Pointer analysis

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Short description: Determining what or where each pointer points to in program code

In computer science, pointer analysis, or points-to analysis, is a static code analysis technique that establishes which pointers, or heap references, can point to which variables, or storage locations. It is often a component of more complex analyses such as escape analysis. A closely related technique is shape analysis.

This is the most common colloquial use of the term. A secondary use has pointer analysis be the collective name for both points-to analysis, defined as above, and alias analysis. Points-to and alias analysis are closely related but not always equivalent problems.


For the following example program, a points-to analysis would compute that the points-to set of p is {x, y}.

int x;
int y;
int* p = unknown() ? &x : &y;


As a form of static analysis, fully precise pointer analysis can be shown to be undecidable.[1] Most approaches are sound, but range widely in performance and precision. Many design decisions impact both the precision and performance of an analysis; often (but not always) lower precision yields higher performance. These choices include:[2][3]

  • Field sensitivity (also known as structure sensitivity): An analysis can either treat each field of a struct or object separately, or merge them.
  • Array sensitivity: An array-sensitive pointer analysis models each index in an array separately. Other choices include modelling just the first entry separately and the rest together, or merging all array entries.
  • Context sensitivity or polyvariance: Pointer analyses may qualify points-to information with a summary of the control flow leading to each program point.
  • Flow sensitivity: An analysis can model the impact of intraprocedural control flow on points-to facts.
  • Heap modeling: Run-time allocations may be abstracted by:
    • their allocation sites (the statement or instruction that performs the allocation, e.g., a call to malloc or an object constructor),
    • a more complex model based on a shape analysis,
    • the type of the allocation, or
    • one single allocation (this is called heap-insensitivity).
  • Heap cloning: Heap- and context-sensitive analyses may further qualify each allocation site by a summary of the control flow leading to the instruction or statement performing the allocation.
  • Subset constraints or equality constraints: When propagating points-to facts, different program statements may induce different constraints on a variable's points-to sets. Equality constraints (like those used in Steensgaard's algorithm) can be tracked with a union-find data structure, leading to high performance at the expense of the precision of a subset-constraint based analysis (e.g., Andersen's algorithm).

Context-Insensitive, Flow-Insensitive Algorithms

Pointer analysis algorithms are used to convert collected raw pointer usages (assignments of one pointer to another or assigning a pointer to point to another one) to a useful graph of what each pointer can point to.[4]

Steensgaard's algorithm and Andersen's algorithm are common context-insensitive, flow-insensitive algorithms for pointer analysis. They are often used in compilers, and have implementations in SVF [5] and LLVM.

Flow-Insensitive Approaches

Many approaches to flow-insensitive pointer analysis can be understood as forms of abstract interpretation, where heap allocations are abstracted by their allocation site (i.e., a program location).[6]

A diagram showing how pointer analysis abstracts runtime memory
Flow-insensitive pointer analyses often abstract possible runtime allocations by their allocation site. At runtime, this program creates three separate heap allocations. A flow-insensitive pointer analysis would treat these as a single abstract memory location, leading to a loss of precision.

Many flow-insensitive algorithms are specified in Datalog, including those in the Soot analysis framework for Java.[7]

Context-sensitive, flow-sensitive algorithms achieve higher precision, generally at the cost of some performance, by analyzing each procedure several times, once per context.[8] Most analyses use a "context-string" approach, where contexts consist of a list of entries (common choices of context entry include call sites, allocation sites, and types).[9] To ensure termination (and more generally, scalability), such analyses generally use a k-limiting approach, where the context has a fixed maximum size, and the least recently added elements are removed as needed.[10] Three common variants of context-sensitive, flow-insensitive analysis are:[11]

  • Call-site sensitivity
  • Object sensitivity
  • Type sensitivity

Call-site sensitivity

In call-site sensitivity, the points-to set of each variable (the set of abstract heap allocations each variable could point to) is further qualified by a context consisting of a list of callsites in the program. These contexts abstract the control-flow of the program.

The following program demonstrates how call-site sensitivity can achieve higher precision than a flow-insensitive, context-insensitive analysis.

int *id(int* x) {
  return x;
int main() {
  int y;
  int z;
  int *y2 = id(&y); // call-site 1
  int *z2 = id(&z); // call-site 2
  return 0;

For this program, a context-insensitive analysis would (soundly but imprecisely) conclude that x can point to either the allocation holding y or that of z, so y2 and z2 may alias, and both could point to either allocation. A callsite-sensitive analysis would analyze id twice, once for call-site 1 and once for call-site 2, and the points-to facts for x would be qualified by the call-site, enabling the analysis to deduce that when main returns, y2 can only point to the allocation holding y and z2 can only point to the allocation holding z.

Object sensitivity

In an object sensitive analysis, the points-to set of each variable is qualified by the abstract heap allocation of the receiver object of the method call. Unlike call-site sensitivity, object-sensitivity is non-syntactic or non-local: the context entries are derived during the points-to analysis itself.[12]

Type sensitivity

Type sensitivity is a variant of object sensitivity where the allocation site of the receiver object is replaced by the class/type containing the method containing the allocation site of the receiver object.[13] This results in strictly fewer contexts than would be used in an object-sensitive analysis, which generally means better performance.


  1. Reps, Thomas (2000-01-01). "Undecidability of context-sensitive data-dependence analysis". ACM Transactions on Programming Languages and Systems 22 (1): 162–186. doi:10.1145/345099.345137. ISSN 0164-0925. 
  2. Barbara G. Ryder (2003). "Dimensions of Precision in Reference Analysis of Object-Oriented Programming Languages". pp. 126–137. doi:10.1007/3-540-36579-6_10. 
  3. (Hind {{{2}}})
  4. Zyrianov, Vlas; Newman, Christian D.; Guarnera, Drew T.; Collard, Michael L.; Maletic, Jonathan I. (2019). "srcPtr: A Framework for Implementing Static Pointer Analysis Approaches". Montreal, Canada: IEEE. https://www.zyrianov.org/papers/ICPC19.pdf. 
  5. Sui, Yulei; Xue, Jingling (2016). "SVF: interprocedural static value-flow analysis in LLVM". ACM. https://yuleisui.github.io/publications/cc16.pdf. 
  6. Smaragdakis, Yannis; Bravenboer, Martin; Lhoták, Ondrej (2011-01-26). "Pick your contexts well". Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages. POPL '11. Austin, Texas, USA: Association for Computing Machinery. pp. 17–30. doi:10.1145/1926385.1926390. ISBN 978-1-4503-0490-0. https://doi.org/10.1145/1926385.1926390. 
  7. Antoniadis, Tony; Triantafyllou, Konstantinos; Smaragdakis, Yannis (2017-06-18). "Porting doop to Soufflé". Proceedings of the 6th ACM SIGPLAN International Workshop on State of the Art in Program Analysis. SOAP 2017. Barcelona, Spain: Association for Computing Machinery. pp. 25–30. doi:10.1145/3088515.3088522. ISBN 978-1-4503-5072-3. https://doi.org/10.1145/3088515.3088522. 
  8. (Smaragdakis Balatsouras)
  9. Thiessen, Rei; Lhoták, Ondřej (2017-06-14). "Context transformations for pointer analysis". ACM SIGPLAN Notices 52 (6): 263–277. doi:10.1145/3140587.3062359. ISSN 0362-1340. https://doi.org/10.1145/3140587.3062359. 
  10. (Li Tan)
  11. (Smaragdakis Balatsouras)
  12. (Smaragdakis Balatsouras)
  13. (Smaragdakis Balatsouras)