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Artículos de revistas sobre el tema "Pointer Analysi"

Autor: Grafiati

Publicado: 11 de marzo de 2023

Última modificación: 19 de julio de 2025

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1

Shields, Denis C., Andrew Collins, and Angela Marlow. "Coding of pointers in the segregation analysis program POINTER." Genetic Epidemiology 11, no. 4 (1994): 385–87. http://dx.doi.org/10.1002/gepi.1370110408.

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2

Zhou, Jinmeng, Ziyue Pan, Wenbo Shen, Xingkai Wang, Kangjie Lu, and Zhiyun Qian. "Type-Alias Analysis: Enabling LLVM IR with Accurate Types." Proceedings of the ACM on Software Engineering 2, ISSTA (2025): 2203–26. https://doi.org/10.1145/3728974.

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LLVM Intermediate Representation (IR) underpins the LLVM compiler infrastructure, offering a strong type system and a static single-assignment (SSA) form that are well-suited for program analysis. However, its single-type design assigns exactly one type to each IR variable, even when the variable may legitimately correspond to multiple types. The recent introduction of opaque pointers exacerbates this limitation: all pointers in the IR are uniformly represented with a generic pointer type (ptr) that erases concrete pointee type information, making many type-based analyses ineffective. To address the limitations of single-type design, we introduce type-alias analysis, a multiple-type design that maintains type-alias sets for IR variables and infers types across IR instructions. We have developed TypeCopilot, a prototype that recovers concrete pointee types for opaque-pointer-enabled LLVM IR generated from C programs. TypeCopilot achieves 98.57% accuracy with 94.98% coverage, allowing existing analysis tools to retain their effectiveness despite the adoption of opaque pointers. To foster further research and security applications, we have open-sourced TypeCopilot, providing the community with a practical foundation for precise, type-aware security analyses on modern LLVM IR.

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3

Zou, Changwei, Dongjie He, Yulei Sui, and Jingling Xue. "TIPS: Tracking Integer-Pointer Value Flows for C++ Member Function Pointers." Proceedings of the ACM on Software Engineering 1, FSE (2024): 1609–31. http://dx.doi.org/10.1145/3660779.

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C++ is crucial in software development, providing low-level memory control for performance and supporting object-oriented programming to construct modular, reusable code structures. Consequently, tackling pointer analysis for C++ becomes challenging, given the need to address these two fundamental features. A relatively unexplored research area involves the handling of C++ member function pointers. Previous efforts have tended to either disregard this feature or adopt a conservative approach, resulting in unsound or imprecise results. C++ member function pointers, handling both virtual (via virtual table indexes) and non-virtual functions (through addresses), pose a significant challenge for pointer analysis due to the mix of integers and pointers, often resulting in unsound or imprecise analysis. We introduce TIPS, the first pointer analysis that effectively manages both pointers and integers, offering support for C++ member function pointers by tracking their value flows. Our evaluation on TIPS demonstrates its accuracy in identifying C++ member function call targets, a task where other tools falter, across fourteen large C++ programs from SPEC CPU, Qt, LLVM, Ninja, and GoogleTest, while maintaining low analysis overhead. In addition, our micro-benchmark suite, complete with ground truth data, allows for precise evaluation of points-to information for C++ member function pointers across various inheritance scenarios, highlighting TIPS's precision enhancements.

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4

Matsumoto, Aki, D. S. Han, and Takao Tsuda. "Alias analysis of pointers in Pascal and Fortran 90: dependence analysis between pointer references." Acta Informatica 33, no. 2 (1996): 99–130. http://dx.doi.org/10.1007/s002360050037.

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5

Smaragdakis, Yannis, and George Balatsouras. "Pointer Analysis." Foundations and Trends® in Programming Languages 2, no. 1 (2015): 1–69. http://dx.doi.org/10.1561/2500000014.

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6

Smaragdakis, Yannis, and George Balatsouras. "Pointer Analysis." Foundations and Trends® in Programming Languages 2, no. 1 (2015): 1–69. https://doi.org/10.1561/2500000014.

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Pointer analysis is a fundamental static program analysis, with a rich literature and wide applications. The goal of pointer analysis is to compute an approximation of the set of program objects that a pointer variable or expression can refer to. We present an introduction and survey of pointer analysis techniques, with an emphasis on distilling the essence of common analysis algorithms. To this end, we focus on a declarative presentation of a common core of pointer analyses: algorithms are modeled as configurable, yet easy-to-follow, logical specifications. The specifications serve as a starting point for a broader discussion of the literature, as independent threads spun from the declarative model.

