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Tan Tian, Ma Xiaoxing, Xu Chang, Ma Chunyan, Li Yue. Survey on Java Pointer Analysis[J]. Journal of Computer Research and Development, 2023, 60(2): 274-293. DOI: 10.7544/issn1000-1239.202220901
Citation: Tan Tian, Ma Xiaoxing, Xu Chang, Ma Chunyan, Li Yue. Survey on Java Pointer Analysis[J]. Journal of Computer Research and Development, 2023, 60(2): 274-293. DOI: 10.7544/issn1000-1239.202220901

Survey on Java Pointer Analysis

Funds: This work was supported by the National Natural Science Foundation of China (61932021,62025202,62002157), and the Aeronautical Science Fund (20185853038,201907053004).
More Information
  • Received Date: October 25, 2022
  • Revised Date: January 10, 2023
  • Available Online: February 10, 2023
  • In recent years, static program analysis has become one of the key techniques to ensure the reliability, security and efficiency of software. As a fundamental program analysis technique, pointer analysis provides a series of fundamental information about the program for static program analysis, such as the points-to relations of any variables in the program, alias relations between variables, program call graph, and the reachability of heap objects. We introduce the important contents of Java pointer analysis, including pointer analysis algorithm, context sensitivity, abstraction of heap objects, handling of complex language features, non-whole program pointer analysis, especially we sort-out and discuss selective context sensitivity, which is the research hotspot of pointer analysis in recent years.

  • [1]
    Smaragdakis Y, Balatsouras G. Pointer analysis[J]. Foundations and Trends in Programming Languages, 2015, 2(1): 1−69 doi: 10.1561/2500000014
    [2]
    张健,张超,玄跻峰,等. 程序分析研究进展[J]. 软件学报,2019,30(1):80−109 doi: 10.13328/j.cnki.jos.005651

    Zhang Jian, Zhang Chao, Xuan Jifeng, et al. Recent progress in program analysis[J]. Journal of Software, 2019, 30(1): 80−109 (in Chinese) doi: 10.13328/j.cnki.jos.005651
    [3]
    Sridharan M, Chandra S, Dolby J, et al. Alias analysis for object-oriented programs [G] //LNCS 7850: Aliasing in Object-Oriented Programming. Types, Analysis and Verification. Berlin: Springer, 2013: 196−232
    [4]
    Chandra S, Fink S J, Sridharan M. Snugglebug: A powerful approach to weakest preconditions [C] //Proc of the 30th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2009: 363−374
    [5]
    Naik M, Aiken A, Whaley J. Effective static race detection for Java [C] //Proc of the 27th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2006: 308−319
    [6]
    Naik M, Park C S, Sen K, et al. Effective static deadlock detection [C] //Proc of the 31st Int Conf on Software Engineering. Piscataway, NJ: IEEE, 2009: 386−396
    [7]
    王蕾,何冬杰,李炼,等. 基于稀疏框架的静态污点分析优化技术[J]. 计算机研究与发展,2019,56(3):480−495 doi: 10.7544/issn1000-1239.2019.20180071

    Wang Lei, He Dongjie, Li Lian, et al. Sparse framework based static taint analysis optimization[J]. Journal of Computer Research and Development, 2019, 56(3): 480−495 (in Chinese) doi: 10.7544/issn1000-1239.2019.20180071
    [8]
    Arzt S, Rasthofer S, Fritz C, et al. FlowDroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android Apps [C] //Proc of the 35th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2014: 259−269
    [9]
    Grech N, Smaragdakis Y. P/Taint: Unified points-to and taint analysis [J]. Proceedings of the ACM on Programming Languages, 2017, 1(OOPSLA): 1−28
    [10]
    Livshits V B, Lam M S. Finding security vulnerabilities in Java applications with static analysis [C] //Proc of the 14th USENIX Security Symp. Berkeley, CA: USENIX Association, 2005: 271−286
    [11]
    Avots D, Dalton M, Livshits V B, et al. Improving software security with a C pointer analysis [C] //Proc of the 27th Int Conf on Software Engineering. New York: ACM, 2005: 332−341
