavoidunder-orover-analyzing. In this paper, we introduce SUPA, the first demand-driven pointer analysis with strong updates for C, designed to support flexible yet effective tradeoffs between effi- Demand-DrivenFlow-SensitivePointerAnalysis 39:3 Pre- Stages Program Value-Flows analysis Efficiency Precision Refine On-Demand Stage[0] ....Stage[N-1] Queries Reachability Solver Stage[i] Select i Budgets Out-Of-Budget[i]? No i++ Yes Fig.1:Overviewof SUPA ciencyandprecisioninansweringclientqueries,inenvironmentswithsmalltimeand memory budgets such as IDEs. As shown in Figure 1, the novelty behind SUPA lies inperformingStrongUPdateAnalysispreciselybyrefiningimpreciselypre-computed value-flowsawayinahybridmulti-stageanalysisframework.Givenapoints-toquery, strong updates are performed by solving a graph-reachability problem on an inter- proceduralvalue-flowgraphthatcapturesthedef-usechainsoftheprogramobtained conservativelybyapre-analysis.Suchover-approximatedvalue-flowscanbeobtained by applying Andersen’s analysis [Andersen 1994] (flow- and context-insensitively). SUPA conducts its reachability analysis on-demand sparsely along only the pre- computedvalue-flowsratherthancontrol-flows.Inaddition,SUPAfiltersoutimprecise value-flows by performing strong updates flow- and context-sensitively where needed withnolossofprecisionaslongasthetotalanalysisbudgetissufficient.Theprecision of SUPA depends on the degree of value-flow refinement performed under a budget. Themorespuriousvalue-flowsSUPAremoves,themoreprecisethepoints-tofactsare. SUPA handles large C programs by staging analyses in increasing efficiency but decreasing precision in a hybrid manner. Currently, SUPA proceeds in two stages by applying FSCS and FS in that order with a configurable budget for each analysis. Whenfailingtoansweraqueryinastagewithinitsallotedbudget,SUPAdowngrades itselftoamorescalablebutlesspreciseanalysisinthenextstageandwilleventually fall back to the pre-computed flow-insensitive information. At each stage, SUPA will re-answer the query by reusing the points-to information found from processing the currentandearlierqueries.Byincreasingthebudgetsusedintheearlierstages(e.g., FSCS), SUPA willobtainimprovedprecisionviaimprovedvalue-flowrefinement. Insummary,thispapermakesthefollowingcontributions: — We present the first demand-driven flow- and context-sensitive pointer analysis with strong updates for C that enables computing precise points-to information by refiningawayimpreciselyprecomputedvalue-flows,subjecttoanalysisbudgets. — Weintroduceahybridmulti-stageanalysisframeworkthatfacilitatesefficiencyand precisiontradeoffsbystagingdifferentanalysesinansweringclientqueries. — We have produced an implementation of SUPA in LLVM (3.5.0) [SUPA 2016]. We evaluate SUPA with uninitialized pointer detection as a practical client by using a totalof18open-sourceCprograms.Astheanalysisbudgetincreases,SUPAachieves improved precision, with its single-stage flow-sensitive analysis reaching 97.4% of that achieved by whole-program flow-sensitive analysis, by consuming about 0.18 39:4 YuleiSuiandJinglingXue seconds and 65KB of memory per query, on average (with a per-query budget of at most10000value-flowedgestraversed).Withcontext-sensitivityalsobeingconsid- ered,morestrongupdatesarealsopossible.SUPA’stwo-stageanalysisthenbecomes morepreciseforsomeprogramsattheexpenseofmoreanalysistimes. — We present four case studies to demonstrate that SUPA is effective in checking whethervariablesareinitializedornotinreal-worldapplications. — We