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7

Wang, Shao-Chung, Lin-Ya Yu, Li-An Her, Yuan-Shin Hwang, and Jenq-Kuen Lee. "Pointer-Based Divergence Analysis for OpenCL 2.0 Programs." ACM Transactions on Parallel Computing 8, no. 4 (2021): 1–23. http://dx.doi.org/10.1145/3470644.

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A modern GPU is designed with many large thread groups to achieve a high throughput and performance. Within these groups, the threads are grouped into fixed-size SIMD batches in which the same instruction is applied to vectors of data in a lockstep. This GPU architecture is suitable for applications with a high degree of data parallelism, but its performance degrades seriously when divergence occurs. Many optimizations for divergence have been proposed, and they vary with the divergence information about variables and branches. A previous analysis scheme viewed pointers and return values from functions as divergence directly, and only focused on OpenCL 1.x. In this article, we present a novel scheme that reports the divergence information for pointer-intensive OpenCL programs. The approach is based on extended static single assignment (SSA) and adds some special functions and annotations from memory SSA and gated SSA. The proposed scheme first constructs extended SSA, which is then used to build a divergence relation graph that includes all of the possible points-to relationships of the pointers and initialized divergence states. The divergence state of the pointers can be determined by propagating the divergence state of the divergence relation graph. The scheme is further extended for interprocedural cases by considering function-related statements. The proposed scheme was implemented in an LLVM compiler and can be applied to OpenCL programs. We analyzed 10 programs with 24 kernels, with a total analyzed program size of 1,306 instructions in an LLVM intermediate representation, with 885 variables, 108 branches, and 313 pointer-related statements. The total number of divergent pointers detected was 146 for the proposed scheme, 200 for the scheme in which the pointer was always divergent, and 155 for the current LLVM default scheme; the total numbers of divergent variables detected were 458, 519, and 482, respectively, with 31, 34, and 32 divergent branches. These experimental results indicate that the proposed scheme is more precise than both a scheme in which a pointer is always divergent and the current LLVM default scheme.

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8

Hirzel, Martin, Daniel Von Dincklage, Amer Diwan, and Michael Hind. "Fast online pointer analysis." ACM Transactions on Programming Languages and Systems 29, no. 2 (2007): 11. http://dx.doi.org/10.1145/1216374.1216379.

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9

Zhu, Jianwen, and Silvian Calman. "Symbolic pointer analysis revisited." ACM SIGPLAN Notices 39, no. 6 (2004): 145–57. http://dx.doi.org/10.1145/996893.996860.

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10

Peng-Sheng Chen, Yuan-Shin Hwang, R. D. C. Ju, and J. K. Lee. "Interprocedural probabilistic pointer analysis." IEEE Transactions on Parallel and Distributed Systems 15, no. 10 (2004): 893–907. http://dx.doi.org/10.1109/tpds.2004.56.

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11

Spoto, Fausto. "Precise null-pointer analysis." Software & Systems Modeling 10, no. 2 (2009): 219–52. http://dx.doi.org/10.1007/s10270-009-0132-5.

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12

Heintze, Nevin, and Olivier Tardieu. "Demand-driven pointer analysis." ACM SIGPLAN Notices 36, no. 5 (2001): 24–34. http://dx.doi.org/10.1145/381694.378802.

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13

Hind, Michael, Michael Burke, Paul Carini, and Jong-Deok Choi. "Interprocedural pointer alias analysis." ACM Transactions on Programming Languages and Systems 21, no. 4 (1999): 848–94. http://dx.doi.org/10.1145/325478.325519.

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14

Petri, Aspasia, and Efthymios Karabetsos. "Extreme output power emitted by commercial laser pointers in Greece: A solely local or a global issue?" Journal of Laser Applications 34, no. 2 (2022): 022025. http://dx.doi.org/10.2351/7.0000664.