    [12]
    Li Yue, Tan Tian, Zhang Yifei, et al. Program tailoring: Slicing by sequential criteria [C] //Proc of the 30th European Conf on Object-Oriented Programming. Wadern, Saarland: Schloss Dagstuhl - Leibniz-Zentrum Fuer Informatik, 2016, 15: 1−15: 27
    [13]
    Sridharan M, Fink S J, Bodík R. Thin slicing [C] //Proc of the 28th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2007: 112−122
    [14]
    Fink S J, Yahav E, Dor N, et al. Effective typestate verification in the presence of aliasing [J]. ACM Transactions on Software Engineering and Methodology, 2008, 17(2): 1−34
    [15]
    Pradel M, Jaspan C, Aldrich J, et al. Statically checking API protocol conformance with mined multi-object specifications [C] //Proc of the 34th Int Conf on Software Engineering. Piscataway, NJ: IEEE, 2012: 925−935
    [16]
    Lhoták O, Smaragdakis Y, Sridharan M. Pointer analysis (Dagstuhl Seminar 13162) [R]. Wadern, Saarland: Schloss Dagstuhl - Leibniz-Zentrum Fuer Informatik, 2013
    [17]
    Weihl W E. Interprocedural data flow analysis in the presence of pointers, procedure variables, and label variables [C] //Proc of the 7th ACM SIGPLAN-SIGACT Symp on Principles of Programming Languages. New York: ACM, 1980: 83−94
    [18]
    Lhoták O, Hendren L J. Scaling Java points-to analysis using SPARK [G] //LNCS 2622: Proc of the 12th Int Conf on Compiler Construction. Berlin: Springer, 2003: 153−169
    [19]
    Bravenboer M, Smaragdakis Y. Strictly declarative specification of sophisticated points-to analyses [C] //Proc of the 24th Annual ACM SIGPLAN Conf on Object-Oriented Programming, Systems, Languages, and Applications. New York: ACM, 2009: 243−262
    [20]
    Li Yue, Tan Tian, Møller A, et al. A principled approach to selective context sensitivity for pointer analysis [J]. ACM Transactions on Programming Languages and Systems, 2020, 42(2): 1−40
    [21]
    Andersen L O. Program analysis and specialization for the C programming language [D]. Copenhagen: University of Copenhagen, DIKU, 1994
    [22]
    Hardekopf B, Lin C. The ant and the grasshopper: Fast and accurate pointer analysis for millions of lines of code [C] //Proc of the 28th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2007: 290−299
    [23]
    Hardekopf B, Lin C. Semi-sparse flow-sensitive pointer analysis [C] //Proc of the 36th Annual ACM SIGPLAN-SIGACT Symp on Principles of Programming Languages. New York: ACM, 2009: 226−238
    [24]
    Yu Hongtao, Xue Jingling, Huo Wei, et al. Level by level: Making flow- and context-sensitive pointer analysis scalable for millions of lines of code [C] //Proc of the 8th Annual IEEE/ACM Int Symp on Code Generation and Optimization. New York: ACM, 2010: 218−229
    [25]
    Sui Yulei, Xue Jingling. SVF: Interprocedural static value-flow analysis in LLVM [C] //Proc of the 25th Int Conf on Compiler Construction. New York: ACM, 2016: 265−266
    [26]
    Hind M. Pointer analysis: Haven't we solved this problem yet? [C] //Proc of the 2001 ACM SIGPLAN-SIGSOFT Workshop on Program Analysis For Software Tools and Engineering. New York: ACM, 2001: 54−61
    [27]
    Gosling J, Joy B, Steele G, et al. The Java® Language Specification, Java SE 17 Edition [S/OL]. Austin, Texas: Oracle America, Inc. , 2021. [2022-10-20].https://docs.oracle.com/javase/specs/jls/se17/html/index.html
    [28]
    Fähndrich M, Foster J S, Su Zhendong, et al. Partial online cycle elimination in inclusion constraint graphs [C] //Proc of the ACM SIGPLAN 1998 Conf on Programming Language Design and Implementation. New York: ACM, 1998: 85−96
    [29]
    Pearce D J, Kelly P H J, Hankin C. Online cycle detection and difference propagation for pointer analysis [C] //Proc of the 3rd IEEE Int Workshop on Source Code Analysis and Manipulation. Piscataway, NJ: IEEE, 2003: 3−12
    [30]