show that SUPA is amenable to parallelization. To demonstrate this, we have developed a parallel implementation of SUPA’s single-stage flow-sensitive analysis basedonIntelThreadingBuildingBlocks(TBB),achievingaspeedupofupto6.9x withanaverageof3.05xa8-coremachineoveritssequentialversion. The rest of this paper is organized as follows. Section 2 provides the background information. Section 3 presents a motivating example. Section 4 introduces our for- malismforSUPA.Section5discussesandanalyzesourexperimentalresults.Section6 containsfourcasestudies.Section7describesaparallelimplementationofSUPA.Sec- tion8describestherelatedwork.Finally,Section9concludesthepaper. 2. BACKGROUND We describe how to represent a C program by an interprocedural sparse value-flow graphtoenabledemand-drivenpointeranalysisviavalue-flowrefinement.Section2.1 introducesthepartofLLVM-IRrelevanttopointeranalysis.Section2.2describeshow toputtop-levelvariablesinSSAform.Section2.3describeshowtoputaddress-taken variablesinSSAform.Section2.4constructsasparsevalue-flowgraphthatrepresents thedef-usechainsforbothtop-levelandaddress-takenvariablesintheprogram. 2.1. LLVM-IR We perform pointer analysis in the LLVM-IR of a program, as in [Balatsouras and Smaragdakis 2016; Hardekopf and Lin 2011; Lhota´k and Chung 2011; Li et al. 2011; Sui et al. 2012; Ye et al. 2014b]. The domains and the LLVM instructions relevant to pointeranalysisaregiveninTableI.ThesetofallvariablesV areseparatedintotwo subsets,O thatcontainsallpossibleabstractobjects,i.e.,address-takenvariablesofa pointerandP thatcontainsalltop-levelvariables. InLLVM-IR,top-levelvariablesinP “SYG,includingstackvirtualregisters(sym- bolsstartingwith”%”)andglobalvariables(symbolsstartingwith”@”)areexplicit,i.e., directlyaccessed.Address-takenvariablesinOareimplicit,i.e.,accessedindirectlyat LLVM’sloadorstoreinstructionsviatop-levelvariables. OnlyasubsetofthecompleteLLVMinstructionsetthatisrelevanttopointeranal- ysis are modeled. As in Table I, every function f of a program contains nine types of instructions (statements), including seven types of instructions used in the function body of f, and one FUNENTRY instruction fpr1,...,rnq with the declarations of the parameters of f, and one FUNEXIT instruction retf p as the unique return of f. Note thattheLLVMpassUnifyFunctionExitNodesisexecutedbeforepointeranalysisinorder toensurethateveryfunctionhasonlyone FUNEXIT instruction. Let us go through the seven types of instructions used inside a function. For an ADDROFinstructionp“&o,knownasanallocationsite,oisoneofthefollowingobjects: (1)astackobject,o ,where(cid:96)isitsallocationsite(viaanLLVMallocainstruction),(2)a (cid:96) globalobject,i.e.,aglobalobjecto ,where(cid:96)isitsallocationsiteoraprogramfunction (cid:96) o , where f is its name, and (3) a dynamically created heap object oh, where (cid:96) is its f (cid:96) heapallocationsite(e.g.,viaamalloc()call).Foreachobjecto(exceptforafunction),we writeo torepresentthesub-objectthatcorrespondstoitsfieldfld.Forflow-sensitive fld pointeranalysis,theinitializationsforglobalobjectstakeplaceattheentryofmain(). Demand-DrivenFlow-SensitivePointerAnalysis 39:5 TableI:DomainsandLLVMinstructionsusedbypointeranalysis. AnalysisDomains LLVMInstructionSet (cid:96) PL instructionlabels ADDROF p =&o fld PC constants(fieldaccesses) COPY p =q s PS stackvirtualregisters PHI p =φpq,rq g PG globalvariables FIELD p =&qÑfld LOAD p =˚q f PF ĎG programfunctions STORE ˚p=q p,q,r,x,y PP “SYG