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Although several countries have enacted legislation that prohibits the commercial distribution of laser pointers, high-power laser pointers are abundantly available on the market. The Greek Atomic Energy Commission (EEAE) investigated the compliance of laser pointers with the laser radiation safety limits in order to apply risk management actions in Greece. Compliance was assessed based on the laser pointer labeled data and in-house optical measurements. Laser pointers were purchased randomly from domestic physical marketplaces. Output power measurements revealed the risk of overexposure compared to the lasers' radiation safety limit values. Measured values also exceeded the output power values claimed on the laser pointer labels; hence, they may pose an unexpected more severe hazard than stated. Spectral analysis revealed an additional emission wavelength by the green laser pointers. All the inspected laser pointers declared “compliance with 21 CFR” and were labeled according to the U.S. 21 CFR 1040.1, although all consumer products available on the Greek market should comply with the relevant EU Directives, harmonized standards, and national legislation. These local findings adhere to a global issue—the unambiguous exposure of nonexpert users to high-power laser pointers that should not be commercially available. Overexposure to laser pointer radiation suppresses national borders as markets are not locally constrained, and laser technology continues to advance. The introduction of a universal laser safety language and a universal regulatory framework for laser pointers might facilitate product safety, international trade, and users' safety. EEAE runs social media campaigns to raise public awareness of laser pointer risks, using specifically designed infographics.

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15

Thiessen, Rei, and Ondřej Lhoták. "Context transformations for pointer analysis." ACM SIGPLAN Notices 52, no. 6 (2017): 263–77. http://dx.doi.org/10.1145/3140587.3062359.

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16

Rugina, Radu, and Martin Rinard. "Pointer analysis for multithreaded programs." ACM SIGPLAN Notices 34, no. 5 (1999): 77–90. http://dx.doi.org/10.1145/301631.301645.

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17

Jianwen Zhu and S. Calman. "Context sensitive symbolic pointer analysis." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 24, no. 4 (2005): 516–31. http://dx.doi.org/10.1109/tcad.2005.844092.

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18

Horwitz, S., P. Pfeiffer, and T. Reps. "Dependence analysis for pointer variables." ACM SIGPLAN Notices 24, no. 7 (1989): 28–40. http://dx.doi.org/10.1145/74818.74821.

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19

Brauer, Jörg, Ralf Huuck, and Bastian Schlich. "Interprocedural Pointer Analysis in Goanna." Electronic Notes in Theoretical Computer Science 254 (October 2009): 65–83. http://dx.doi.org/10.1016/j.entcs.2009.09.060.

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20

Vivien, Frédéric, and Martin Rinard. "Incrementalized pointer and escape analysis." ACM SIGPLAN Notices 36, no. 5 (2001): 35–46. http://dx.doi.org/10.1145/381694.378804.

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21

Machiry, Aravind, John Kastner, Matt McCutchen, Aaron Eline, Kyle Headley, and Michael Hicks. "C to checked C by 3c." Proceedings of the ACM on Programming Languages 6, OOPSLA1 (2022): 1–29. http://dx.doi.org/10.1145/3527322.

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Owing to the continued use of C (and C++), spatial safety violations (e.g., buffer overflows) still constitute one of today's most dangerous and prevalent security vulnerabilities. To combat these violations, Checked C extends C with bounds-enforced checked pointer types. Checked C is essentially a gradually typed spatially safe C - checked pointers are backwards-binary compatible with legacy pointers, and the language allows them to be added piecemeal, rather than necessarily all at once, so that safety retrofitting can be incremental. This paper presents a semi-automated process for porting a legacy C program to Checked C. The process centers on 3C, a static analysis-based annotation tool. 3C employs two novel static analysis algorithms - typ3c and boun3c - to annotate legacy pointers as checked pointers, and to infer array bounds annotations for pointers that need them. 3C performs a root cause analysis to direct a human developer to code that should be refactored; once done, 3C can be re-run to infer further annotations (and updated root causes). Experiments on 11 programs totaling 319KLoC show 3C to be effective at inferring checked pointer types, and experience with previously and newly ported code finds 3C works well when combined with human-driven refactoring.

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22

Hind, Michael, and Anthony Pioli. "Which pointer analysis should I use?" ACM SIGSOFT Software Engineering Notes 25, no. 5 (2000): 113–23. http://dx.doi.org/10.1145/347636.348916.

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23

Hardekopf, Ben, and Calvin Lin. "Semi-sparse flow-sensitive pointer analysis." ACM SIGPLAN Notices 44, no. 1 (2009): 226–38. http://dx.doi.org/10.1145/1594834.1480911.