    Sharir M, Pnueli A. Two approaches to interprocedural data flow analysis [R]. New York: New York University, 1978
    [31]
    Lhoták O, Hendren L J. Context-sensitive points-to analysis: Is it worth it? [G] //LNCS 3923: Proc of the 15th Int Conf on Compiler Construction. Berlin: Springer, 2006: 47−64
    [32]
    Whaley J, Lam M S. Cloning-based context-sensitive pointer alias analysis using binary decision diagrams[J]. ACM SIGPLAN Notices, 2004, 39(6): 131−144 doi: 10.1145/996893.996859
    [33]
    Xu Guoqing, Rountev A. Merging equivalent contexts for scalable heap-cloning-based context-sensitive points-to analysis [C] //Proc of the 2008 Int Symp on Software Testing and Analysis. New York: ACM, 2008: 225−236
    [34]
    Tan Tian, Li Yue, Ma Xiaoxing, et al. Making pointer analysis more precise by unleashing the power of selective context sensitivity [J]. Proceedings of the ACM on Programming Languages, 2021, 5(OOPSLA): 1−27
    [35]
    Tan Tian, Li Yue. Tai-e: A static analysis framework for Java by harnessing the best designs of classics [J]. arXiv preprint, arXiv: 2208,00337
    [36]
    Tan Tian, Li Yue. Tai-e: An easy-to-learn/use static analysis framework for Java [CP/OL]. [2022-10-22].https://github.com/pascal-lab/Tai-e
    [37]
    He Dongjie, Lu Jingbo, Xue Jingling. Qilin: A new framework for supporting fine-grained context-sensitivity in Java pointer analysis [C] //Proc of the 36th European Conf on Object-Oriented Programming. Wadern, Saarland: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2022, 30: 1−29
    [38]
    Smaragdakis Y. Doop: Framework for Java pointer and taint analysis (using P/Taint) [CP/OL]. [2022-10-22].https://bitbucket.org/yanniss/doop
    [39]
    Bodden E. Soot: A framework for analyzing and transforming Java and Android applications [CP/OL]. [2022-10-22]. http://soot-oss.github.io/soot/
    [40]
    Shivers O G. Control-flow analysis of higher-order languages [D]. Pittsburgh, PA: Carnegie Mellon University, 1991
    [41]
    Might M, Smaragdakis Y, Horn D V. Resolving and exploiting the k-CFA paradox: Illuminating functional vs. object-oriented program analysis [C] //Proc of the 31st ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2010: 305−315
    [42]
    Milanova A, Rountev A, Ryder B G. Parameterized object sensitivity for points-to and side-effect analyses for Java [C] //Proc of the 2002 Int Symp on Software Testing and Analysis. New York: ACM, 2002: 1−11
    [43]
    Milanova A, Rountev A, Ryder B G. Parameterized object sensitivity for points-to analysis for Java[J]. ACM Transactions on Software Engineering and Methodology, 2002, 14(1): 1−41
    [44]
    Smaragdakis Y, Bravenboer M, Lhoták O. Pick your contexts well: Understanding object-sensitivity [C] //Proc of the 38th Annual ACM SIGPLAN-SIGACT Symp on Principles of Programming Languages. New York: ACM, 2011: 17−30
    [45]
    Kastrinis G, Smaragdakis Y. Hybrid context-sensitivity for points-to analysis [C] //Proc of the 34th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2013: 423−434
    [46]
    Tan Tian, Li Yue, Xue Jingling. Making k-object-sensitive pointer analysis more precise with still k-limiting [G] //LNCS 9837: Proc of the 2016 Int Static Analysis Symp. Berlin: Springer, 2016: 489−510
    [47]
    Jeon M, Jeong S, Oh H. Precise and scalable points-to analysis via data-driven context tunneling [J]. Proceedings of the ACM on Programming Languages, 2018, 2(OOPSLA): 1−29
    [48]
    Jeong S, Jeon M, Cha S, et al. Data-driven context-sensitivity for points-to analysis [J]. Proceedings of the ACM on Programming Languages, 2017, 1(OOPSLA): 1−28
    [49]
    Jeon M, Oh H. Return of CFA: Call-site sensitivity can be superior to object sensitivity even for object-oriented programs [J]. Proceedings of the ACM on Programming Languages, 2022, 6(POPL): 1−29
    [50]