top-levelvariables CALL p =qpr1,...,rnq o,a,b,c,d PO address-takenvariables FUNENTRY fpr1,...,rnq v PV “P YO programvariables FUNEXIT retf p COPY denotes a casting instruction (e.g., bitcast) in LLVM. PHI is a standard SSA instructionintroducedataconfluencepointintheCFGtoselectthevalueofavariable fromdifferentcontrol-flowbranches.LOAD(STORE)isamemoryaccessinginstruction thatreads(write)avaluefrom(into)anaddress-takenobject. Ourhandlingoffield-sensitivityisANSI-compliant.Givenapointertoanaggregate (e.g., a struct or an array), pointer arithmetic used for accessing anything other than theaggregateitselfhasundefinedbehavior[ISO901990;Pearceetal.2007]andthus not modeled. To model the field accesses of a struct object, FIELD represents a getele- mentptrinstructionwithitsfieldoffsetfldasaconstantvalue.Agetelementptrinstruc- tionthatoperatesonanon-constantfieldofastructismodeledas COPY instructions, oneforeveryfieldofthestructconservatively.Arraysaretreatedmonolithically. CALL, p “ qpr1,...,rnq, denotes a call instruction, where q can be either a global variable(foradirectcall)orastackvirtualregister(foranindirectcall). 2.2. SSAFormforTop-LevelVariables LLVM-IR is known as a partial SSA form since only top-level variables are in SSA form.InLLVM-IR,top-levelvariablesareexplicit,i.e.,directlyaccessedandcanthus be put in SSA form by using a standard SSA construction algorithm [Cytron et al. 1991](with PHI instructionsinsertedatconfluencepoints). p = &a; p = &a; Points-to relations for p and q q = &c; q = &c; observed at runtime x = &b; p q p q a = &b; y = &d; c = &d; *p = x; *q = y; a c a c t1 = *p; t1 = *p; swap *p = *q; swap t2 = *q; b d b d *p = t2; *q = t1; *q = t1; (a) C code (b) Partial SSA (c) Before swap (d) After swap Fig.2:AswapexampleanditspartialSSAform. Let us illustrate LLVM’s partial SSA form by using an example in Figure 2. Fig- ure 2(a) shows a swap program in C and Figure 2(b) gives its corresponding partial 39:6 YuleiSuiandJinglingXue SSA form. Figures 2(c) and (d) depict some (runtime) points-to relations before and aftertheswapoperation.Inthisexample,wehavep,q,x,y,t1,t2 P P anda,b,c,d P O. Notethatx,y,t1andt2arenewtemporaryregistersintroducedinordertoputthepro- gramgiveninFigure2(a)intothepartialSSAformgiveninFigure2(b).Inparticular, ˚p“˚q isdecomposedintot2“˚q and˚p“t2,wheret2isatop-levelpointer. In LLVM-IR, all top-level variables are in SSA form. In this example, all top-level variablesaretriviallyinSSAform,aseachhasexactlyonedefinitiononly.Asaresult, thedef-usechainsfortop-levelvariablesarereadilyavailable. However,address-takenvariablesareaccessedindirectlyatloadsandstoresviatop- level variables and thus not in SSA form. For example, the address-taken variable a is defined implicitly twice, once at ˚p “ x and once at ˚p “ t2, and the address-taken variable c is also defined implicitly twice, once at ˚q “ y and once at ˚q “ t1. As a result,thedef-usechainsforaddress-takenvariablesarenotimmediatelyavailable. 2.3. SSAFormforAddress-TakenVariables Starting with LLVM’s partial SSA form, we first perform a pre-analysis by using An- dersen’s algorithm flow- and context-insensitively [Andersen 1994], implemented in SVF [Sui and Xue 2016]. We then put address-taken variables in memory SSA form, by using the SSA construction algorithm [Cytron et al. 1991]. Imprecise points-to in- formationcomputedthiswaywillberefinedbyourdemand-drivenpointeranalysis. Givenavariablev,AnderPtspvqrepresentsitspoints-tosetcomputedbyAndersen’s algorithm. There are two