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24

Rugina, Radu, and Martin C. Rinard. "Pointer analysis for structured parallel programs." ACM Transactions on Programming Languages and Systems 25, no. 1 (2003): 70–116. http://dx.doi.org/10.1145/596980.596982.

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25

Liu, Bozhen, Jeff Huang, and Lawrence Rauchwerger. "Rethinking Incremental and Parallel Pointer Analysis." ACM Transactions on Programming Languages and Systems 41, no. 1 (2019): 1–31. http://dx.doi.org/10.1145/3293606.

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26

Liang, Yufei, Teng Zhang, Ganlin Li, et al. "Pointer Analysis for Database-Backed Applications." Proceedings of the ACM on Programming Languages 9, PLDI (2025): 1417–41. https://doi.org/10.1145/3729307.

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Database-backed applications form the backbone of modern software, yet their complexity poses significant challenges for static analysis. These applications involve intricate interactions among application code, diverse database frameworks such as JDBC, Hibernate, and Spring Data JPA, and languages like Java and SQL. In this paper, we introduce DBridge, the first pointer analysis specifically designed for Java database-backed applications, capable of statically constructing comprehensive Java-to-database value flows. DBridge unifies application code analysis, database access specification modeling, SQL analysis, and database abstraction within a single pointer analysis framework, capturing interactions across a wide range of database access APIs and frameworks. Additionally, we present DB-Micro, a new micro-benchmark suite with 824 test cases crafted to systematically evaluate static analysis for database-backed applications. Experiments on DB-Micro and large, complex, real-world applications demonstrate DBridge's effectiveness, achieving high recall and precision in building Java-to-database value flows efficiently and outperforming state-of-the-art tools in SQL statement identification. To further validate DBridge's utility, we develop three client analyses for security and program understanding. Evaluation on these real-world applications reveals 30 Stored XSS attack vulnerabilities and 3 horizontal broken access control vulnerabilities, all previously undiscovered and real, as well as a high detection rate in impact analysis for schema changes. By open-sourcing DBridge (14K LoC) and DB-Micro (22K LoC), we seek to help advance static analysis for modern database-backed applications in the future.

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27

ÇEZİKTÜRK, Özlem. "CONTENT ANALYSIS OF STUDENTS' CONSTRUCTIONS OF EIGHT POINTED, TEN POINTED, AND TWELVE POINTED STARS." Journal of Academic Social Sciences 75, no. 75 (2018): 244–54. http://dx.doi.org/10.16992/asos.13978.

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28

Abdulhadi, Zayid, and Walter Hengartner. "One Pointed Univalent Logharmonic Mappings." Journal of Mathematical Analysis and Applications 203, no. 2 (1996): 333–51. http://dx.doi.org/10.1006/jmaa.1996.0383.

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29

El Abbadi, Nidhal. "New Algorithm for Text in Text Steganography." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 26, 2021): 99–112. http://dx.doi.org/10.55562/jrucs.v23i2.483.

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Steganography is a technique to hide secret information in some other data (we call it a cover) without leaving any apparent evidence of data alteration. All of the traditional steganographic techniques have limited information- hiding capacity. They can hide only 10% (or less) of the data amounts of the cover. While much of the recent research in steganography has been on hiding data in images, many of the solutions that for images are more complicated when applied to natural language text as a cover medium. Many approaches to steganalysis attempt to detect statistical anomalies in cover data which predict the presence of hidden information. Natural language cover texts must not only pass the statistical muster of automatic analysis, but also the minds of human readers. This paper present a new algorithm to hide a large amount of text in cover text without effecting the cover, by using many types of pointers ( which are characters can interpreter as invisible character, or as apart of cover. Pointers used as single pointer or set of pointer to represent new single pointer. In this algorithm we can hide more than 40% of the data amounts of the cover.

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30

Li, Yue, Tian Tan, Anders Møller, and Yannis Smaragdakis. "Precision-guided context sensitivity for pointer analysis." Proceedings of the ACM on Programming Languages 2, OOPSLA (2018): 1–29. http://dx.doi.org/10.1145/3276511.

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31

Zhang, Sean, Barbara G. Ryder, and William A. Landi. "Experiments with combined analysis for pointer aliasing." ACM SIGPLAN Notices 33, no. 7 (1998): 11–18. http://dx.doi.org/10.1145/277633.277635.