    Smaragdakis Y, Kastrinis G, Balatsouras G. Introspective analysis: Context-sensitivity, across the board [C] //Proc of the 35th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2014: 485−495
    [51]
    Li Yue, Tan Tian, Møller A, et al. Precision-guided context sensitivity for pointer analysis [J]. Proceedings of the ACM on Programming Languages, 2018, 2(OOPSLA): 1−29
    [52]
    Lu Jingbo, Xue Jingling. Precision-preserving yet fast object-sensitive pointer analysis with partial context sensitivity [J]. Proceedings of the ACM on Programming Languages, 2019, 3(OOPSLA): 1−29
    [53]
    Lu Jingbo, He Dongjie, Xue Jingling. Eagle: CFL-reachability-based precision-preserving acceleration of object-sensitive pointer analysis with partial context sensitivity [J]. ACM Transactions on Software Engineering and Methodology, 2021, 30(4): 1−46
    [54]
    He Dongjie, Lu Jingbo, Gao Yaoqing, et al. Accelerating object-sensitive pointer analysis by exploiting object containment and reachability [C] //Proc of the 35th European Conf on Object-Oriented Programming. Wadern, Saarland: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2021, 16: 1−31
    [55]
    Li Yue, Tan Tian, Møller A, et al. Scalability-first pointer analysis with self-tuning context-sensitivity [C] //Proc of the 2018 26th ACM Joint Meeting on European Software Engineering Conf and Symp on the Foundations of Software Engineering. New York: ACM, 2018: 129−140
    [56]
    Tan Tian, Li Yue, Xue Jingling. Efficient and precise points-to analysis: Modeling the heap by merging equivalent automata [C] //Proc of the 38th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2017: 278−291
    [57]
    Jeon M, Lee M, Oh H. Learning graph-based heuristics for pointer analysis without handcrafting application-specific features [J]. Proceedings of the ACM on Programming Languages, 2020, 4(OOPSLA): 1−30
    [58]
    Kanvar V, Khedker U P. Heap Abstractions for Static Analysis [J]. ACM Computing Surveys, 2017, 49(2): 1−47
    [59]
    Chen Yifan, Yang Chenyang, Zhang Xin, et al. Accelerating program analyses in Datalog by merging library facts [G] //LNCS 12913: Proc of the 2021 Int Static Analysis Symp. Berlin: Springer, 2021: 77−101
    [60]
    IBM T. J. Watson Research Center. WALA: T. J. Watson Libraries for Analysis [CP/OL]. [2022-10-22].https://github.com/wala/WALA
    [61]
    Hopcroft J E, Karp R M. A linear algorithm for testing equivalence of finite automata [R]. Ithaca, NY: Cornell University, 1971
    [62]
    Späth J, Do L N Q, Ali K, et al. Boomerang: Demand-driven flow- and context-sensitive pointer analysis for Java [C] //Proc of the 30th European Conf on Object-Oriented Programming. Wadern, Saarland: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2016, 22: 1−26
    [63]
    Livshits B, Sridharan M, Smaragdakis Y, et al. In defense of soundiness: A manifesto[J]. Communications of the ACM, 2015, 58(2): 44−46 doi: 10.1145/2644805
    [64]
    Rastogi V, Chen Yan, Jiang Xuxian. DroidChameleon: Evaluating Android anti-malware against transformation attacks [C] //Proc of the 8th ACM SIGSAC Symp on Information, Computer and Communications Security. New York: ACM, 2013: 329−334
    [65]
    Landman D, Serebrenik A, Vinju J J. Challenges for static analysis of Java reflection: Literature review and empirical study [C] //Proc of the IEEE/ACM 39th Int Conf on Software Engineering. Piscataway, NJ: IEEE, 2017: 507−518
    [66]
    Barros P, Just R, Millstein S, et al. Static analysis of implicit control flow: Resolving Java reflection and Android intents (T) [C] //Proc of the 30th IEEE/ACM Int Conf on Automated Software Engineering. Piscataway, NJ: IEEE, 2015: 669−679
    [67]
    Livshits B, Whaley J, Lam M S. Reflection analysis for Java [G] //LNCS 3780: Proc of the 2005 Asian Symp on Programming Languages and Systems. Berlin: Springer, 2005: 139−160
    [68]