steps [Sui et al. 2014a], illustrated in Figures 3(a) and (b) intraprocedurallyandinFigures4(a)and(b)interprocedurally. Step1:ComputingModificationandReferenceSide-Effects. As shown in Figure 3(a), every load, e.g., t1 “ ˚q is annotated with a µpaq operator for each object a pointed by q, i.e., a P AnderPtspqq to represent a potential use of a at the load. Similarly, every store, e.g., ˚p “ x is annotated with a a“χpaq operator for each object a P AnderPtsppqtorepresentapotentialdefanduseofaatthestore.Ifacanbestrongly updated, then a receives whatever x points to and the old contents in a are killed. Otherwise,amustalsoincorporateitsoldcontents,resultinginaweakupdatetoa. Wecomputetheside-effectsofafunctioncallbyapplyingalightweightinterproce- duralmod-refanalysis[Suietal.2014a,§4.2.1].Foragivencallsite(cid:96),itisannotated withµpaq(a“χpaq)ifamayberead(modified)insidethecalleesof(cid:96)(discoveredby Andersen’s pointer analysis). In addition, appropriate χ and µ operators are also added for the FUNENTRY and FUNEXIT instructions of these callees in order to mimicpassingparametersandreturningresultsforaddress-takenvariables. Figure4(a)givesanexamplemodifiedfromFigure3(a)bymovingthefourswapin- structionsintoafunction,swap.Forreadside-effects,µpaqandµpcqareaddedbefore callsite(cid:96) torepresentthepotentialusesofaandcinswap.Correspondingly,swap’s 7 FUNENTRY instruction(cid:96)8 isannotatedwitha“χpaqandc“χpcqtoreceivetheval- uesofaandcpassedfrom(cid:96) .Formodificationside-effects,a“χpaqandc“χpcqare 7 added after (cid:96) to receive the potentially modified values of a and c returned from 7 swap’s FUNEXIT instruction(cid:96)13,whichareannotatedwithµpaqandµpcq. Step2:MemorySSARenaming. All the address-taken variables are converted into SSA form as suggested in [Chow et al. 1996]. Every µpaq is treated as a use of a. Every a“χpaq is treated as both a def and use of a, as a may admit only a weak update. Then the SSA form for address-taken variables is obtained by applying a standardSSAconstructionalgorithm[Cytronetal.1991]. Fortheprogramannotatedwithµ’sandχ’sinFigure3(a),Figure3(b)givesitsmem- orySSAform.Similarly,Figure4(b)givesthememorySSAformforFigure4(a). Demand-DrivenFlow-SensitivePointerAnalysis 39:7 ℓ1: p = &a; p = &a; p = &a; ℓ2: q = &c; q = &c; q = &c; ℓ3: x = &b; x = &b; x = &b; ℓ4: y = &d; y = &d; y = &d; ℓ5: *p = x; *p = x; *p = x; a = !(a) a1 = !(a0) a1 = !(a0) ℓ6: *q = y; *q = y; [a] *q = y; c = !(c) c1 = !(c0) c1 = !(c0) "(a) "(a1) "(a1) [c] ℓ7: t1 = *p; t1 = *p; t1 = *p; [a] "(c) "(c1) "(c1) [c] ℓ8: t2 = *q; t2 = *q; t2 = *q; swap swap swap ℓ9: *p = t2; *p = t2; *p = t2; a = !(a) a2 = !(a1) a2 = !(a1) ℓ10: *q = t1; *q = t1; *q = t1; c = !(c) c2 = !(c1) c2 = !(c1) (a) Step 1: adding "s$and$!s (b) Step 2: renaming (c) Sparse value-flows of a and c Fig. 3: Memory SSA form and sparse value-flows constructed intraprocedurally for Figure2,obtainedwithAndersen’sanalysis:AnderPtsppq“tauandAnderPtspqq“tcu. foo(){ ℓ8:swap(p,q){ foo(){ swap(p,q){ foo(){ swap(p,q){ ℓ1 : p = &a; a = !(a) p = &a; a1 = !(a0) p = &a; a1 = !(a0) ℓ2 : q = &c; c = !(c) q = &c; c1 = !(c0) q = &c; c1 = !(c0) [a] [a] ℓ3 : x = &b; "(a) x = &b; "(a1) x = &b; "(a1) ℓ4 : y = &d; ℓ9: t1 = *p; y = &d; t1 = *p; y = &d; [a] t1 = *p; [c] ℓ5 : *p = x; "(c) *p = x; "(c1) *p = x; [c] "(c1) a = !(a) ℓ10: t2 = *q; a1 = !(a0) t2 = *q; a1 = !(a0) t2 = *q; [c] ℓ6 : * q c == y!;(c) ℓ11: * p a= = t 2!;(a) * q c 1= =y ;!(c0) * p a=2 t=2 !;(a1) [a] * q c 1= =y ;!