Pick Your Contexts Well: Understanding Object-Sensitivity
Object-sensitivity has emerged as an excellent context abstraction for points-to analysis in object-oriented languages. Despite its practical success, however, object-sensitivity is poorly understood.
Yannis Smaragdakis, Martin Bravenboer, Ondrej Lhotak. 2011.
In Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages (POPL ‘11).
Object-sensitivity has emerged as an excellent context abstraction for points-to analysis in object-oriented languages. Despite its practical success, however, object-sensitivity is poorly understood. For instance, for a context depth of 2 or higher, past scalable implementations deviate significantly from the original definition of an object-sensitive analysis. The reason is that the analysis has many degrees of freedom, relating to which context elements are picked at every method call and object creation. We offer a clean model for the analysis design space, and discuss a formal and informal understanding of object-sensitivity and of how to create good object-sensitive analyses. The results are surprising in their extent.
Read the PDF: Pick Your Contexts Well: Understanding Object-Sensitivity (opens in a new tab)
Related Posts
Five AI Trends from the latest NeurIPS 2023 Conference
Five AI Trends from the Neural Information Processing Systems (NeurIPS) conference which focuses on Large Language Models (LLMS).
Credit Card Fraud Identification Using Machine Learning on Graphs
The most common type of fraud according to a 2022 report by FTC, and we demonstrate the advantages of leveraging graph analytics to automatically identify fraudulent transactions from a set of financial transactions, using machine learning (ML)