    Li Yue, Tan Tian, Sui Yulei, et al. Self-inferencing reflection resolution for Java [G] //LNCS 8586: Proc of the 2014 European Conf on Object-Oriented Programming. Berlin: Springer, 2014: 27−53
    [69]
    Li Yue, Tan Tian, Xue Jingling. Effective soundness-guided reflection analysis [G] //LNCS 9291: Proc of the 2015 Int Static Analysis Symp. Berlin: Springer, 2015: 162−180
    [70]
    Li Yue, Tan Tian, Xue Jingling. Understanding and analyzing Java reflection [J]. ACM Transactions on Software Engineering and Methodology, 2019, 28(2): 1−50
    [71]
    Oracle America, Inc. . Java Native Interface Specification Contents [S/OL]. Austin, Texas: Oracle America, Inc., 2021 [2022-10-22].https://docs.oracle.com/en/java/javase/17/docs/specs/jni/index.html
    [72]
    Fourtounis G, Triantafyllou L, Smaragdakis Y. Identifying Java calls in native code via binary scanning [C] //Proc of the 29th ACM SIGSOFT Int Symp on Software Testing and Analysis. New York: ACM, 2020: 388−400
    [73]
    Christakis M, Bird C. What developers want and need from program analysis: An empirical study [C] //Proc of the 31st IEEE/ACM Int Conf on Automated Software Engineering. New York: ACM, 2016: 332−343
    [74]
    Bravenboer M, Smaragdakis Y. Exception analysis and points-to analysis: Better together [C] //Proc of the 18th Int Symp on Software Testing and Analysis. New York: ACM, 2009: 1−12
    [75]
    Kastrinis G, Smaragdakis Y. Efficient and effective handling of exceptions in Java points-to analysis [G] //LNCS 7791: Proc of the 2013 Int Conf on Compiler Construction. Berlin: Springer, 2013: 41−60
    [76]
    Fourtounis G, Smaragdakis Y. Deep static modeling of invoke dynamic [C] //Proc of the 33rd European Conf on Object-Oriented Programming. Wadern, Saarland: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2019, 15: 1−28
    [77]
    Heintze N, Tardieu O. Demand-driven pointer analysis [C] //Proc of the 2001 ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2001: 24−34
    [78]
    Sridharan M, Gopan D, Shan L, et al. Demand-driven points-to analysis for Java [C] //Proc of the 20th Annual ACM SIGPLAN Conf on Object-Oriented Programming, Systems, Languages, and Applications. New York: ACM, 2005: 59−76
    [79]
    Sridharan M, Bodík R. Refinement-based context-sensitive points-to analysis for Java [C] //Proc of the 27th ACM SIGPLAN Conf on Programming Language Design and Implementation. New York: ACM, 2006: 387−400
    [80]
    Yan Dacong, Xu Guoqing, Rountev A. Demand-driven context-sensitive alias analysis for Java [C] //Proc of the 2011 Int Symp on Software Testing and Analysis. New York: ACM, 2011: 155−165
    [81]
    Xu Guoqing, Rountev A, Sridharan M. Scaling CFL-reachability-based points-to analysis using context-sensitive must-not-alias analysis [G] //LNCS 5653: Proc of the 2009 European Conf on Object-Oriented Programming. Berlin: Springer, 2009: 98−122
    [82]
    Shang Lei, Xie Xinwei, Xue Jingling. On-demand dynamic summary-based points-to analysis [C] //Proc of the 10th Int Symp on Code Generation and Optimization. New York: ACM, 2012: 264−274
    [83]
    Reps T, Horwitz S, Sagiv M. Precise interprocedural dataflow analysis via graph reachability [C] //Proc of the 22nd ACM SIGPLAN-SIGACT Symp on Principles of Programming Languages. New York: ACM, 1995: 49−61
    [84]
    Lu Yi, Shang Lei, Xie Xinwei, et al. An incremental points-to analysis with CFL-reachability [G] //LNCS 7791: Proc of the 2013 Int Conf on Compiler Construction. Berlin: Springer, 2013: 61−81
    [85]
    Liu Bozhen, Huang J, Rauchwerger L. Rethinking incremental and parallel pointer analysis [J]. ACM Transactions on Programming Languages and Systems, 2019, 41(1): 1−31
    [86]
    Liu Bozhen, Huang J. SHARP: Fast incremental context-sensitive pointer analysis for Java [J]. Proceedings of the ACM on Programming Languages, 2022, 6(OOPSLA1): 1−28
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