(c0) * p a=2 t=2 !;(a1) [a] "(a) ℓ12: *q = t1; "(a1) *q = t1; [c] "(a1) *q = t1; "(c) c = !(c) "(c1) c2 = !(c1) "(c1) c2 = !(c1) [c] ℓ 7 : swap(p,q); swap(p,q); swap(p,q); a = !(a) "(a) a2 = !(a1) "(a2) a2 = !(a1) [a] "(a2) c = !(c) ℓ13: "(c) c2 = !(c1) "(c2) c2 = !(c1) [c] "(c2) } } } } } } (a) Step 1: adding "s$and$!s (b) Step 2: renaming (c) Sparse value-flows of a and c Fig.4:MemorySSAformandsparsevalue-flowsconstructedinterprocedurallyforan examplemodifiedfromFigure2withitsfourswapinstructionsmovedintoaseparate function,calledswap.(cid:96)8 and(cid:96)13 correspondtothe FUNENTRY and FUNEXIT ofswap. 2.4. SparseValue-FlowGraph Oncebothtop-levelandaddress-takenvariablesareinSSAform,theirdef-usechains areimmediatelyavailable,asshowninTableII.Wediscussedtop-levelvariablesear- lier. For the two address-taken variables a and c in Figure 2, Figure 3(c) depicts their def-use chains, i.e., sparse value-flows for the memory SSA form in Figure 3(b). Simi- larly,Figure4(c)givestheirsparsevalue-flowsforthememorySSAforminFigure4(b). Givenaprogram,asparsevalue-flowgraph(SVFG),G “pN,Eq,isamulti-edged vfg directed graph that captures its def-use chains for both top-level and address-taken 39:8 YuleiSuiandJinglingXue Table II: Def-use information of both top-level and address-taken variables. Def v (Use )denotesthesetofdefinition(use)instructionsforavariablev PV. v Instruction (cid:96) DefsandUsesofVariablesinMemorySSAForm p“&o t(cid:96)u“Def p p“q t(cid:96)u“Def (cid:96)PUse p q p“φpq,rq t(cid:96)u“Def (cid:96)PUse (cid:96)PUse p q r p“&qÑfld t(cid:96)u“Def (cid:96)PUse p q p“˚q µpa q t(cid:96)u“Def (cid:96)PUse (cid:96)PUse i p q ai ˚p“q a “χpa q (cid:96)PUse (cid:96)PUse (cid:96)PDef (cid:96)PUse i`1 i p q ai`1 ai p“qpr ,...,r q t(cid:96)u“Def (cid:96)PUse @iP1,...,n:(cid:96)PUse 1 n p q ri µpa q a “χpa q (cid:96)PUse (cid:96)PDef (cid:96)PUse i j`1 j ai aj`1 aj fpr ,...,r q a “χpa q @iP1,...,n:(cid:96)PDef (cid:96)PDef (cid:96)PUse 1 n i`1 i ri ai`1 ai ret p µpa q (cid:96)PUse (cid:96)PUse f i p ai (cid:96)PDef (cid:96)1 PUse (cid:96)PDef (cid:96)1 PUse [INTRA-TOP] p p [INTRA-ADDR] ai ai (cid:96)ÝÑp (cid:96)1 (cid:96)ÝÑa (cid:96)1 (cid:96):p“qpr ,...,r q o PAnderPtspqq (cid:96)1 :fpr1,...,r1q [INTER-CALL-TOP] 1 n f 1 n @iP1,...,n:(cid:96)ÝrÑi (cid:96)1 (cid:96):p“qp...q a PAnderPtspqq (cid:96)1 :ret p1 f f [INTER-RET-TOP] (cid:96)1 ÝÑp (cid:96) (cid:96):p“qp...qµpa q a PAnderPtspqq (cid:96)1 :fp...qa “χpa q [INTER-CALL-ADDR] i f j`1 j (cid:96)ÝÑa (cid:96)1 (cid:96): “qp...qa “χpa q a PAnderPtspqq (cid:96)1 :ret µpa q [INTER-RET-ADDR] j`1 j f f i (cid:96)1 ÝÑa (cid:96) Fig.5:Value-flowconstructioninMemorySSAform. variables. N is the set of nodes representing all instructions and E is the set of edges v representing all potential def-use chains. In particular, an edge (cid:96) ÝÑ (cid:96) , where v P V, 1 2 fromstatement(cid:96) tostatement(cid:96) signifiesapotentialdef-usechainforvwithitsdefat 1 2 (cid:96) anduseat(cid:96) .Wereferto(cid:96) ÝÑv (cid:96) adirectvalue-flowifv P P andanindirectvalue- 1 2 1 2 flow if v P O. This representation is sparse since the intermediate program points between (cid:96) and (cid:96) are omitted, thereby enabling the underlying points-to information 1 2 tobegraduallyrefinedbyapplyingasparsedemand-drivenpointeranalysis. Figure 5 gives the rules for connecting value-flows between two instructions based on the defs and uses computed in Table II. For intraprocedural value-flows, [INTRA-TOP]and[INTRA-ADDR]handletop-levelandaddress-takenvariables,respec- tively.InSSAform,everyuseofavariableonlyhasauniquedefinition.Forauseofa