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Defensive Points-To Analysis: Effective Soundness via Laziness · RelationalAI
Check out highlights of RelationalAI at Snowflake's Data Cloud Summit 2024!
Defensive Points-To Analysis: Effective Soundness via Laziness We present a defensive may-point-to analysis approach, which offers soundness
even in the presence of arbitrary opaque code.
Authors: Yannis Smaragdakis, George Kastrinis´. 2018.
In Proceedings of the 32nd European Conference on Object-Oriented Programming
(ECOOP ‘18).
We present a defensive may-point-to analysis approach, which offers soundness
even in the presence of arbitrary opaque code: all non-empty points-to sets
computed are guaranteed to be over-approximations of the sets of values arising
at run time. A key design tenet of the analysis is laziness: the analysis
computes points-to relationships only for variables or objects that are
guaranteed to never escape into opaque code.
Read the PDF:
Defensive Points-To Analysis: Effective Soundness via Laziness
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