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32

Pearce, David J., Paul H. J. Kelly, and Chris Hankin. "Efficient field-sensitive pointer analysis of C." ACM Transactions on Programming Languages and Systems 30, no. 1 (2007): 4. http://dx.doi.org/10.1145/1290520.1290524.

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33

Salcianu, Alexandru, and Martin Rinard. "Pointer and escape analysis for multithreaded programs." ACM SIGPLAN Notices 36, no. 7 (2001): 12–23. http://dx.doi.org/10.1145/568014.379553.

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34

Guyer, Samuel Z., and Calvin Lin. "Error checking with client-driven pointer analysis." Science of Computer Programming 58, no. 1-2 (2005): 83–114. http://dx.doi.org/10.1016/j.scico.2005.02.005.

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35

Scholz, Berhard, Johann Blieberger, and Thomas Fahringer. "Symbolic pointer analysis for detecting memory leaks." ACM SIGPLAN Notices 34, no. 11 (1999): 104–13. http://dx.doi.org/10.1145/328691.328704.

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36

Das, Manuvir. "Unification-based pointer analysis with directional assignments." ACM SIGPLAN Notices 35, no. 5 (2000): 35–46. http://dx.doi.org/10.1145/358438.349309.

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37

Cheng, Ben-Chung, and Wen-Mei W. Hwu. "Modular interprocedural pointer analysis using access paths." ACM SIGPLAN Notices 35, no. 5 (2000): 57–69. http://dx.doi.org/10.1145/358438.349311.

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38

Da Silva, Jeff, and J. Gregory Steffan. "A probabilistic pointer analysis for speculative optimizations." ACM SIGOPS Operating Systems Review 40, no. 5 (2006): 416–25. http://dx.doi.org/10.1145/1168917.1168908.

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39

Da Silva, Jeff, and J. Gregory Steffan. "A probabilistic pointer analysis for speculative optimizations." ACM SIGPLAN Notices 41, no. 11 (2006): 416–25. http://dx.doi.org/10.1145/1168918.1168908.

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40

Da Silva, Jeff, and J. Gregory Steffan. "A probabilistic pointer analysis for speculative optimizations." ACM SIGARCH Computer Architecture News 34, no. 5 (2006): 416–25. http://dx.doi.org/10.1145/1168919.1168908.

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41

Ma, Wenjie, Shengyuan Yang, Tian Tan, Xiaoxing Ma, Chang Xu, and Yue Li. "Context Sensitivity without Contexts: A Cut-Shortcut Approach to Fast and Precise Pointer Analysis." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 539–64. http://dx.doi.org/10.1145/3591242.

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Over the past decades, context sensitivity has been considered as one of the most effective ideas for improving the precision of pointer analysis for Java. Different from the extremely fast context-insensitivity approach, context sensitivity requires every program method to be analyzed under different contexts for separating the static abstractions of different dynamic instantiations of the method’s variables and heap objects, and thus reducing spurious object flows introduced by method calls. However, despite great precision benefits, as each method is equivalently cloned and analyzed under each context, context sensitivity brings heavy efficiency costs. Recently, numerous selective context-sensitive approaches have been put forth for scaling pointer analysis to large and complex Java programs by applying contexts only to the selected methods while analyzing the remaining ones context-insensitively; however, because the selective approaches do not fundamentally alter the primary methodology of context sensitivity (and do not thus remove its efficiency bottleneck), they produce much improved but still limited results. In this work, we present a fundamentally different approach called Cut-Shortcut for fast and precise pointer analysis for Java. Its insight is simple: the main effect of cloning methods under different contexts is to filter spurious object flows that have been merged inside a callee method; from the view of a typical pointer flow graph (PFG), such effect can be simulated by cutting off (Cut) the edges that introduce precision loss to certain pointers and adding Shortcut edges directly from source pointers to the target ones circumventing the method on PFG. As a result, we can achieve the effect of context sensitivity without contexts. We identify three general program patterns and develop algorithms based on them to safely cut off and add shortcut edges on PFG, formalize them and formally prove the soundness. To comprehensively validate Cut-Shortcut’s effectiveness, we implement two versions of Cut-Shortcut for two state-of-the-art pointer analysis frameworks for Java, one in Datalog for the declarative Doop and the other in Java for the imperative Tai-e, and we consider all the large and complex programs used in recent literatures that meet the experimental requirements. The evaluation results are extremely promising: Cut-Shortcut is even able to run faster than context insensitivity for most evaluated programs while obtaining high precision that is comparable to context sensitivity (if scalable) in both frameworks. This is for the first time that we have been able to achieve such a good efficiency and precision trade-off for those hard-to-analyze programs, and we hope Cut-Shortcut could offer new perspectives for developing more effective pointer analysis for Java in the future.