identified as a (with its i-th version) at (cid:96)1 annotated with µpa q, its unique definition i i inSSAformisa atan(cid:96)annotatedwitha “χpa q.Then,(cid:96)ÝÑa (cid:96)1 isgeneratedtorep- i i i´1 resent potentially the value-flow of a from (cid:96) to (cid:96)1. Thus, the PHI functions introduced foraddress-takenvariableswillbeignored,asthevalueain(cid:96)ÝÑa (cid:96)1 isnotversioned. Let us consider interprocedural value-flows. The def-use information in Table II is only intraprocedural. According to Figure 5, interprocedural value-flows are con- structed to represent parameter passing for top-level variables ([INTER-CALL-TOP] and [INTER-RET-TOP]), and the µ{χ operators annotated at FUNENTRY, FUNEXIT and CALL foraddress-takenvariables([INTER-CALL-ADDR]and[INTER-RET-ADDR]). Demand-DrivenFlow-SensitivePointerAnalysis 39:9 [INTER-CALL-TOP] connects the value-flow from an actual argument r at a call in- i struction(cid:96)toitscorrespondingformalparameterr1 attheFUNENTRY(cid:96)1ofeverycallee i f invoked at the call. Conversely, [INTER-RET-TOP] models the value-flow from the FUNEXIT instruction of f to every callsite where f is invoked. Just like for top-level variables,[INTER-CALL-ADDR]and[INTER-RET-ADDR]buildthevalue-flowsofaddress- takenvariablesacrossthefunctionsaccordingtotheannotatedµ’sandχ’s.Notethat the versions i and j of an SSA variable a in different functions may be different. For a c example,Figure4(c)illustratesthefourinter-proceduralvalue-flows(cid:96) ÝÑ(cid:96) ,(cid:96) ÑÝ (cid:96) , 7 8 7 8 a c (cid:96) ÝÑ(cid:96) and(cid:96) ÑÝ (cid:96) obtainedbyapplyingthetworulestoFigure4(b). 13 7 13 7 a The SVFG obtained this way may contain spurious def-use chains, such as (cid:96) ÝÑ (cid:96) 5 9 in Figure 3, as Andersen’s flow- and context-insensitive pointer analysis is fast but imprecise. However, this representation allows imprecise points-to information to be refinedbyperformingsparsewhole-programflow-sensitivepointeranalysisasinprior work [Hardekopf and Lin 2011; Nagaraj and Govindarajan 2013; Sui et al. 2016a; Ye etal.2014b].Inthispaper,weintroduceademand-drivenflow-andcontext-sensitive pointeranalysiswithstrongupdatesthatcananswerpoints-toqueriesefficientlyand preciselyon-demand,byremovingspuriousdef-usechainsintheSVFGiteratively. 3. AMOTIVATINGEXAMPLE Our demand-driven pointer analysis, SUPA, operates on the SVFG of a program. It computes points-to queries flow- and context-sensitively on-demand by performing strongupdates,wheneverpossible,torefineawayimprecisevalue-flowsintheSVFG. Our example program, shown in Figure 6(a), is simple (even with 16 lines). The program consists of a straight-line sequence of code, with (cid:96) – (cid:96) taken directly from 1 10 Figure2(b)and thesixnewstatements (cid:96) – (cid:96) addedtoenable ustohighlightsome 11 16 key properties of SUPA. We assume that u at (cid:96)11 is uninitialized but i at (cid:96)12 is initial- ized.TheSVFGembeddedinFigure6(a)willbereferredtoshortlybelow.Wedescribe how SUPA can be used to prove that z at (cid:96)16 points only to the initialized object i, by computingflow-sensitivelyon-demandthepoints-toqueryptpx(cid:96) ,zyq,i.e.,thepoints-to 16 setofz attheprogrampointafter(cid:96) ,whichisdefinedin(1)inSection4. 