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42

Liu, Xia, Ming Lei, Hu Wei Liu, Yin Cao, and Xiao Hong Gao. "Risk Analysis of Quality and Safety for Laser Pointer with Remote Control." Advanced Materials Research 968 (June 2014): 263–66. http://dx.doi.org/10.4028/www.scientific.net/amr.968.263.

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Multimedia teaching and lecturing have been widely used nowadays, and then the technology of laser pointer with remote control appears in order to satisfy the requirements. The radiation power of some laser pointers is large, which may be lead to impaired eyes due to improper use. The hazard of laser radiation was analyzed. The standards, laws and regulations of the domestic and foreign were compared. Some suggestions and measures of perfecting the relative standards, strengthening consumers' education and industry rectification were provided.

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43

Lyde, Steven, William E. Byrd, and Matthew Might. "Control-flow analysis of dynamic languages via pointer analysis." ACM SIGPLAN Notices 51, no. 2 (2016): 54–62. http://dx.doi.org/10.1145/2936313.2816712.

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44

Ellyana, Rossy Lydia, and I. Wayan Angga Wijaya Kusuma. "Penentuan Indeks Bias Kaca Berdasarkan Pola Interferensi Cahaya Laser Terhambur Menggunakan Cermin Datar “Berdebu”." Jurnal Teori dan Aplikasi Fisika 7, no. 2 (2019): 169–78. http://dx.doi.org/10.23960/jtaf.v7i2.2150.

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Experiment on Interference Pattern of Laser Light Scattered Analysis using a "Dusty" Plane Mirror aims to determine the glass refractive index value. The dust samples used in this experiment are baby powder, chalk, rice flour, wood powder and beauty powder. The dust samples sprinkled over a clean mirror, so it will produce an interference pattern that can be seen on the screen. The results of the interference pattern are circular because the screen is perforated with a diameter of 0.01 m. Dust particle sample variation only affects the brightness level of interference patterns. Analysis of interference patterns using red laser pointers or green laser pointers will get the brightest interference patterns when using baby powder compared to other dust samples. Based on the resulting interference pattern, the refractive index value of the glass using a red laser pointer is 1.62 ± 0.04 and the weighted average value of the glass refractive index is 1.68 ± 0.02, whereas the refractive index value of glass using a green laser pointer is 1.74 ± 0.07 and the weighted average value of the refractive index of the glass is 1.79 ± 0.02

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45

Chase, David, Mark Wegman, and F. Ken Zadeck. "Analysis of pointers and structures." ACM SIGPLAN Notices 39, no. 4 (2004): 343–59. http://dx.doi.org/10.1145/989393.989429.

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46

Chase, David R., Mark Wegman, and F. Kenneth Zadeck. "Analysis of pointers and structures." ACM SIGPLAN Notices 25, no. 6 (1990): 296–310. http://dx.doi.org/10.1145/93548.93585.

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47

Li, Yajun. "Ruled surfaces generated by Risley prism pointers: I. Pointing accuracy evaluation and enhancement based on a structural analysis of the scan field inside the pointer." Journal of the Optical Society of America A 38, no. 12 (2021): 1884. http://dx.doi.org/10.1364/josaa.435621.

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48

Junjie Wang, Xiaohong Li, Bobo Yan, and Zhiyong Feng. "Pointer Analysis Based Vulnerability Detection for Browser Extension." International Journal of Digital Content Technology and its Applications 6, no. 1 (2012): 488–95. http://dx.doi.org/10.4156/jdcta.vol6.issue1.59.

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49

Whaley, John, and Martin Rinard. "Compositional pointer and escape analysis for Java programs." ACM SIGPLAN Notices 34, no. 10 (1999): 187–206. http://dx.doi.org/10.1145/320385.320400.

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Yong, Suan Hsi, Susan Horwitz, and Thomas Reps. "Pointer analysis for programs with structures and casting." ACM SIGPLAN Notices 34, no. 5 (1999): 91–103. http://dx.doi.org/10.1145/301631.301647.

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