16 Figure 6(b) depicts the points-to relations for the six address-taken variables and some top-level ones found at the end of the code sequence by a whole-program flow- sensitive analysis (with strong updates) like SFS [Hardekopf and Lin 2011]. Due to flow-sensitivity,multiplesolutionsforapointeraremaintained.Inthisexample,these are the true relations observed at the end of program execution. Note that SFS gives rise to Figure 2(c) by analyzing (cid:96) – (cid:96) , Figure 2(d) by analyzing also (cid:96) – (cid:96) , and 1 6 7 10 finally,Figure6(b)byanalyzing(cid:96) –(cid:96) further.Asz pointstoibutnotu,nowarning 11 16 isissuedforz,implyingthatz isregardedasbeingproperlyinitialized. Figure 6(c) shows how the points-to relations in Figure 6(b) are over-approximated flow-insensitively by applying Andersen’s analysis [Andersen 1994]. In this case, a single solution is computed conservatively for the entire program. Due to the lack of strong updates in analyzing the two stores performed by swap, the points-to relations in Figures 2(c) and 2(d) are merged, causing ˚a and ˚c to become spurious aliases. When (cid:96) – (cid:96) are analyzed, the seven spurious points-to relations (shown in dashed 11 16 arrowsinFigure6(c))areintroduced.Sincezpointstoi(correctly)andu(spuriously),a falsealarmforzwillbeissued.Failingtoconsiderflow-sensitivity,Andersen’sanalysis isnotpreciseforthisuninitializationpointerdetectionclient. Let us now explain how SUPA, shown in Figure 1, works. SUPA will first perform a pre-analysis to the example program to build the SVFG given in Figure 6(a), as discussed in Section 2. For its top-level variables, their direct value-flows, i.e., def- use chains are explicit and thus omitted to avoid cluttering. For example, q has three 39:10 YuleiSuiandJinglingXue Points-to Spurious Points-to Direct Value-flow Indirect Value-flow ℓ1 : p = &a; Query ℓ1: p = &a; ℓ2 : q = &c; pt.(⟨ℓ16 ,z⟩) =? ℓ2: q = &c; ℓ3 : x = &b; ℓ4: y = &d; ℓ4 : y = &d; ℓ5:*p = x; [y] 7 ℓ5:*p = x; SU for c [q] ℓ6: *q = y; [a] ℓ6: *q = y; [a] [c] 6 5 ℓ7: t1 = *p; [c] [q] x ℓ8: t2 = *q; [a] [t2] 4 ℓ8: t2 = *q; swap [c] SU for a ℓ9: *p = t2; [p] ℓ9: *p = t2; ℓ10:*q = t1; 3 [a] ℓ12: v = &i; 2 [p] ℓ11: w = &u; ℓ13 : t3 = *p; [a] ℓ12: v = &i; 9 [v] x ℓ14: *t3 = w; x ℓ13: t3 = *p; [b] [d] [b] ℓ14:*t3 = w; [d] SU for d ℓ15: *t3 = v; [t3] [b] ℓℓ1156 :: * tz3 == *vt3;; [d] Spurious Value-Flows x[b] ℓ1 6 : z = *t3; [d] 8 [t13] (a) A program and its SVFG (with (d) The SUPA analysis for resolving pt(⟨ℓ16 ,z⟩) = {i} by only indirect value-flows shown) traversing from ⟨ℓ16 ,z⟩ backwards against the value-flows p q p q a c a c t3 t3 b d b d z z i u i u (b) Flow-sensitive points-to relations found (c) Flow-insensitive points-to relations to hold at the end of the program (with some for top-level pointers omitted) (with some for top-level pointers omitted) Fig.6:Amotivatingexampleforillustrating SUPA (SUstandsfor“StrongUpdate”). q q q def-use chains (cid:96) ÝÑ (cid:96) , (cid:96) ÝÑ (cid:96) and (cid:96) ÝÑ (cid:96) . For its address-taken variables, there 2 6 2 8 2 10 are nine indirect value-flows, i.e., def-use chains depicted in Figure 6(a). Let us see how the two def-use chains for b are created. As t3 points to b, (cid:96) , (cid:96) and (cid:96) will be 14 15 16 annotated with b “ χpbq, b “ χpbq and µpbq, respectively. By putting b in SSA form, b thesethreefunctionsbecomeb2“χpb1q,b3“χpb2qandµpb3q.Hence,wehave(cid:96) ÑÝ (cid:96) 14 15 b and (cid:96) ÑÝ (cid:96) , indicating b at (cid:96) has two potential definitions, with the one at (cid:96) 15 16 16 15 overwritingtheoneat(cid:96) .Thedef-usechainsfordandaarebuiltsimilarly. 14

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